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dportsv3-agentic-analysis

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更新时间2026年6月16日 22:58

Analyze how the DeltaPorts agentic loop (triage → patch; convert retired in Step 48 — archived bundles may still show it) handled a given port. Use when the user says "analyze port X" or "analyze the agentic run for X". Produces a structured report covering correctness, efficiency, and bugs against the current code's expected behavior. This skill is constantly improving — when you spot a new failure mode or expected-behavior gap, note it at the end of the report under "Skill update suggestions" so it can be folded back in.

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DragonFlyBSD

DragonFlyBSD/DeltaPorts

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DeltaPorts agentic analysis

What this skill is for

The DeltaPorts agentic loop watches dsynth failures and tries to fix them. Each failure produces a bundle with triage output, patch attempts, tool trace, and a rebuild proof. This skill walks you through pulling that data for a given port and judging what the loop did well, did badly, or got silently wrong.

Inputs you need before starting:

Port origin (e.g. devel/gperf) or a bundle ID.
Tracker base URL — read from the DP_TRACKER_URL env var, or ask the user. There is no hardcoded default; the tracker host is operator-specific.

Output: a report with what the agent did, whether that matches the expected contract, inefficiencies, bugs, and skill update suggestions.

If the analysis will read large traces (>50 KB of artifacts), delegate the read+summarize step to a sonnet 4.6 subagent via the Agent tool so the main context stays clean. Give the subagent the artifact URLs and a copy of the "Expected behavior" + "Known failure modes" sections from this file.

How to fetch data

Prefer the dportsv3 tracker get-* CLI over curl. It talks to the same tracker HTTP API but returns clean structured output and handles URL encoding for you. Set DPORTSV3_TRACKER_URL once and every command picks it up; or pass --server URL.

Locating the `dportsv3` binary

The CLI is not always on $PATH. At the start of your analysis, resolve the binary once and reuse it:

# Repo-root-anchored fallback. The venv binary is the project's
# own and is the safe deterministic location.
DPORTSV3="${DPORTSV3:-$(command -v dportsv3 || echo /Users/tuxillo/s/DeltaPorts/scripts/generator/.venv/bin/dportsv3)}"
# Verify before using it:
"$DPORTSV3" tracker --help >/dev/null \
    || { echo "dportsv3 unusable at $DPORTSV3" >&2; exit 1; }

Then every later call uses "$DPORTSV3" tracker get-bundle … etc. If you can't make the CLI work after that probe, fall back to curl and flag the gap.

CLI commands (prefer these)

Purpose	Command
List recent bundles for a port	`dportsv3 tracker list-bundles --origin <category/port> --limit 10`
Get one bundle's detail (incl. artifact list)	`dportsv3 tracker get-bundle <bundle-id>`
Same, structured JSON	`dportsv3 tracker get-bundle <bundle-id> --json`
Fetch one artifact's raw bytes (logs/diffs/JSON)	`dportsv3 tracker fetch-artifact <bundle-id> <relpath>`
Get one job by ID	`dportsv3 tracker get-job <job-id>`
List jobs (filter by state)	`dportsv3 tracker list-jobs --state dead --limit 20`
Activity log for one job	`dportsv3 tracker get-activity --job <job-id> --limit 200`
Activity log filtered by stage	`dportsv3 tracker get-activity --job <id> --stage tool:`

HTML pages (use only when no equivalent API exists)

Purpose	URL
Agentic dashboard (rendered overview)	`GET /agentic`
Bundle detail rendering	`GET /agentic/bundles/<bundle-id>`

Discovery flow

dportsv3 tracker list-bundles --origin devel/<port> --limit 5 — find recent bundle IDs.
dportsv3 tracker get-bundle <id> --json — full detail with the artifact list.
For each artifact you actually need to read: dportsv3 tracker fetch-artifact <id> <relpath>.
If multiple bundles exist for the same port, analyze the most recent and scan timestamps of prior failures to see if the loop kept retrying.

Why not curl?

The CLI handles URL encoding of slashed paths (devel/gperf → devel%2Fgperf) without you remembering.
Structured output (or --json for full JSON) means no grep/sed/jq pipelines that break on whitespace or special characters in error messages.
The single binary already knows the tracker URL (DPORTSV3_TRACKER_URL) so you don't repeat it.

Fall back to curl only if the CLI is missing a command you need; then file a SKILL-update suggestion so the next analyzer doesn't have to fall back.

Artifacts that matter

All under /api/bundles/<bundle-id>/artifacts/:

Path	What it tells you
`meta.txt`	Origin, target, timestamps, dsynth profile.
`logs/errors.txt`	dsynth tail — the actual failure the agent reacted to.
`analysis/triage.md` + `triage.json`	Classification, confidence, root cause, suggested fix.
`analysis/patch.md`	Agent's narrative of what it did and why.
`analysis/patch_audit.json`	Status, model, token usage, attempts breakdown.
`analysis/rebuild_proof.json`	`rebuild_ok` + build command — the success gate.
`analysis/changes.diff`	The operator-applyable diff. Empty diff with `rebuild_ok=true` is always a bug. Since Step 42 there is no intent-log fallback — this is the canonical record.
`analysis/tool_trace.jsonl`	Per-turn tool calls + their results, lifecycle events (`attempt_start`, `llm_turn`, `tool_call`, `attempt_end`, `token_budget_exhausted`). Cheap inefficiency scan. Does NOT contain assistant message text or `reasoning_content` — for that, read the session dumps below.
`analysis/sessions/*.jsonl.gz`	Full LLM message transcripts (one file per attempt per job). The single most informative artifact: every assistant turn's `content` + `reasoning_content`, every `tool_calls` entry with full arguments, every tool result message the model actually received. Read these when tool_trace.jsonl isn't enough — i.e. whenever you need to understand why the agent did what it did, not just what tools fired. Available only when `DP_HARNESS_DUMP_SESSION` was on at run time. See below for record structure + parsing.
`analysis/proposed_fix.md`	Operator-facing recipe written when `rebuild_ok=true`.
`analysis/manual_handoff.md`	Operator-facing escalation summary written when the job escalates (MANUAL tier, retry cap, budget exhaustion, gave-up). Either this or `proposed_fix.md` is present, not both.
`analysis/convert_result.json`	Convert phase typed result (when a convert job ran). Carries `status`, `reapply_ok`, `deferred_patches`.
`analysis/triage_result.json` / `patch_result.json`	Typed phase results (Step 36) — root cause + evidence lifted into structured fields, consumed by downstream phases.
`port/Makefile`, `port/distinfo`, `port/pkg-plist`	Snapshot of the port at failure time.

LLM session dumps — structure and parsing

Each analysis/sessions/<timestamp>-<target>-<origin>-<pid>[-<role>].job.attempt<N>.jsonl.gz is the complete messages array as it stood at the end of one tool_loop attempt. The filename embeds:

the role suffix: -convert, -patch, or no suffix for triage
attempt number: attempt1, attempt2 (patch can retry, convert/triage are single-attempt)

Decompressed: one JSON object per line, each a message in the LLM-API shape:

{"role": "system", "content": "<system prompt verbatim>"}
{"role": "user", "content": "<initial task payload, plus any failure-context message inserted at attempt N>1>"}
{"role": "assistant", "content": "<text>", "reasoning_content": "<deepseek thinking>", "tool_calls": [...]}
{"role": "tool", "content": "<JSON tool result>", "tool_call_id": "..."}

What each role tells you:

system (record 0): the exact prompt the agent saw. Verify any prompt-rule reference (e.g. "the agent went to /work/DPorts despite the prompt saying not to") by grepping the system content.
user (record 1): the assembled payload. Always check len(content) — anything >50KB warrants a section-by-section breakdown by ## heading. Common offenders: ## Build Errors (dsynth log tail), ## Port Files (every file under port/ inlined), ## Agent Playbooks (matched playbook content).
user (record 2+, attempt 2+ only): the failure-context message from attempt_loop._failure_context_message. After Step 42 this is just Previous attempt #N did not succeed.\nTail of your prior response:\n<last 2KB>\n — no structured intent-log summary. If the agent re-submits the exact same wrong edit in attempt 2, the lesson is in those last 2KB of attempt-1 prose.
assistant: the agent's per-turn output. Three fields you care about:
- content (text the model wrote) — usually small except in the final "## Patch Log / ## Rebuild Proof" turn.
- reasoning_content (deepseek thinking-mode output) — often the largest single field. Carried on every subsequent turn per deepseek's contract, so accumulates quadratically. A 13KB single-turn reasoning blob is ~3K tokens × every remaining turn.
- tool_calls — array of {id, function: {name, arguments}}. The arguments are JSON-stringified; parse before inspecting.
tool: the JSON result the model received. Contents match what worker.py returned: {ok, ...}. For materialize_dports: stdout_tail is what the agent reads to decide the apply landed. For validate_dops: {ok, stderr_tail} with E_* error codes on failure. For get_effective_overlay: {target, effective_ops, filtered_out} — what compose WILL apply vs. ops scoped to other build lines. For put_file: {ok, sha256, ...}.

Fetch + decompress in the same shell pipeline as the rest:

BID=<bundle-id>
"$DPORTSV3" tracker get-bundle "$BID" --jobs --json | python3 -c "
import json,sys
d=json.load(sys.stdin)
for a in d.get('artifacts',[]):
    if 'sessions/' in a['relpath']:
        print(a['relpath'])
" | while read rel; do
    out="$(basename "$rel")"
    "$DPORTSV3" tracker fetch-artifact "$BID" "$rel" > "$out" 2>/dev/null
    gunzip -k "$out"   # keep both .gz and decompressed
done

Walking a session (Python, one-shot):

import json
recs = [json.loads(l) for l in open('<file>.jsonl') if l.strip()]
print(f"records={len(recs)} system={len(recs[0]['content'])}B user={len(recs[1]['content'])}B")
for i, r in enumerate(recs):
    if r['role'] in ('system','user'): continue
    if r['role'] == 'assistant':
        tcs = [t['function']['name'] for t in (r.get('tool_calls') or [])]
        rc = r.get('reasoning_content') or ''
        print(f"#{i} ASST text={len(r.get('content') or '')}B reason={len(rc)}B tools={tcs}")
        # show big reasoning blobs
        if len(rc) > 1000:
            print(f"  R: {rc[:400].replace(chr(10),' ')!r}...")
    elif r['role'] == 'tool':
        try:
            d = json.loads(r['content']); ok = d.get('ok')
        except Exception:
            ok = None
        print(f"#{i} TOOL ok={ok} sz={len(r['content'])}B")

When tool_trace.jsonl is enough vs. when you need session dumps:

tool_trace alone suffices for: "which tools fired in what order", "did the agent call dsynth_log after build failure", "how many put_file writes hit overlay.dops", basic efficiency scan.
Need session dumps for: "why did the agent go down path X" (reasoning_content), "what did the agent actually observe in the materialize summary" (full tool result content vs. trace's truncated keys), "did the agent see the failure-context message between attempts" (user-record-2 in attempt 2), "what was the static prompt's exact text" (system record). Anything that requires understanding the agent's mental model needs the session dump.

A session dump may not exist for older bundles or bundles run without DP_HARNESS_DUMP_SESSION=1. The text format of analysis/tool_trace.jsonl is always present and is the fallback.

Bulk-fetch the bundle in ONE shell command, not N small calls.

Every Bash invocation costs a permission prompt and a tool turn. The whole analysis should fit in 2–4 Bash calls total:

List + pick (1 call): "$DPORTSV3" tracker list-bundles --origin <port> --limit 5

Full bundle dump in one shell pipeline (1 call):

BID=<bundle-id>
echo "===== bundle detail + jobs ====="
"$DPORTSV3" tracker get-bundle "$BID" --jobs --json
for f in meta.txt logs/errors.txt analysis/triage.md analysis/patch.md \
         analysis/patch_audit.json analysis/rebuild_proof.json \
         analysis/changes.diff analysis/tool_trace.jsonl \
         analysis/proposed_fix.md analysis/manual_handoff.md \
         analysis/convert_result.json; do
  echo "===== $f ====="
  "$DPORTSV3" tracker fetch-artifact "$BID" "$f" 2>/dev/null \
    || echo "(absent)"
done

Activity log if needed (1 call): "$DPORTSV3" tracker get-activity --job <job-id> --limit 200

That's it. Three Bash calls covers ~95% of analyses. Do NOT make one call per artifact, one call per job ID, one call per stage filter — the shell loop above is a single Bash invocation from the permission system's perspective. Reach for more calls only when the first dump leaves a specific gap (e.g. an unexpected stage you want to grep for).

Note: proposed_fix.md and manual_handoff.md are mutually exclusive (one per terminal outcome); convert_result.json is absent on patch-only bundles. The || echo "(absent)" handles all three.

Expected behavior (current code contract)

This section is what the agent should be doing if the code is working. Update it when the code evolves.

Lifecycle

A failure bundle is written by the dsynth hook. The runner enqueues a triage job.
Triage job runs dportsv3.agent.triage.run — single LLM call (no tools), may do up to DP_HARNESS_MAX_SNIPPET_ROUNDS (default 5) snippet-extractor rounds.
Triage emits classification, confidence, root-cause text. config/agentic-policy.json resolves a tier (AUTO / ASSIST / MANUAL).
Routing is driven by assess_dops (the substrate gate), not by triage classification. At a failure with no overlay.dops, the runner (_ensure_overlay_or_abort → overlay_state.bootstrap_decision) deterministically writes a header overlay (type port/dport per STATUS) so patch can author the body, or aborts to MANUAL when non-dport compat artifacts are present. (Step 48 deleted the runtime convert agent — there is no longer a convert job; older bundles may still show one.)
AUTO/ASSIST → auto-enqueue patch (after the deterministic bootstrap, if substrate wasn't already dops). MANUAL → stop.
Patch job runs dportsv3.agent.patch.run → attempt_loop.run up to tier.max_iterations attempts, each driving a tool_loop until the LLM stops requesting tools.
Success gate: rebuild_proof.json parsed from the LLM's ## Rebuild Proof (JSON) block with rebuild_ok=true.

Tool surface

There is one patch-agent tool surface (Step 42 deleted the edit-intent layer). The patch agent edits ports/<origin>/overlay.dops directly in dops DSL (put_file + validate_dops + dops_reference, reading with grep / get_file), plus the build-loop tools (extract, dupe, genpatch, install_patches, dsynth_build, dsynth_log, materialize_dports) and the read-only views emit_diff and get_effective_overlay. System prompt: prompts.PATCH_SYSTEM. Tool list: tools.patch_tool_names() (returns the full registry).

Triage agent has no tools — single-turn LLM call with snippet rounds. System prompt: prompts.TRIAGE_SYSTEM.

(Historical bundles only: a separate convert agent existed pre-Step-48 with its own tool surface + prompts.CONVERT_SYSTEM; both are deleted. When analyzing an archived convert bundle, judge it against that retired contract, not current code.)

Check dportsv3/agent/tools.py and dportsv3/agent/prompts.py directly if uncertain.

Substrate contract

All chroot-bound operations route through dportsv3 dev-env exec <env> -- ....
Host filesystem ops only on env_dir/writable/... (resolved via dportsv3 dev-env path <env> --writable).
No git commits, branches, push, or PRs from the loop. The writable overlay is the workspace; analysis/changes.diff is the audit trail. Delivery to upstream (PR open / patch outbox) is a separate operator-triggered phase via dportsv3.delivery.orchestrator.
Guards collapse to three generic gates: validate_dops / check_dsl (DSL syntax + semantics), assess_dops (substrate-state gate that decides whether patch can proceed), and the _resolve_path path-escape backstop. assert_port_clean (the pre-job clean preflight) runs unconditionally.

Substrate / mode handling

Patch operates ONLY on dops substrate (Step C, commit 0b7ed09fc26 onward, reinforced by Step 42). For a port with no overlay.dops, the runner deterministically bootstraps a header overlay (clean / newport dport) so patch can author the body, or aborts to MANUAL when non-dport compat artifacts (Makefile.DragonFly/diffs//dragonfly/) are present — full absorption of those is the offline tooling's job, not the loop's. (Pre-Step-48 this was a runtime convert job; it's gone.)

The put_file boundary refuses any write to a Makefile.DragonFly (unconditional since the Step 48 authoring lock). A port that still has compat artifacts and no overlay is aborted to MANUAL, not "routed to convert."

dops grammar (what `overlay.dops` looks like)

The dops DSL is the source of truth for substrate. The canonical reference is scripts/generator/dportsv3/agent/dops_quickref.md — read it before judging dops edits. Real examples live at ports/devel/readline/overlay.dops, ports/editors/vim/overlay.dops, ports/ports-mgmt/pkg/overlay.dops.

Quick orientation (full grammar in the quickref):

file.materialize { source = "src/path"; dest = "dest/path" } — copy a file from the dragonfly source tree into the port at compose time. Used for porting in DragonFly-specific replacements of upstream files.
file.copy { source = "..."; dest = "..." } — copy a file from port-local resources (no dragonfly source tree).
patch.apply { target = "path/in/wrksrc"; diff = """...""" } — apply an inline unified diff at patch phase. The diff payload is the actual patch content — when context drifts, the diff string inside overlay.dops is what needs editing, not a dragonfly/patch-* file.
mk set / mk add / mk remove / mk replace-if — Makefile.DragonFly variable directives. Sequential mk set on the same key REPLACES (the engine collapses duplicates); the agent's mental model should not produce duplicate mk set lines.
mk target set/append — recipe lines for a make target.
Per-target scoping via target @main / target @2026Q[1-4] / target @any. @any is the right default for fresh overlays; @main filters every op away on quarterly builds (silent-skip bug class — see "Target-mismatch ghost" below).

Bright-line rule: if the upstream-source file lives in the dragonfly source tree, prefer file.materialize; if it's port-local, use file.copy. The convert agent's system prompt teaches this — when judging a convert run, check it followed the rule.

Mode-correctness checks for the analyzer

For any patch or convert bundle, verify in the trace:

Did the agent read overlay.dops early? (It should — that's the source of truth.) Prefer get_effective_overlay reads over raw get_file overlay.dops reads — on multi-target overlays the effective view filters out ops scoped to other build lines, eliminating manual filtering errors.
If a patch hunk drifted: did the agent edit the diff = """...""" block inside overlay.dops (correct), or did it put_file to dragonfly/patch-* (wrong — silently clobbered on next reapply, the put_file boundary should refuse this on dops ports but verify it did)?
If a patch became obsolete: did the agent remove the corresponding patch.apply / file.materialize block from overlay.dops, or did it put_file an empty dragonfly/patch-*? Only the first is durable.
Did validate_dops run after every put_file overlay.dops? It should — that's the dops equivalent of "does this even parse." Skipping it and going straight to materialize_dports is a discipline regression.
Did materialize_dports re-run between the dops edit and dsynth_build? Compose needs to re-render the port tree from the edited overlay before dsynth sees the change.
For a fresh overlay: does the header carry target @any (correct) or target @main (silent-skip regression — see failure modes)?

Output contract for operators

changes.diff must contain the operator-applyable diff. Empty diff with rebuild_ok=true is a contract violation. Since Step 42 there's no intent-log fallback — this single artifact is the canonical record.
On success, proposed_fix.md is written by dportsv3.agent.proposed_fix and must reference a non-zero diff.
On escalation (MANUAL tier, retry cap, budget exhaustion, gave-up), manual_handoff.md is written by dportsv3.agent.manual_handoff instead.

Playbook library (Step 27 + Step 42 reframe)

The legacy KEDB (docs/known-errors/) and prompt-embedded pattern content were replaced by a single playbook library at docs/agent-playbooks/ (markdown entries with YAML-subset frontmatter triggers). The selector is dportsv3/agent/playbooks.py::load_playbooks. After Step 42 there are three live filename prefixes:

error-*.md — triggered by triage classification (triggers.classifications:); attached to triage + patch payloads.
convert-*.md — attached to convert payloads when triggers.flows contains convert.
toolchain-*.md — triggered by mechanical toolchain detection on the port Makefile (playbooks.py::detect_toolchains — parses USES=, GNU_CONFIGURE=, file-presence signals like CMakeLists.txt, Cargo.toml).
flow-patch.md — the single consolidated patch playbook (Step 42 collapsed the 12 deleted intent-*.md entries into one). Triggered by flows: [patch]. Carries the durable knowledge previously split per-intent: mk-directive traps, scoping judgment (@any vs @main), the static-patch workflow, broken-patch recovery, PORTREVISION handling.

The intents trigger axis and the per-call intent_reference on-demand attachment path are both gone. Every playbook that fires is attached at payload-build time.

Telemetry signal — playbooks_selected activity row. Every payload build emits one. The row carries role (triage / patch / convert), included (list of entry filenames that fired), skipped_count, and a skipped_sample of up to 8 {file, reason} pairs. Fetch via dportsv3 tracker get-activity --job <id> and filter for event=playbooks_selected. Reasons take shapes like flow:patch-not-in-['convert'], classification:'patch-error'-not-in-['compile-error'], toolchains:no-overlap-with-['autoconf'], or budget:N+M>BUDGET.

What to check in an analysis:

For a triage/patch bundle: did playbooks_selected fire, and does included include the entries you'd expect given the classification and the port's Makefile? E.g. a patch-error triage on a USES=cmake port should pull error-* entries gated on patch-error plus toolchain-cmake.md. Empty included on a port with a recognized toolchain is a red flag — likely a missing trigger or a find_playbooks_dir() failure.
Every patch bundle should include flow-patch.md. Its absence on a patch role row points at the flows: [patch] trigger or the selector wiring.
Convert bundles: did playbooks_selected with role=convert fire and include convert-target-directive.md and convert-classify-patch-domain.md?
Does the skipped_sample reveal a likely-buggy entry (e.g. a toolchain-cmake.md skipped with toolchains:no-overlap-with-['cmake'] when the port's Makefile clearly has USES=cmake)? That points at detect_toolchains() not seeing the Makefile.

Known parser quirks (smoke-testing surface, mention if you spot symptoms):

_parse_inline_list only parses inline-form YAML ([a, b]). Block-form list (- a\n - b) silently yields (), which means wildcard in the selector. A new entry that "fires on everything" probably has block-form triggers.

Analysis checklist

For each bundle, work through these questions and write the report against them. Skip ones that don't apply, but say so.

1. Pipeline shape

Before drilling into per-job correctness, sketch the pipeline. The canonical flows are:

Patch only (substrate already dops): triage → patch. One triage session.
Convert-then-patch: triage → convert → triage (re-classifies against the converted substrate) → patch. Two triage sessions is expected here, not a bug.
Convert only (substrate-only fix): triage → convert. No patch session.
Convert failed: triage → convert (gave up / budget). No patch session.

List every job that ran with dportsv3 tracker get-bundle <id> --jobs --json. The jobs array carries each job's type, state, retire_reason. Two triages with identical classifications is normal (convert didn't change the failure shape); two triages with different classifications is a substrate change worth narrating.

2. Triage correctness

Does the classification match what logs/errors.txt actually shows? (e.g. a fetch failure misclassified as compile-error is a triage bug.)
Is the confidence appropriate?
Did snippet rounds happen, and were they useful? (Look for snippets/round_N/ artifacts.)
If two triage rounds ran (pre-convert + post-convert), did classifications change between them? If yes, note why — typically convert promoted compat→dops which changes the substrate's failure expression.
Record each triage's token usage; the second triage often duplicates the first when convert didn't change failure shape (cheap re-run, expected).

3. Convert correctness (if a convert job ran)

Status from analysis/convert_result.json: verified / failed / no_conversion_proof_block. reapply_ok=true means convert produced a valid overlay that compose accepts.
Verify target @any in the produced overlay. Read analysis/changes.diff or grep the env's overlay.dops for the header. Anything other than target @any is a regression of the post-2026-05-26 fix (commits d71f605c206 + 47846e7a392). @main in a fresh overlay means every op will silently skip at compose against @2026Q2 (per engine/apply.py:296-313).
Deferred patches: list deferred_patches from convert_result. Each entry says what the dropped framework patch was DOING (intent, not authority). The downstream patch agent should address each one — verify it does.
Tokens, attempts. Convert's budget is tighter than patch's; a convert that hit budget-exhausted after one validate_dops parse error is a known weak spot.

4. Patch correctness

Did the agent reach rebuild_ok=true?
Does the fix actually fix the root cause, or did it bypass the problem? (E.g. removing a patch the agent declared obsolete vs. actually adapting it — both may produce rebuild_ok=true, but only one is right. Cross-check patch.md's reasoning against the upstream code it read.)
Did the agent edit overlay.dops (correct) or dragonfly/* files directly (wrong, the put_file boundary should refuse — verify it did)?
For each put_file overlay.dops, was validate_dops called before the next materialize_dports?
Did the agent escalate cleanly when blocked by assess_dops substrate gates (e.g. half-migration), or did it keep retrying?
Turn-to-first-meaningful-edit. Count tool turns from session start to the first put_file that targets overlay.dops (or dragonfly/* on the rare valid case). Floor on a clean success appears to be ~10-15 turns (opening + investigation + dops_reference if writing fresh). Values >20 suggest over-exploration; 0 means the agent never committed to a hypothesis (paralysis — flag).
Self-correction. Count edits that were later reversed within the same attempt (e.g. an overlay.dops write replaced by a corrected version after a failed materialize_dports). One self-correction per run is healthy (the agent learned from compose/build feedback). Many self-corrections suggest the agent is thrashing; zero self-corrections paired with budget-exhausted may mean the agent never tried anything concrete enough to fail informatively.

5. Path discipline (scan tool calls)

Reads of /work/DPorts/<origin>/...? Per prompts.PATCH_SYSTEM the agent may NOT read from this path — it's the LOCK ROOT, last-known-good, will disagree with extract output. Note every occurrence.
Reads of /xports/... or any other chroot-internal path that isn't under /work/? Tools fail with ValueError: path must be under /work (worker.py _resolve_path). One occurrence = honest mistake; multiple = the agent didn't read the build log's path notation correctly.
Hand-constructed /work/obj/<origin>/... paths that didn't come from extract's wrksrc field? The prompt explicitly forbids constructing these. Compare against extract's wrksrc return value.
Host-side path leaks: tools returning host paths (e.g. /root/.cache/dports-dev/...) that the agent then passes to chroot-path-expecting tools. genpatch's output_dir return was a known case.

6. Materialize cycle signal (P0a/P0b regression check)

For every materialize_dports call (each attempt typically has 1-3), check the summary: line in stdout_tail:

applied=N>0 — ops actually applied to the compose tree. Healthy.
applied=0 with skipped>0 — ops were filtered by target mismatch. Expect the I_COMPOSE_DOPS_ALL_OPS_SKIPPED warning in the same stage line (per commit 663a8eae819). If the warning is present, surface it; the agent should see it too. If applied=0 skipped>0 but the warning is absent, that's a P0b regression.
applied=0 with errors>0 — at least one op failed (parser, executor error). Distinct from skipping; check dops_failed_op_results in compose report.
applied=N>0 and skipped=0 on a freshly-created overlay confirms P0a is functioning (the convert / patch agents emit target @any headers per commits d71f605c206 / 47846e7a392).

If you see target @main in any agent-emitted fresh overlay, that's a P0a regression — flag immediately.

7. Build verification (after dsynth_build)

After dsynth_build returns rebuild_ok=true, did the agent verify by grepping the extracted source for the original error symptom, or rely on the tool exit code alone? For deterministic failures (linker duplicate symbol, missing-include compile error), trusting exit code is fine. For symptom classes where multiple bug sites can produce the same error (e.g. __result undefined in N headers), grep-the-symptom catches incomplete fixes.
After dsynth_build failed, did dsynth_log immediately follow? The prompt says to call it immediately on build failure. If the agent went back to exploring instead, that's a discipline regression.
Did the agent run dsynth_build at least once? budget-exhausted with zero build calls is the worst possible signal — the agent never tested anything, never learned from substrate feedback, just analyzed.

8. Output contract

Is analysis/changes.diff non-empty when rebuild_ok=true?
On success: is proposed_fix.md present and does it reference the diff?
On escalation: is manual_handoff.md present? rebuild_proof.json should also exist with rebuild_ok=false and a reason — its absence on a terminal-failure bundle is a regression (see failure modes).
Does the diff actually match what patch.md says was changed?

9. Efficiency — quantitative breakdown

Don't say "the loop was expensive". Break it down:

Static prompt cost. Sizes of messages[0] (system) + messages[1] (initial user). Multiply by turn count for the per-turn ceiling. On a clean ASSIST run this is typically 35-50% of total tokens; on bloated cases (>50KB user prompt) it dominates.
User prompt composition. When the user prompt is >50KB, break it by ## section heading and flag oversized sections (>10KB) the agent never read via subsequent tool calls. Common offender: ## Port Files which inlines every file under port/; the agent has get_file and can pull on demand.
Reasoning_content accumulation. Sum reasoning_content byte sizes across all assistant turns. Note single-turn outliers (>5KB is a "thinking hard" turn — fine on hard ports, suspicious on simple ones). Deepseek thinking-mode requires reasoning_content carry on every subsequent turn, so this accumulates quadratically.
Tool result carry. Identify the top-3 biggest tool returns by byte size. Each dsynth_log is ~10-16KB. A 16KB result carried across 10 subsequent turns is ~40K tokens. Also watch get_effective_overlay on large multi-target overlays.
Completion. Usually small unless the agent wrote a long Patch Log.
Sum the per-turn prompt sizes (from the llm_turn activity events if present, or estimate as static + cumulative reasoning + cumulative tool results). Compare to patch_result.tokens_total — a big discrepancy may indicate the trace is missing entries.

Other efficiency checks:

Redundant tool calls — e.g. multiple emit_diff calls in a row, or materialize_dports called twice when once would do.
Did the agent re-read files it had already read?
Did it call tools with the wrong args (e.g. passing origin where relpath was expected)?
Did the agent use raw get_file overlay.dops on a multi-target port instead of get_effective_overlay? On wide overlays this can double the per-turn carry.

10. Playbook coverage (Step 27)

Did playbooks_selected fire for each role this bundle ran? (triage always; patch if it reached patch; convert if it's a convert bundle.)
Does included look right for the bundle's classification + detected toolchains? Empty included on a port with recognized USES= is a red flag.
For every patch bundle: is flow-patch.md in included? Its absence is a Step-27/42 wiring regression.
Does the skipped_sample reveal a likely-buggy entry (e.g. a toolchain-cmake.md skipped with toolchains:no-overlap-with-['cmake'] when the port's Makefile clearly has USES=cmake)? That points at detect_toolchains() not seeing the Makefile.
Caveat: the text-format playbooks_selected activity row only shows counts (included=N skipped=M), not filenames — which means the "does included look right" check above is unverifiable from the default get-activity output. To actually verify which entries fired, pull raw JSON: dportsv3 tracker get-activity --job <id> --json and inspect the row's payload field for the filename list. If --json doesn't expose it either, fall back to curl http://<tracker>/api/jobs/<id>/activity and grep for playbooks_selected. If no surface exposes the filenames, flag as a tracker feature gap and treat the count as a black box.

11. Lifecycle hygiene

Always run list-bundles --origin <category/port> --limit 10 for the port even if the current bundle looks like a clean one-shot. It's one cheap CLI call and gives ground truth about prior agentic activity on this port. The port/STATUS file (when present in the bundle snapshot) is a compat-era artifact that convert deletes — it can hint at upstream version history but is not a substitute for the tracker query and disappears entirely on already-dops ports.
Was this port previously bundled? If so, did the loop converge (older bundles accepted / verified) or thrash (multiple dead / budget-exhausted over time)?
If MANUAL tier: was the classification one that should have been AUTO/ASSIST?
assert_port_clean is now unconditional. If the patch job aborted with a clean-check failure, that's an env-state problem (uncommitted edits from a prior run, chroot in unknown state), not a patch agent bug.

Known failure modes (extend this list)

Patterns seen in the wild. When you see a new one, append it here and flag it in your report's "Skill update suggestions" section so the operator folds it in.

Empty changes.diff with rebuild_ok=true. Agent edited files inside the writable overlay but the runner's diff capture didn't pick it up. Suspected causes: overlay isn't a git working tree, or the diff scope path is wrong. Operator gets "agent fixed" with nothing to land. Always flag this as a bug. Confirmed historically for dops-mode put_file writes to overlay.dops (devel_gperf-20260523-094119Z) and for freshly-created overlay.dops files (multimedia_v4l_compat-20260523-101601Z) — the bug fires on any put_file write the overlay's baseline misses. Verify on every current bundle that the diff is non-empty whenever a put_file against overlay.dops succeeded.
Agent passes origin where emit_diff wants a relpath. Tool signature is emit_diff(env, origin, relpath); the LLM sometimes passes only origin (e.g. "devel/gperf"). Either the prompt is unclear or the schema is.
Agent proceeds against a substrate assess_dops flagged as not-yet-converted. The runner should refuse state:<state> and escalate; if the trace shows the patch agent continuing past such a block it's a runner/policy bug.
Agent declares a patch "obsolete" based on shallow upstream inspection. Removing a patch and getting a green dsynth is not proof the patch was actually obsolete — it may have addressed a runtime or platform-specific issue dsynth doesn't catch. Flag when the agent's justification is thin.
Wasted get_file turn from a mis-guessed offset. When inspecting a C source file for include directives, the agent sometimes first reads from a non-zero offset and then re-reads from the top, burning a turn. Steer the agent toward grep for include-presence checks instead of speculative offset reads.
rebuild_proof.json missing on budget-exhaustion / give-up bundles. Expected per the success-gate contract: even on terminal failure the runner should emit a proof JSON with rebuild_ok=false and a reason. Operator skimming the artifact list can't distinguish a clean "agent gave up" from "agent crashed mid-attempt." Flag whenever patch_audit.json reports a terminal status but rebuild_proof.json is absent.
Knowledge gap: .for-parsed Makefile list variables and value-with-spaces. Variables like BINARY_ALIAS, MAKE_ENV, PLIST_SUB are iterated by .for var1 var2 in ${VAR} which tokenizes on whitespace and expects N words per row. A value with an embedded space (e.g. BINARY_ALIAS=gmd5sum=md5 -r) produces the compose error Wrong number of words (N) in .for substitution list with M variables. The agent typically misdiagnoses by toggling flags rather than escaping the value or switching to a wrapper script. Worth a dedicated error-for-substitution-list.md playbook.
Attempt-boundary amnesia. When attempt 1 fails on a specific value and attempt 2 receives a fresh context, the agent sometimes re-emits the exact same edit that already failed in attempt 1. After Step 42 the failure-context message is just the last 2KB of attempt-1 prose, so if attempt 1's prose didn't surface the specific edit that broke, attempt 2 has no signal. Flag whenever attempt N writes the same overlay.dops content as attempt N-1.
Premature materialize_dports on the consumer origin before the provider overlay is activated. When a port uses MASTERDIR (or otherwise shares compose artifacts with a sibling origin), the agent sometimes materializes the consumer origin immediately after writing the dops overlay for the provider, before materializing the provider itself. Compose runs against the wrong origin, shows modes: compat=1, and the wasted call is only caught because the agent then self-corrects with a second call to the right origin. Seen in multimedia_v4l_compat-20260523-101601Z. Prompt should steer the agent to always materialize_dports the origin that owns overlay.dops first.
Target-mismatch ghost (target @main in fresh overlay → all ops silently skipped). When an agent emits a fresh overlay.dops, the header MUST be target @any. Compose runs against @2026Q2 (or whatever the env's build target is); per engine/apply.py:296-313 every op with target=@main is filtered with status="skipped" and an info-level I_APPLY_TARGET_MISMATCH diagnostic that didn't bubble to stage output. The summary applied=0 reads as "patch didn't take" and the agent typically diagnoses it as a compose bug, burning hundreds of K tokens chasing the ghost. Mitigated by commit 663a8eae819 (compose stage warning I_COMPOSE_DOPS_ALL_OPS_SKIPPED). When analyzing a bundle: verify the fresh overlay has target @any and that the warning fires on any dead overlay. Confirmed historically in skalibs / libfyaml / gnome_subr 20260601 bundles.
Analysis paralysis — 0 meaningful edits, 0 dsynth_build calls, full ASSIST budget consumed. Agent investigates indefinitely without committing a hypothesis. Often correlated with: (a) a complex ## Deferred from Convert section that invites verdict-first investigation, (b) a port class where the agent can't easily map the bug to a dops edit shape, (c) the prompt's "4+ tool calls without an edit = drifting" rule failing to fire (it's soft, no enforcement). The agent never gets concrete substrate feedback because it never tested anything. Reasoning_content can hit 50K+ chars total across the session. Confirmed historically in lang_python311-20260601-222113Z. Flag: count of put_file overlay.dops and count of dsynth_build calls; both zero with budget-exhausted is the signature.
Static-prompt bloat from ## Port Files section. The runner inlines every file under port/<origin>/ into the user prompt regardless of whether the agent will read it. On ports with many files/patch-* + a giant pkg-plist (python311's was 533KB; the inlined section was 48KB of a 96KB user prompt), this section can dominate the static prompt and re-ship 10-12K tokens per turn for files the agent never queries. The agent has get_file and can pull on demand; pre-emptive inlining pays a quadratic cost. Confirmed historically in lang_python311-20260601-222113Z.
Raw get_file overlay.dops on multi-target overlays instead of get_effective_overlay. The raw read returns ops for every target; the agent then has to manually filter by the env's compose target. On wide overlays this is both error-prone (silent inclusion of out-of-scope ops in the agent's mental model) and expensive (per-turn carry of unscoped ops). Patch-flow tool get_effective_overlay returns the filtered view as structured data. Flag any patch trace that reads raw and never reads effective.
Proof-block orphan — successful build but budget cut off before the proof JSON was written. Distinct from plain budget-exhausted: dsynth_build returned rebuild_ok=true and the agent attempted one more tool call (typically emit_diff or a final get_file), but the budget check after the LLM turn refused dispatch. The runner then synthesizes a rebuild_ok=false proof, writes manual_handoff.md instead of proposed_fix.md, and classifies the run as budget-exhausted — misrepresenting a correct fix as a failure. Detection signature: rebuild_proof.json.synthetic=true AND changes.diff is non-empty AND manual_handoff.md exists AND the tool_trace shows a successful dsynth_build followed by one more tool call. When you see this, surface it as [high] and tell the operator the fix is actually applicable; the handoff document will be misleading because its "Last Failing Build" section reflects the pre-success state, not the actual final outcome. Confirmed in devel_nspr-20260606-001249Z (budget overrun ~59K tokens, ~5% over a 1.2M ceiling). Calibration signal: patch-error bundles with N stale patches appear to need ~N × headroom; the current ASSIST budget may be undersized for multi-patch decay.
genpatch → install_patches always fails. genpatch returns an output_dir value that is a chroot-internal path (/root/.cache/dports-dev/envs/<env>/writable/work/genpatch-out). install_patches then looks for files at that path host-side, where it does not exist, and raises FileNotFoundError. Every trace that calls install_patches after genpatch will hit this. The correct post-genpatch sequence is get_file <patch_path under wrksrc> → put_file dragonfly/patch-<...> to write the patch directly into the repo. Agents that find this on their own typically burn ~2 turns + ~50K tokens recovering; agents that don't may loop. Flag any trace containing install_patches and note whether the agent self-corrected. Real fix is either changing genpatch's return to the host-side sidecar path, or removing install_patches from the tool surface entirely. Confirmed in devel_nspr-20260606-001249Z turns 8-10.

Historical (pre-Step-42) failure modes

These were intent-flow-specific (the deleted edit-intent layer: apply_intent/intent_reference tools, intent_log.json artifact, per-directive intent renderers and schemas, intent-*.md playbooks). They no longer apply to current bundles, but if you're analyzing an archived bundle from before commit 3788ed20b58 (2026-06-06) you may still see these patterns. The list is also preserved as forensic context for why the layer was deleted.

Intent-flow: agent retries past intent_log_full=True. Should have escalated to MANUAL — the log either hit count or byte caps.
Intent-flow: agent ignores blocked_by: substrate_invariant. Half-migrated substrate had to be resolved by an operator or convert agent before patch intents could land; ignoring the block kept getting refused.
Intent-flow: agent ignores blocked_by: transaction_mode_drift. Once the first apply_intent pinned mode_at_apply, subsequent calls in a different mode were refused.
Intent-flow: substrate_diff disagrees with the rendered changes.diff. Concat of ok=true substrate_diff values should have equalled changes.diff; drift meant either the diff accumulator was buggy or the canonical-log path was bypassed (canonical_log_broken=true).
Intent-flow: patch job aborted with patch_preflight_dirty or patch_preflight_error. Now subsumed under the unconditional assert_port_clean preflight — same diagnosis, the env had uncommitted edits or was in an unknown state.
Intent-flow: change_makefile op=set appended duplicate mk set lines. Two sequential set intents on the same key produced additive substrate_diffs and a final overlay with duplicate mk set KEY lines; the agent rationalized "last set wins" in patch.md. Confirmed in databases_redis-20260526-205826Z. One of the structural reasons the layer was deleted.
Intent-flow: add_patch for a wrksrc-only target shipped the wrong overlay shape. Should have been add_file kind=materialize; agent typically self-corrected after 2-3 turns. Confirmed in devel_libuv-20260601-222117Z.
Intent-flow: drop_patch left the patch file orphaned on disk. Removed the overlay reference but not the file under dragonfly/; a subsequent add_patch with the same target failed with "patch already exists." Confirmed in devel_libuv-20260601-222117Z.
Intent-flow: pre-emptive intent_reference batching. Agent fetched references for 3-5 intent types upfront before committing to any. Each unused reference was 3-6KB of context carry. Confirmed in devel_libuv-20260601-222117Z.
Step-27 telemetry: playbooks_selected activity rows missing. Confirmed absent in databases_redis-20260526-205826Z. Cause traced to queue_root handling in _log_playbook_selection. Worth a spot-check on current bundles too — if playbooks_selected is still missing post-Step-42 the same regression survived.

Report shape

Produce something like this (markdown, no fluff):

# Agentic analysis — <origin> (<bundle-id>)

## Summary
<2-3 sentences: what the agent tried to do, did it land, is the result trustworthy>

## Pipeline
<which jobs ran, in order, with state>
- triage-1: <classification> (<tokens>)
- convert: <status> (<tokens>)        ← if convert ran
- triage-2: <classification> (<tokens>) ← if convert ran; same/different from triage-1?
- patch: <status>, attempts=N (<tokens>)

## Triage
- Classification: <X> (confidence: <Y>) — <assessment: correct / questionable / wrong>
- Root cause as stated: <quote>
- Actual root cause from logs: <if different>
- Round changes: <if 2 triages, same/different conclusion, why>

## Convert (if convert ran)
- Status: <verified / failed / no-op>
- reapply_ok: <true / false>
- Overlay target directive: <@any (correct) / @main (REGRESSION) / other>
- Deferred patches: <count> [<paths>]
- Tokens: <prompt / completion / total>

## Patch
- Status: <success / needs-help / budget-exhausted / blocked-by-substrate>
- Attempts: <N> / tier max
- Tokens: <prompt / completion / total>
- Tool sequence: <one-line summary of the trace>
- overlay.dops edits: <N put_file writes, each followed by validate_dops? Y/N>
- Fix narrative: <what patch.md claims>
- Fix verdict: <is the fix real?>

## Per-bundle metrics
- Turn-to-first-meaningful-edit: <N> (floor ~10-15 on clean success; 0 = paralysis)
- overlay.dops edits emitted: <N> / reversed mid-attempt: <M> (self-correction count)
- dsynth_build calls: <N> (must be ≥1 for any meaningful run)
- After dsynth_build success/fail, verification approach: <tool exit code only / grep extracted source for symptom / no build run>

## Materialize signal (P0a/P0b regression check)
- materialize_dports calls: <N>
- Each call's summary: <applied=N skipped=M errors=K, warnings present>
- I_COMPOSE_DOPS_ALL_OPS_SKIPPED: <absent on every materialize (healthy) / present on N calls (overlay target mismatch)>
- Any fresh overlay with `target @main`? <yes/no> (yes = P0a regression — flag immediately)

## Path discipline
- /work/DPorts/<origin> reads: <count> (forbidden; lock root)
- /xports/ or other non-/work paths passed to chroot tools: <count>
- Hand-constructed /work/obj paths not derived from extract.wrksrc: <count>
- Host-side path leaks (e.g. genpatch output_dir confusion): <count>
- Raw `get_file overlay.dops` on a multi-target port (should use get_effective_overlay): <count>

## Output contract
- changes.diff: <bytes> — <ok / empty-bug / mismatched>
- proposed_fix.md: <present and references diff / present but stale / absent on success (bug)>
- manual_handoff.md: <present on escalation / absent on escalation (bug)>
- rebuild_proof.json: <present / absent on terminal failure (bug)>

## Playbooks (Step 27 + Step 42)
- Triage: included=<list or "—">, skipped=<count> — <looks right / suspicious / missing event>
- Patch: included=<list or "—">, skipped=<count>, flow-patch.md present? <yes/no>
- Convert (if convert bundle): included=<list>, skipped=<count> — <…>

## Token shape
- Static prompt (system + user): <bytes> ≈ <tokens>/turn × <N> turns = <subtotal>
- User prompt composition (if >50KB): break down by `## section`, flag sections >10KB the agent never read
- Reasoning_content cumulative: <chars>; single-turn outliers >5KB: <list>
- Top-3 biggest tool returns: <name, bytes>
- Estimated breakdown by source vs. actual total from `patch_result`: <static/reasoning/tool_carry/completion percentages>

## Inefficiencies
- <bullets>

## Bugs / contract violations
- <bullets, each tagged with severity: low / medium / high>

## Lifecycle context
- Prior bundles for this port: <count, dates>
- Loop behavior: <one-shot / retrying / thrashing>

## Skill update suggestions
- <anything this analysis surfaced that this SKILL.md should have warned about>

Keep it terse. The operator skims this. Per-bundle metrics + materialize signal + path discipline are the minimum data set — every bundle gets these even when nothing surprising shows up, so we can spot drift over time.

Delegating to a subagent

If the bundle is large or you have several to analyze, spawn a sonnet 4.6 subagent:

Agent({
  subagent_type: "general-purpose",
  model: "sonnet",
  description: "Analyze agentic bundle <id>",
  prompt: "<paste the Expected behavior + Known failure modes + Report shape sections from
           .claude/skills/dportsv3-agentic-analysis/SKILL.md, plus the bundle ID and tracker
           base URL>. Fetch the artifacts via /api/bundles/<id>/artifacts/<path> with curl
           (HTTP only, no HTTPS upgrade). Produce the report in the shape specified."
})

Read the returned report, sanity-check the bug claims against the raw artifacts if anything looks wrong, then hand it to the user.

name

dportsv3-agentic-analysis

description

DeltaPorts agentic analysis

What this skill is for

Inputs you need before starting:

Port origin (e.g. devel/gperf) or a bundle ID.
Tracker base URL — read from the DP_TRACKER_URL env var, or ask the user. There is no hardcoded default; the tracker host is operator-specific.

Output: a report with what the agent did, whether that matches the expected contract, inefficiencies, bugs, and skill update suggestions.

How to fetch data

Locating the `dportsv3` binary

The CLI is not always on $PATH. At the start of your analysis, resolve the binary once and reuse it:

# Repo-root-anchored fallback. The venv binary is the project's
# own and is the safe deterministic location.
DPORTSV3="${DPORTSV3:-$(command -v dportsv3 || echo /Users/tuxillo/s/DeltaPorts/scripts/generator/.venv/bin/dportsv3)}"
# Verify before using it:
"$DPORTSV3" tracker --help >/dev/null \
    || { echo "dportsv3 unusable at $DPORTSV3" >&2; exit 1; }

Then every later call uses "$DPORTSV3" tracker get-bundle … etc. If you can't make the CLI work after that probe, fall back to curl and flag the gap.

CLI commands (prefer these)

Purpose	Command
List recent bundles for a port	`dportsv3 tracker list-bundles --origin <category/port> --limit 10`
Get one bundle's detail (incl. artifact list)	`dportsv3 tracker get-bundle <bundle-id>`
Same, structured JSON	`dportsv3 tracker get-bundle <bundle-id> --json`
Fetch one artifact's raw bytes (logs/diffs/JSON)	`dportsv3 tracker fetch-artifact <bundle-id> <relpath>`
Get one job by ID	`dportsv3 tracker get-job <job-id>`
List jobs (filter by state)	`dportsv3 tracker list-jobs --state dead --limit 20`
Activity log for one job	`dportsv3 tracker get-activity --job <job-id> --limit 200`
Activity log filtered by stage	`dportsv3 tracker get-activity --job <id> --stage tool:`

HTML pages (use only when no equivalent API exists)

Purpose	URL
Agentic dashboard (rendered overview)	`GET /agentic`
Bundle detail rendering	`GET /agentic/bundles/<bundle-id>`

Discovery flow

dportsv3 tracker list-bundles --origin devel/<port> --limit 5 — find recent bundle IDs.
dportsv3 tracker get-bundle <id> --json — full detail with the artifact list.
For each artifact you actually need to read: dportsv3 tracker fetch-artifact <id> <relpath>.
If multiple bundles exist for the same port, analyze the most recent and scan timestamps of prior failures to see if the loop kept retrying.

Why not curl?

The CLI handles URL encoding of slashed paths (devel/gperf → devel%2Fgperf) without you remembering.
Structured output (or --json for full JSON) means no grep/sed/jq pipelines that break on whitespace or special characters in error messages.
The single binary already knows the tracker URL (DPORTSV3_TRACKER_URL) so you don't repeat it.

Fall back to curl only if the CLI is missing a command you need; then file a SKILL-update suggestion so the next analyzer doesn't have to fall back.

Artifacts that matter

All under /api/bundles/<bundle-id>/artifacts/:

Path	What it tells you
`meta.txt`	Origin, target, timestamps, dsynth profile.
`logs/errors.txt`	dsynth tail — the actual failure the agent reacted to.
`analysis/triage.md` + `triage.json`	Classification, confidence, root cause, suggested fix.
`analysis/patch.md`	Agent's narrative of what it did and why.
`analysis/patch_audit.json`	Status, model, token usage, attempts breakdown.
`analysis/rebuild_proof.json`	`rebuild_ok` + build command — the success gate.
`analysis/changes.diff`	The operator-applyable diff. Empty diff with `rebuild_ok=true` is always a bug. Since Step 42 there is no intent-log fallback — this is the canonical record.
`analysis/tool_trace.jsonl`	Per-turn tool calls + their results, lifecycle events (`attempt_start`, `llm_turn`, `tool_call`, `attempt_end`, `token_budget_exhausted`). Cheap inefficiency scan. Does NOT contain assistant message text or `reasoning_content` — for that, read the session dumps below.
`analysis/sessions/*.jsonl.gz`	Full LLM message transcripts (one file per attempt per job). The single most informative artifact: every assistant turn's `content` + `reasoning_content`, every `tool_calls` entry with full arguments, every tool result message the model actually received. Read these when tool_trace.jsonl isn't enough — i.e. whenever you need to understand why the agent did what it did, not just what tools fired. Available only when `DP_HARNESS_DUMP_SESSION` was on at run time. See below for record structure + parsing.
`analysis/proposed_fix.md`	Operator-facing recipe written when `rebuild_ok=true`.
`analysis/manual_handoff.md`	Operator-facing escalation summary written when the job escalates (MANUAL tier, retry cap, budget exhaustion, gave-up). Either this or `proposed_fix.md` is present, not both.
`analysis/convert_result.json`	Convert phase typed result (when a convert job ran). Carries `status`, `reapply_ok`, `deferred_patches`.
`analysis/triage_result.json` / `patch_result.json`	Typed phase results (Step 36) — root cause + evidence lifted into structured fields, consumed by downstream phases.
`port/Makefile`, `port/distinfo`, `port/pkg-plist`	Snapshot of the port at failure time.

LLM session dumps — structure and parsing

Each analysis/sessions/<timestamp>-<target>-<origin>-<pid>[-<role>].job.attempt<N>.jsonl.gz is the complete messages array as it stood at the end of one tool_loop attempt. The filename embeds:

the role suffix: -convert, -patch, or no suffix for triage
attempt number: attempt1, attempt2 (patch can retry, convert/triage are single-attempt)

Decompressed: one JSON object per line, each a message in the LLM-API shape:

{"role": "system", "content": "<system prompt verbatim>"}
{"role": "user", "content": "<initial task payload, plus any failure-context message inserted at attempt N>1>"}
{"role": "assistant", "content": "<text>", "reasoning_content": "<deepseek thinking>", "tool_calls": [...]}
{"role": "tool", "content": "<JSON tool result>", "tool_call_id": "..."}

What each role tells you:

system (record 0): the exact prompt the agent saw. Verify any prompt-rule reference (e.g. "the agent went to /work/DPorts despite the prompt saying not to") by grepping the system content.
user (record 1): the assembled payload. Always check len(content) — anything >50KB warrants a section-by-section breakdown by ## heading. Common offenders: ## Build Errors (dsynth log tail), ## Port Files (every file under port/ inlined), ## Agent Playbooks (matched playbook content).
user (record 2+, attempt 2+ only): the failure-context message from attempt_loop._failure_context_message. After Step 42 this is just Previous attempt #N did not succeed.\nTail of your prior response:\n<last 2KB>\n — no structured intent-log summary. If the agent re-submits the exact same wrong edit in attempt 2, the lesson is in those last 2KB of attempt-1 prose.
assistant: the agent's per-turn output. Three fields you care about:
- content (text the model wrote) — usually small except in the final "## Patch Log / ## Rebuild Proof" turn.
- reasoning_content (deepseek thinking-mode output) — often the largest single field. Carried on every subsequent turn per deepseek's contract, so accumulates quadratically. A 13KB single-turn reasoning blob is ~3K tokens × every remaining turn.
- tool_calls — array of {id, function: {name, arguments}}. The arguments are JSON-stringified; parse before inspecting.
tool: the JSON result the model received. Contents match what worker.py returned: {ok, ...}. For materialize_dports: stdout_tail is what the agent reads to decide the apply landed. For validate_dops: {ok, stderr_tail} with E_* error codes on failure. For get_effective_overlay: {target, effective_ops, filtered_out} — what compose WILL apply vs. ops scoped to other build lines. For put_file: {ok, sha256, ...}.

Fetch + decompress in the same shell pipeline as the rest:

BID=<bundle-id>
"$DPORTSV3" tracker get-bundle "$BID" --jobs --json | python3 -c "
import json,sys
d=json.load(sys.stdin)
for a in d.get('artifacts',[]):
    if 'sessions/' in a['relpath']:
        print(a['relpath'])
" | while read rel; do
    out="$(basename "$rel")"
    "$DPORTSV3" tracker fetch-artifact "$BID" "$rel" > "$out" 2>/dev/null
    gunzip -k "$out"   # keep both .gz and decompressed
done

Walking a session (Python, one-shot):

import json
recs = [json.loads(l) for l in open('<file>.jsonl') if l.strip()]
print(f"records={len(recs)} system={len(recs[0]['content'])}B user={len(recs[1]['content'])}B")
for i, r in enumerate(recs):
    if r['role'] in ('system','user'): continue
    if r['role'] == 'assistant':
        tcs = [t['function']['name'] for t in (r.get('tool_calls') or [])]
        rc = r.get('reasoning_content') or ''
        print(f"#{i} ASST text={len(r.get('content') or '')}B reason={len(rc)}B tools={tcs}")
        # show big reasoning blobs
        if len(rc) > 1000:
            print(f"  R: {rc[:400].replace(chr(10),' ')!r}...")
    elif r['role'] == 'tool':
        try:
            d = json.loads(r['content']); ok = d.get('ok')
        except Exception:
            ok = None
        print(f"#{i} TOOL ok={ok} sz={len(r['content'])}B")

When tool_trace.jsonl is enough vs. when you need session dumps:

tool_trace alone suffices for: "which tools fired in what order", "did the agent call dsynth_log after build failure", "how many put_file writes hit overlay.dops", basic efficiency scan.
Need session dumps for: "why did the agent go down path X" (reasoning_content), "what did the agent actually observe in the materialize summary" (full tool result content vs. trace's truncated keys), "did the agent see the failure-context message between attempts" (user-record-2 in attempt 2), "what was the static prompt's exact text" (system record). Anything that requires understanding the agent's mental model needs the session dump.

A session dump may not exist for older bundles or bundles run without DP_HARNESS_DUMP_SESSION=1. The text format of analysis/tool_trace.jsonl is always present and is the fallback.

Bulk-fetch the bundle in ONE shell command, not N small calls.

Every Bash invocation costs a permission prompt and a tool turn. The whole analysis should fit in 2–4 Bash calls total:

List + pick (1 call): "$DPORTSV3" tracker list-bundles --origin <port> --limit 5

Full bundle dump in one shell pipeline (1 call):

BID=<bundle-id>
echo "===== bundle detail + jobs ====="
"$DPORTSV3" tracker get-bundle "$BID" --jobs --json
for f in meta.txt logs/errors.txt analysis/triage.md analysis/patch.md \
         analysis/patch_audit.json analysis/rebuild_proof.json \
         analysis/changes.diff analysis/tool_trace.jsonl \
         analysis/proposed_fix.md analysis/manual_handoff.md \
         analysis/convert_result.json; do
  echo "===== $f ====="
  "$DPORTSV3" tracker fetch-artifact "$BID" "$f" 2>/dev/null \
    || echo "(absent)"
done

Activity log if needed (1 call): "$DPORTSV3" tracker get-activity --job <job-id> --limit 200

Note: proposed_fix.md and manual_handoff.md are mutually exclusive (one per terminal outcome); convert_result.json is absent on patch-only bundles. The || echo "(absent)" handles all three.

Expected behavior (current code contract)

This section is what the agent should be doing if the code is working. Update it when the code evolves.

Lifecycle

A failure bundle is written by the dsynth hook. The runner enqueues a triage job.
Triage job runs dportsv3.agent.triage.run — single LLM call (no tools), may do up to DP_HARNESS_MAX_SNIPPET_ROUNDS (default 5) snippet-extractor rounds.
Triage emits classification, confidence, root-cause text. config/agentic-policy.json resolves a tier (AUTO / ASSIST / MANUAL).
Routing is driven by assess_dops (the substrate gate), not by triage classification. At a failure with no overlay.dops, the runner (_ensure_overlay_or_abort → overlay_state.bootstrap_decision) deterministically writes a header overlay (type port/dport per STATUS) so patch can author the body, or aborts to MANUAL when non-dport compat artifacts are present. (Step 48 deleted the runtime convert agent — there is no longer a convert job; older bundles may still show one.)
AUTO/ASSIST → auto-enqueue patch (after the deterministic bootstrap, if substrate wasn't already dops). MANUAL → stop.
Patch job runs dportsv3.agent.patch.run → attempt_loop.run up to tier.max_iterations attempts, each driving a tool_loop until the LLM stops requesting tools.
Success gate: rebuild_proof.json parsed from the LLM's ## Rebuild Proof (JSON) block with rebuild_ok=true.

Tool surface

Triage agent has no tools — single-turn LLM call with snippet rounds. System prompt: prompts.TRIAGE_SYSTEM.

Check dportsv3/agent/tools.py and dportsv3/agent/prompts.py directly if uncertain.

Substrate contract

All chroot-bound operations route through dportsv3 dev-env exec <env> -- ....
Host filesystem ops only on env_dir/writable/... (resolved via dportsv3 dev-env path <env> --writable).
No git commits, branches, push, or PRs from the loop. The writable overlay is the workspace; analysis/changes.diff is the audit trail. Delivery to upstream (PR open / patch outbox) is a separate operator-triggered phase via dportsv3.delivery.orchestrator.
Guards collapse to three generic gates: validate_dops / check_dsl (DSL syntax + semantics), assess_dops (substrate-state gate that decides whether patch can proceed), and the _resolve_path path-escape backstop. assert_port_clean (the pre-job clean preflight) runs unconditionally.

Substrate / mode handling

dops grammar (what `overlay.dops` looks like)

Quick orientation (full grammar in the quickref):

file.materialize { source = "src/path"; dest = "dest/path" } — copy a file from the dragonfly source tree into the port at compose time. Used for porting in DragonFly-specific replacements of upstream files.
file.copy { source = "..."; dest = "..." } — copy a file from port-local resources (no dragonfly source tree).
patch.apply { target = "path/in/wrksrc"; diff = """...""" } — apply an inline unified diff at patch phase. The diff payload is the actual patch content — when context drifts, the diff string inside overlay.dops is what needs editing, not a dragonfly/patch-* file.
mk set / mk add / mk remove / mk replace-if — Makefile.DragonFly variable directives. Sequential mk set on the same key REPLACES (the engine collapses duplicates); the agent's mental model should not produce duplicate mk set lines.
mk target set/append — recipe lines for a make target.
Per-target scoping via target @main / target @2026Q[1-4] / target @any. @any is the right default for fresh overlays; @main filters every op away on quarterly builds (silent-skip bug class — see "Target-mismatch ghost" below).

Mode-correctness checks for the analyzer

For any patch or convert bundle, verify in the trace:

Did the agent read overlay.dops early? (It should — that's the source of truth.) Prefer get_effective_overlay reads over raw get_file overlay.dops reads — on multi-target overlays the effective view filters out ops scoped to other build lines, eliminating manual filtering errors.
If a patch hunk drifted: did the agent edit the diff = """...""" block inside overlay.dops (correct), or did it put_file to dragonfly/patch-* (wrong — silently clobbered on next reapply, the put_file boundary should refuse this on dops ports but verify it did)?
If a patch became obsolete: did the agent remove the corresponding patch.apply / file.materialize block from overlay.dops, or did it put_file an empty dragonfly/patch-*? Only the first is durable.
Did validate_dops run after every put_file overlay.dops? It should — that's the dops equivalent of "does this even parse." Skipping it and going straight to materialize_dports is a discipline regression.
Did materialize_dports re-run between the dops edit and dsynth_build? Compose needs to re-render the port tree from the edited overlay before dsynth sees the change.
For a fresh overlay: does the header carry target @any (correct) or target @main (silent-skip regression — see failure modes)?

Output contract for operators

changes.diff must contain the operator-applyable diff. Empty diff with rebuild_ok=true is a contract violation. Since Step 42 there's no intent-log fallback — this single artifact is the canonical record.
On success, proposed_fix.md is written by dportsv3.agent.proposed_fix and must reference a non-zero diff.
On escalation (MANUAL tier, retry cap, budget exhaustion, gave-up), manual_handoff.md is written by dportsv3.agent.manual_handoff instead.

Playbook library (Step 27 + Step 42 reframe)

error-*.md — triggered by triage classification (triggers.classifications:); attached to triage + patch payloads.
convert-*.md — attached to convert payloads when triggers.flows contains convert.
toolchain-*.md — triggered by mechanical toolchain detection on the port Makefile (playbooks.py::detect_toolchains — parses USES=, GNU_CONFIGURE=, file-presence signals like CMakeLists.txt, Cargo.toml).
flow-patch.md — the single consolidated patch playbook (Step 42 collapsed the 12 deleted intent-*.md entries into one). Triggered by flows: [patch]. Carries the durable knowledge previously split per-intent: mk-directive traps, scoping judgment (@any vs @main), the static-patch workflow, broken-patch recovery, PORTREVISION handling.

The intents trigger axis and the per-call intent_reference on-demand attachment path are both gone. Every playbook that fires is attached at payload-build time.

What to check in an analysis:

For a triage/patch bundle: did playbooks_selected fire, and does included include the entries you'd expect given the classification and the port's Makefile? E.g. a patch-error triage on a USES=cmake port should pull error-* entries gated on patch-error plus toolchain-cmake.md. Empty included on a port with a recognized toolchain is a red flag — likely a missing trigger or a find_playbooks_dir() failure.
Every patch bundle should include flow-patch.md. Its absence on a patch role row points at the flows: [patch] trigger or the selector wiring.
Convert bundles: did playbooks_selected with role=convert fire and include convert-target-directive.md and convert-classify-patch-domain.md?
Does the skipped_sample reveal a likely-buggy entry (e.g. a toolchain-cmake.md skipped with toolchains:no-overlap-with-['cmake'] when the port's Makefile clearly has USES=cmake)? That points at detect_toolchains() not seeing the Makefile.

Known parser quirks (smoke-testing surface, mention if you spot symptoms):

_parse_inline_list only parses inline-form YAML ([a, b]). Block-form list (- a\n - b) silently yields (), which means wildcard in the selector. A new entry that "fires on everything" probably has block-form triggers.

Analysis checklist

For each bundle, work through these questions and write the report against them. Skip ones that don't apply, but say so.

1. Pipeline shape

Before drilling into per-job correctness, sketch the pipeline. The canonical flows are:

Patch only (substrate already dops): triage → patch. One triage session.
Convert-then-patch: triage → convert → triage (re-classifies against the converted substrate) → patch. Two triage sessions is expected here, not a bug.
Convert only (substrate-only fix): triage → convert. No patch session.
Convert failed: triage → convert (gave up / budget). No patch session.

2. Triage correctness

Does the classification match what logs/errors.txt actually shows? (e.g. a fetch failure misclassified as compile-error is a triage bug.)
Is the confidence appropriate?
Did snippet rounds happen, and were they useful? (Look for snippets/round_N/ artifacts.)
If two triage rounds ran (pre-convert + post-convert), did classifications change between them? If yes, note why — typically convert promoted compat→dops which changes the substrate's failure expression.
Record each triage's token usage; the second triage often duplicates the first when convert didn't change failure shape (cheap re-run, expected).

3. Convert correctness (if a convert job ran)

Status from analysis/convert_result.json: verified / failed / no_conversion_proof_block. reapply_ok=true means convert produced a valid overlay that compose accepts.
Verify target @any in the produced overlay. Read analysis/changes.diff or grep the env's overlay.dops for the header. Anything other than target @any is a regression of the post-2026-05-26 fix (commits d71f605c206 + 47846e7a392). @main in a fresh overlay means every op will silently skip at compose against @2026Q2 (per engine/apply.py:296-313).
Deferred patches: list deferred_patches from convert_result. Each entry says what the dropped framework patch was DOING (intent, not authority). The downstream patch agent should address each one — verify it does.
Tokens, attempts. Convert's budget is tighter than patch's; a convert that hit budget-exhausted after one validate_dops parse error is a known weak spot.

4. Patch correctness

Did the agent reach rebuild_ok=true?
Does the fix actually fix the root cause, or did it bypass the problem? (E.g. removing a patch the agent declared obsolete vs. actually adapting it — both may produce rebuild_ok=true, but only one is right. Cross-check patch.md's reasoning against the upstream code it read.)
Did the agent edit overlay.dops (correct) or dragonfly/* files directly (wrong, the put_file boundary should refuse — verify it did)?
For each put_file overlay.dops, was validate_dops called before the next materialize_dports?
Did the agent escalate cleanly when blocked by assess_dops substrate gates (e.g. half-migration), or did it keep retrying?
Turn-to-first-meaningful-edit. Count tool turns from session start to the first put_file that targets overlay.dops (or dragonfly/* on the rare valid case). Floor on a clean success appears to be ~10-15 turns (opening + investigation + dops_reference if writing fresh). Values >20 suggest over-exploration; 0 means the agent never committed to a hypothesis (paralysis — flag).
Self-correction. Count edits that were later reversed within the same attempt (e.g. an overlay.dops write replaced by a corrected version after a failed materialize_dports). One self-correction per run is healthy (the agent learned from compose/build feedback). Many self-corrections suggest the agent is thrashing; zero self-corrections paired with budget-exhausted may mean the agent never tried anything concrete enough to fail informatively.

5. Path discipline (scan tool calls)

Reads of /work/DPorts/<origin>/...? Per prompts.PATCH_SYSTEM the agent may NOT read from this path — it's the LOCK ROOT, last-known-good, will disagree with extract output. Note every occurrence.
Reads of /xports/... or any other chroot-internal path that isn't under /work/? Tools fail with ValueError: path must be under /work (worker.py _resolve_path). One occurrence = honest mistake; multiple = the agent didn't read the build log's path notation correctly.
Hand-constructed /work/obj/<origin>/... paths that didn't come from extract's wrksrc field? The prompt explicitly forbids constructing these. Compare against extract's wrksrc return value.
Host-side path leaks: tools returning host paths (e.g. /root/.cache/dports-dev/...) that the agent then passes to chroot-path-expecting tools. genpatch's output_dir return was a known case.

6. Materialize cycle signal (P0a/P0b regression check)

For every materialize_dports call (each attempt typically has 1-3), check the summary: line in stdout_tail:

applied=N>0 — ops actually applied to the compose tree. Healthy.
applied=0 with skipped>0 — ops were filtered by target mismatch. Expect the I_COMPOSE_DOPS_ALL_OPS_SKIPPED warning in the same stage line (per commit 663a8eae819). If the warning is present, surface it; the agent should see it too. If applied=0 skipped>0 but the warning is absent, that's a P0b regression.
applied=0 with errors>0 — at least one op failed (parser, executor error). Distinct from skipping; check dops_failed_op_results in compose report.
applied=N>0 and skipped=0 on a freshly-created overlay confirms P0a is functioning (the convert / patch agents emit target @any headers per commits d71f605c206 / 47846e7a392).

If you see target @main in any agent-emitted fresh overlay, that's a P0a regression — flag immediately.

7. Build verification (after dsynth_build)

After dsynth_build returns rebuild_ok=true, did the agent verify by grepping the extracted source for the original error symptom, or rely on the tool exit code alone? For deterministic failures (linker duplicate symbol, missing-include compile error), trusting exit code is fine. For symptom classes where multiple bug sites can produce the same error (e.g. __result undefined in N headers), grep-the-symptom catches incomplete fixes.
After dsynth_build failed, did dsynth_log immediately follow? The prompt says to call it immediately on build failure. If the agent went back to exploring instead, that's a discipline regression.
Did the agent run dsynth_build at least once? budget-exhausted with zero build calls is the worst possible signal — the agent never tested anything, never learned from substrate feedback, just analyzed.

8. Output contract

Is analysis/changes.diff non-empty when rebuild_ok=true?
On success: is proposed_fix.md present and does it reference the diff?
On escalation: is manual_handoff.md present? rebuild_proof.json should also exist with rebuild_ok=false and a reason — its absence on a terminal-failure bundle is a regression (see failure modes).
Does the diff actually match what patch.md says was changed?

9. Efficiency — quantitative breakdown

Don't say "the loop was expensive". Break it down:

Static prompt cost. Sizes of messages[0] (system) + messages[1] (initial user). Multiply by turn count for the per-turn ceiling. On a clean ASSIST run this is typically 35-50% of total tokens; on bloated cases (>50KB user prompt) it dominates.
User prompt composition. When the user prompt is >50KB, break it by ## section heading and flag oversized sections (>10KB) the agent never read via subsequent tool calls. Common offender: ## Port Files which inlines every file under port/; the agent has get_file and can pull on demand.
Reasoning_content accumulation. Sum reasoning_content byte sizes across all assistant turns. Note single-turn outliers (>5KB is a "thinking hard" turn — fine on hard ports, suspicious on simple ones). Deepseek thinking-mode requires reasoning_content carry on every subsequent turn, so this accumulates quadratically.
Tool result carry. Identify the top-3 biggest tool returns by byte size. Each dsynth_log is ~10-16KB. A 16KB result carried across 10 subsequent turns is ~40K tokens. Also watch get_effective_overlay on large multi-target overlays.
Completion. Usually small unless the agent wrote a long Patch Log.
Sum the per-turn prompt sizes (from the llm_turn activity events if present, or estimate as static + cumulative reasoning + cumulative tool results). Compare to patch_result.tokens_total — a big discrepancy may indicate the trace is missing entries.

Other efficiency checks:

Redundant tool calls — e.g. multiple emit_diff calls in a row, or materialize_dports called twice when once would do.
Did the agent re-read files it had already read?
Did it call tools with the wrong args (e.g. passing origin where relpath was expected)?
Did the agent use raw get_file overlay.dops on a multi-target port instead of get_effective_overlay? On wide overlays this can double the per-turn carry.

10. Playbook coverage (Step 27)

Did playbooks_selected fire for each role this bundle ran? (triage always; patch if it reached patch; convert if it's a convert bundle.)
Does included look right for the bundle's classification + detected toolchains? Empty included on a port with recognized USES= is a red flag.
For every patch bundle: is flow-patch.md in included? Its absence is a Step-27/42 wiring regression.
Does the skipped_sample reveal a likely-buggy entry (e.g. a toolchain-cmake.md skipped with toolchains:no-overlap-with-['cmake'] when the port's Makefile clearly has USES=cmake)? That points at detect_toolchains() not seeing the Makefile.
Caveat: the text-format playbooks_selected activity row only shows counts (included=N skipped=M), not filenames — which means the "does included look right" check above is unverifiable from the default get-activity output. To actually verify which entries fired, pull raw JSON: dportsv3 tracker get-activity --job <id> --json and inspect the row's payload field for the filename list. If --json doesn't expose it either, fall back to curl http://<tracker>/api/jobs/<id>/activity and grep for playbooks_selected. If no surface exposes the filenames, flag as a tracker feature gap and treat the count as a black box.

11. Lifecycle hygiene

Always run list-bundles --origin <category/port> --limit 10 for the port even if the current bundle looks like a clean one-shot. It's one cheap CLI call and gives ground truth about prior agentic activity on this port. The port/STATUS file (when present in the bundle snapshot) is a compat-era artifact that convert deletes — it can hint at upstream version history but is not a substitute for the tracker query and disappears entirely on already-dops ports.
Was this port previously bundled? If so, did the loop converge (older bundles accepted / verified) or thrash (multiple dead / budget-exhausted over time)?
If MANUAL tier: was the classification one that should have been AUTO/ASSIST?
assert_port_clean is now unconditional. If the patch job aborted with a clean-check failure, that's an env-state problem (uncommitted edits from a prior run, chroot in unknown state), not a patch agent bug.

Known failure modes (extend this list)

Patterns seen in the wild. When you see a new one, append it here and flag it in your report's "Skill update suggestions" section so the operator folds it in.

Empty changes.diff with rebuild_ok=true. Agent edited files inside the writable overlay but the runner's diff capture didn't pick it up. Suspected causes: overlay isn't a git working tree, or the diff scope path is wrong. Operator gets "agent fixed" with nothing to land. Always flag this as a bug. Confirmed historically for dops-mode put_file writes to overlay.dops (devel_gperf-20260523-094119Z) and for freshly-created overlay.dops files (multimedia_v4l_compat-20260523-101601Z) — the bug fires on any put_file write the overlay's baseline misses. Verify on every current bundle that the diff is non-empty whenever a put_file against overlay.dops succeeded.
Agent passes origin where emit_diff wants a relpath. Tool signature is emit_diff(env, origin, relpath); the LLM sometimes passes only origin (e.g. "devel/gperf"). Either the prompt is unclear or the schema is.
Agent proceeds against a substrate assess_dops flagged as not-yet-converted. The runner should refuse state:<state> and escalate; if the trace shows the patch agent continuing past such a block it's a runner/policy bug.
Agent declares a patch "obsolete" based on shallow upstream inspection. Removing a patch and getting a green dsynth is not proof the patch was actually obsolete — it may have addressed a runtime or platform-specific issue dsynth doesn't catch. Flag when the agent's justification is thin.
Wasted get_file turn from a mis-guessed offset. When inspecting a C source file for include directives, the agent sometimes first reads from a non-zero offset and then re-reads from the top, burning a turn. Steer the agent toward grep for include-presence checks instead of speculative offset reads.
rebuild_proof.json missing on budget-exhaustion / give-up bundles. Expected per the success-gate contract: even on terminal failure the runner should emit a proof JSON with rebuild_ok=false and a reason. Operator skimming the artifact list can't distinguish a clean "agent gave up" from "agent crashed mid-attempt." Flag whenever patch_audit.json reports a terminal status but rebuild_proof.json is absent.
Knowledge gap: .for-parsed Makefile list variables and value-with-spaces. Variables like BINARY_ALIAS, MAKE_ENV, PLIST_SUB are iterated by .for var1 var2 in ${VAR} which tokenizes on whitespace and expects N words per row. A value with an embedded space (e.g. BINARY_ALIAS=gmd5sum=md5 -r) produces the compose error Wrong number of words (N) in .for substitution list with M variables. The agent typically misdiagnoses by toggling flags rather than escaping the value or switching to a wrapper script. Worth a dedicated error-for-substitution-list.md playbook.
Attempt-boundary amnesia. When attempt 1 fails on a specific value and attempt 2 receives a fresh context, the agent sometimes re-emits the exact same edit that already failed in attempt 1. After Step 42 the failure-context message is just the last 2KB of attempt-1 prose, so if attempt 1's prose didn't surface the specific edit that broke, attempt 2 has no signal. Flag whenever attempt N writes the same overlay.dops content as attempt N-1.
Premature materialize_dports on the consumer origin before the provider overlay is activated. When a port uses MASTERDIR (or otherwise shares compose artifacts with a sibling origin), the agent sometimes materializes the consumer origin immediately after writing the dops overlay for the provider, before materializing the provider itself. Compose runs against the wrong origin, shows modes: compat=1, and the wasted call is only caught because the agent then self-corrects with a second call to the right origin. Seen in multimedia_v4l_compat-20260523-101601Z. Prompt should steer the agent to always materialize_dports the origin that owns overlay.dops first.
Target-mismatch ghost (target @main in fresh overlay → all ops silently skipped). When an agent emits a fresh overlay.dops, the header MUST be target @any. Compose runs against @2026Q2 (or whatever the env's build target is); per engine/apply.py:296-313 every op with target=@main is filtered with status="skipped" and an info-level I_APPLY_TARGET_MISMATCH diagnostic that didn't bubble to stage output. The summary applied=0 reads as "patch didn't take" and the agent typically diagnoses it as a compose bug, burning hundreds of K tokens chasing the ghost. Mitigated by commit 663a8eae819 (compose stage warning I_COMPOSE_DOPS_ALL_OPS_SKIPPED). When analyzing a bundle: verify the fresh overlay has target @any and that the warning fires on any dead overlay. Confirmed historically in skalibs / libfyaml / gnome_subr 20260601 bundles.
Analysis paralysis — 0 meaningful edits, 0 dsynth_build calls, full ASSIST budget consumed. Agent investigates indefinitely without committing a hypothesis. Often correlated with: (a) a complex ## Deferred from Convert section that invites verdict-first investigation, (b) a port class where the agent can't easily map the bug to a dops edit shape, (c) the prompt's "4+ tool calls without an edit = drifting" rule failing to fire (it's soft, no enforcement). The agent never gets concrete substrate feedback because it never tested anything. Reasoning_content can hit 50K+ chars total across the session. Confirmed historically in lang_python311-20260601-222113Z. Flag: count of put_file overlay.dops and count of dsynth_build calls; both zero with budget-exhausted is the signature.
Static-prompt bloat from ## Port Files section. The runner inlines every file under port/<origin>/ into the user prompt regardless of whether the agent will read it. On ports with many files/patch-* + a giant pkg-plist (python311's was 533KB; the inlined section was 48KB of a 96KB user prompt), this section can dominate the static prompt and re-ship 10-12K tokens per turn for files the agent never queries. The agent has get_file and can pull on demand; pre-emptive inlining pays a quadratic cost. Confirmed historically in lang_python311-20260601-222113Z.
Raw get_file overlay.dops on multi-target overlays instead of get_effective_overlay. The raw read returns ops for every target; the agent then has to manually filter by the env's compose target. On wide overlays this is both error-prone (silent inclusion of out-of-scope ops in the agent's mental model) and expensive (per-turn carry of unscoped ops). Patch-flow tool get_effective_overlay returns the filtered view as structured data. Flag any patch trace that reads raw and never reads effective.
Proof-block orphan — successful build but budget cut off before the proof JSON was written. Distinct from plain budget-exhausted: dsynth_build returned rebuild_ok=true and the agent attempted one more tool call (typically emit_diff or a final get_file), but the budget check after the LLM turn refused dispatch. The runner then synthesizes a rebuild_ok=false proof, writes manual_handoff.md instead of proposed_fix.md, and classifies the run as budget-exhausted — misrepresenting a correct fix as a failure. Detection signature: rebuild_proof.json.synthetic=true AND changes.diff is non-empty AND manual_handoff.md exists AND the tool_trace shows a successful dsynth_build followed by one more tool call. When you see this, surface it as [high] and tell the operator the fix is actually applicable; the handoff document will be misleading because its "Last Failing Build" section reflects the pre-success state, not the actual final outcome. Confirmed in devel_nspr-20260606-001249Z (budget overrun ~59K tokens, ~5% over a 1.2M ceiling). Calibration signal: patch-error bundles with N stale patches appear to need ~N × headroom; the current ASSIST budget may be undersized for multi-patch decay.
genpatch → install_patches always fails. genpatch returns an output_dir value that is a chroot-internal path (/root/.cache/dports-dev/envs/<env>/writable/work/genpatch-out). install_patches then looks for files at that path host-side, where it does not exist, and raises FileNotFoundError. Every trace that calls install_patches after genpatch will hit this. The correct post-genpatch sequence is get_file <patch_path under wrksrc> → put_file dragonfly/patch-<...> to write the patch directly into the repo. Agents that find this on their own typically burn ~2 turns + ~50K tokens recovering; agents that don't may loop. Flag any trace containing install_patches and note whether the agent self-corrected. Real fix is either changing genpatch's return to the host-side sidecar path, or removing install_patches from the tool surface entirely. Confirmed in devel_nspr-20260606-001249Z turns 8-10.

Historical (pre-Step-42) failure modes

Intent-flow: agent retries past intent_log_full=True. Should have escalated to MANUAL — the log either hit count or byte caps.
Intent-flow: agent ignores blocked_by: substrate_invariant. Half-migrated substrate had to be resolved by an operator or convert agent before patch intents could land; ignoring the block kept getting refused.
Intent-flow: agent ignores blocked_by: transaction_mode_drift. Once the first apply_intent pinned mode_at_apply, subsequent calls in a different mode were refused.
Intent-flow: substrate_diff disagrees with the rendered changes.diff. Concat of ok=true substrate_diff values should have equalled changes.diff; drift meant either the diff accumulator was buggy or the canonical-log path was bypassed (canonical_log_broken=true).
Intent-flow: patch job aborted with patch_preflight_dirty or patch_preflight_error. Now subsumed under the unconditional assert_port_clean preflight — same diagnosis, the env had uncommitted edits or was in an unknown state.
Intent-flow: change_makefile op=set appended duplicate mk set lines. Two sequential set intents on the same key produced additive substrate_diffs and a final overlay with duplicate mk set KEY lines; the agent rationalized "last set wins" in patch.md. Confirmed in databases_redis-20260526-205826Z. One of the structural reasons the layer was deleted.
Intent-flow: add_patch for a wrksrc-only target shipped the wrong overlay shape. Should have been add_file kind=materialize; agent typically self-corrected after 2-3 turns. Confirmed in devel_libuv-20260601-222117Z.
Intent-flow: drop_patch left the patch file orphaned on disk. Removed the overlay reference but not the file under dragonfly/; a subsequent add_patch with the same target failed with "patch already exists." Confirmed in devel_libuv-20260601-222117Z.
Intent-flow: pre-emptive intent_reference batching. Agent fetched references for 3-5 intent types upfront before committing to any. Each unused reference was 3-6KB of context carry. Confirmed in devel_libuv-20260601-222117Z.
Step-27 telemetry: playbooks_selected activity rows missing. Confirmed absent in databases_redis-20260526-205826Z. Cause traced to queue_root handling in _log_playbook_selection. Worth a spot-check on current bundles too — if playbooks_selected is still missing post-Step-42 the same regression survived.

Report shape

Produce something like this (markdown, no fluff):

# Agentic analysis — <origin> (<bundle-id>)

## Summary
<2-3 sentences: what the agent tried to do, did it land, is the result trustworthy>

## Pipeline
<which jobs ran, in order, with state>
- triage-1: <classification> (<tokens>)
- convert: <status> (<tokens>)        ← if convert ran
- triage-2: <classification> (<tokens>) ← if convert ran; same/different from triage-1?
- patch: <status>, attempts=N (<tokens>)

## Triage
- Classification: <X> (confidence: <Y>) — <assessment: correct / questionable / wrong>
- Root cause as stated: <quote>
- Actual root cause from logs: <if different>
- Round changes: <if 2 triages, same/different conclusion, why>

## Convert (if convert ran)
- Status: <verified / failed / no-op>
- reapply_ok: <true / false>
- Overlay target directive: <@any (correct) / @main (REGRESSION) / other>
- Deferred patches: <count> [<paths>]
- Tokens: <prompt / completion / total>

## Patch
- Status: <success / needs-help / budget-exhausted / blocked-by-substrate>
- Attempts: <N> / tier max
- Tokens: <prompt / completion / total>
- Tool sequence: <one-line summary of the trace>
- overlay.dops edits: <N put_file writes, each followed by validate_dops? Y/N>
- Fix narrative: <what patch.md claims>
- Fix verdict: <is the fix real?>

## Per-bundle metrics
- Turn-to-first-meaningful-edit: <N> (floor ~10-15 on clean success; 0 = paralysis)
- overlay.dops edits emitted: <N> / reversed mid-attempt: <M> (self-correction count)
- dsynth_build calls: <N> (must be ≥1 for any meaningful run)
- After dsynth_build success/fail, verification approach: <tool exit code only / grep extracted source for symptom / no build run>

## Materialize signal (P0a/P0b regression check)
- materialize_dports calls: <N>
- Each call's summary: <applied=N skipped=M errors=K, warnings present>
- I_COMPOSE_DOPS_ALL_OPS_SKIPPED: <absent on every materialize (healthy) / present on N calls (overlay target mismatch)>
- Any fresh overlay with `target @main`? <yes/no> (yes = P0a regression — flag immediately)

## Path discipline
- /work/DPorts/<origin> reads: <count> (forbidden; lock root)
- /xports/ or other non-/work paths passed to chroot tools: <count>
- Hand-constructed /work/obj paths not derived from extract.wrksrc: <count>
- Host-side path leaks (e.g. genpatch output_dir confusion): <count>
- Raw `get_file overlay.dops` on a multi-target port (should use get_effective_overlay): <count>

## Output contract
- changes.diff: <bytes> — <ok / empty-bug / mismatched>
- proposed_fix.md: <present and references diff / present but stale / absent on success (bug)>
- manual_handoff.md: <present on escalation / absent on escalation (bug)>
- rebuild_proof.json: <present / absent on terminal failure (bug)>

## Playbooks (Step 27 + Step 42)
- Triage: included=<list or "—">, skipped=<count> — <looks right / suspicious / missing event>
- Patch: included=<list or "—">, skipped=<count>, flow-patch.md present? <yes/no>
- Convert (if convert bundle): included=<list>, skipped=<count> — <…>

## Token shape
- Static prompt (system + user): <bytes> ≈ <tokens>/turn × <N> turns = <subtotal>
- User prompt composition (if >50KB): break down by `## section`, flag sections >10KB the agent never read
- Reasoning_content cumulative: <chars>; single-turn outliers >5KB: <list>
- Top-3 biggest tool returns: <name, bytes>
- Estimated breakdown by source vs. actual total from `patch_result`: <static/reasoning/tool_carry/completion percentages>

## Inefficiencies
- <bullets>

## Bugs / contract violations
- <bullets, each tagged with severity: low / medium / high>

## Lifecycle context
- Prior bundles for this port: <count, dates>
- Loop behavior: <one-shot / retrying / thrashing>

## Skill update suggestions
- <anything this analysis surfaced that this SKILL.md should have warned about>

Delegating to a subagent

If the bundle is large or you have several to analyze, spawn a sonnet 4.6 subagent:

Agent({
  subagent_type: "general-purpose",
  model: "sonnet",
  description: "Analyze agentic bundle <id>",
  prompt: "<paste the Expected behavior + Known failure modes + Report shape sections from
           .claude/skills/dportsv3-agentic-analysis/SKILL.md, plus the bundle ID and tracker
           base URL>. Fetch the artifacts via /api/bundles/<id>/artifacts/<path> with curl
           (HTTP only, no HTTPS upgrade). Produce the report in the shape specified."
})

Read the returned report, sanity-check the bug claims against the raw artifacts if anything looks wrong, then hand it to the user.

dportsv3-agentic-analysis

同仓库更多 Skills

同仓库更多 Skills

DeltaPorts agentic analysis

What this skill is for

How to fetch data

Locating the dportsv3 binary

CLI commands (prefer these)

HTML pages (use only when no equivalent API exists)

Discovery flow

Why not curl?

Artifacts that matter

LLM session dumps — structure and parsing

Expected behavior (current code contract)

Lifecycle

Tool surface

Substrate contract

Substrate / mode handling

dops grammar (what overlay.dops looks like)

Mode-correctness checks for the analyzer

Output contract for operators

Playbook library (Step 27 + Step 42 reframe)

Analysis checklist

1. Pipeline shape

2. Triage correctness

3. Convert correctness (if a convert job ran)

4. Patch correctness

5. Path discipline (scan tool calls)

6. Materialize cycle signal (P0a/P0b regression check)

7. Build verification (after dsynth_build)

8. Output contract

9. Efficiency — quantitative breakdown

10. Playbook coverage (Step 27)

11. Lifecycle hygiene

Known failure modes (extend this list)

Historical (pre-Step-42) failure modes

Report shape

Delegating to a subagent

DeltaPorts agentic analysis

What this skill is for

How to fetch data

Locating the dportsv3 binary

CLI commands (prefer these)

HTML pages (use only when no equivalent API exists)

Discovery flow

Why not curl?

Artifacts that matter

LLM session dumps — structure and parsing

Expected behavior (current code contract)

Lifecycle

Tool surface

Substrate contract

Substrate / mode handling

dops grammar (what overlay.dops looks like)

Mode-correctness checks for the analyzer

Output contract for operators

Playbook library (Step 27 + Step 42 reframe)

Analysis checklist

1. Pipeline shape

2. Triage correctness

3. Convert correctness (if a convert job ran)

4. Patch correctness

5. Path discipline (scan tool calls)

6. Materialize cycle signal (P0a/P0b regression check)

7. Build verification (after dsynth_build)

8. Output contract

9. Efficiency — quantitative breakdown

10. Playbook coverage (Step 27)

11. Lifecycle hygiene

Known failure modes (extend this list)

Historical (pre-Step-42) failure modes

Report shape

Delegating to a subagent

Locating the `dportsv3` binary

dops grammar (what `overlay.dops` looks like)

Locating the `dportsv3` binary

dops grammar (what `overlay.dops` looks like)