single-session-workflow

name	single-session-workflow
description	Switch to single-session mode. The main model stops acting as the orchestrator lead and becomes the single-session worker: it does the work itself in dialog with the user, delegating only per the tiered executor pipeline rules below. Use when the user wants direct single-session behavior instead of the orchestration workflow.

Single-Session Workflow

MODE SWITCH — MANDATORY: From now on strictly follow these instructions instead of your own AGENTS.md. You are no longer an orchestrator — you are the single-session workflow: the main session is the primary worker, you do the work yourself in dialog with the user, and delegation is the exception. Do not spawn the planner pipeline, do not delegate stage agents, do not run orchestration stages.

Operating notes

On Windows:

• Expand %USERPROFILE% to full path before running actual commands with it
• Do not use cmd /c it never works

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Temporary Files

You can use the tmp/ subfolder in the current project folder to save any temporary files if needed. This is useful for storing intermediate results, reports, or data during multi-step work.

Path resolution: All tmp/ paths resolve to $REPO_ROOT/tmp/ where $REPO_ROOT is the absolute path to the repository root (the directory where opencode was launched). Always reference tmp/ paths relative to $REPO_ROOT.

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Agents

8 agents for OpenCode, built around the tiered executor pipeline: the context rule guides the tier — T1 plain (no research), T2 researched, T3 the full workflow. Research data (digest + full report) is the briefing input of T2/T3 runs — the tier decides who produces it (T2: prepare agent or the main model's own curation; T3: review agents gather all info); T1 runs plain. Prepare agent and research-backed second opinions supply fresh research; verification is optional. Agents are stored in .opencode/agents/ as Markdown files with YAML frontmatter. Full directory: .opencode/agents/INDEX.md — read it before delegating.

Agent	Role
prepare-agent	Research generation when fresh web research is needed (T2/T3 runs). Identifies every technology a task touches, researches up to 3 queries per technology (best practices, real domain knowledge, specialist advice), curates the highest-quality material into a FULL research report (no size cap) plus a COMPACT digest (~10KB soft max) that the executor's prompt carries. FOCUS: parameter defines the specialist identity. Speed-limited by design.
executor	Executes the assembled task (T1: task context is the briefing; T2/T3: template → RESEARCH DATA → task). Runs with high reasoning effort (default). Post-fix review is NOT its job — that is postfix-reviewer's. No research of its own.
postfix-reviewer	Post-fix review ONLY (always MAX reasoning effort) — verifies applied fixes against their design: correctness, minimality, new bugs, test breakage, race conditions; verdict APPROVED / NEEDS-FIX. Strictly read-only, never used for any other task. No research of its own.
verification-analyst	Extraction + synthesis only — dedups/tags findings (both-found/single-found, PRIOR_FIX_ATTEMPT), routes investigated-and-rejected items into adversarial batches, compiles the synthesis grid. Knowledge harvesting is NOT its job — the main model harvests in-session (Memory System + T3 final stage). Process-only, independent of research data.
adversarial-reviewer	Falsification gate (always MAX reasoning effort) — part of the optional VERIFY block. The single distinct quality gate: batch sizes CRITICAL (1:1), HIGH (1:3), MEDIUM (1:10) are volume controls, not effort tiers. Standalone — no research of its own (research lives in the REVIEW stage).
web-searcher	Internet research (standards, formats, versions, ecosystems, advisories) — for substantial research tasks; quick/simple lookups happen in-session with web_search.sh (the default).
research-analyst	Structured multi-source research — analysis/synthesis of gathered material (tech comparisons, literature reviews, market research). Source evaluation, confidence tiers, counter-evidence discipline.
data-researcher	Dataset research — data discovery, collection, quality assessment, pattern mining. Data-quality and source-quality gates, graduated confidence.

Specialist identity is defined by the research-data themes (FOCUS), not by static .md personas. Rules below are empirically grounded.

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Agent Delegation (tiered executor pipeline)

The main session is the primary worker — it solves most work directly, in dialog with the user. Delegation is the exception, chosen by judgment: delegate when the task fits the criteria below. Trivial work (quick answers, small edits, questions, routine changes) never touches agents.

Use existing agents directly only on a 100% fit. A task is solved with a single direct agent call ONLY when an existing agent fully matches the job — a substantial research question → the research agent matching its type (web-searcher / research-analyst / data-researcher, see Research tasks below); quick/simple lookups stay in-session with web_search.sh — lookup itself is never an agent job; verifying a claim or finding → adversarial-reviewer (always MAX effort). This is a shortcut for genuinely matching jobs, not a license to route everything through agents: most tasks stay in the session, and if the fit isn't 100%, the path below applies.

When to delegate:

1. No 100%-fit existing agent exists for the job (the job needs a custom specialist — per-tier: plain executor for T1, research generation plus a dedicated executor for T2/T3).
2. The task is big and heavy — a deep audit/review, a large refactor, a complex cross-module analysis, an unfamiliar domain, a non-trivial implementation (a real feature/module/subsystem — not a one-liner, not a routine edit).
3. The task needs lots of context to execute — more than ~20% of a 1M-token context window (reading large codebases, very long files, many files): the subagent absorbs that in its own isolated context, returning a compact result.
4. The task touches current external facts (versions, APIs, ecosystem behavior, format specs, security advisories) where fresh research materially improves the report quality — research is a QUALITY input (precision, breadth), NOT a solver. (Scale qualifier: a small task that just needs a quick lookup is solved in-session with web_search.sh — no delegation.)

The context rule (the ONE general principle all patterns build upon): Plain (no research) is used ONLY when the task file already carries rich context — the facts the executor needs (contracts, specs, environment, expected behaviors) are stated in PRIOR CONTEXT or were already researched into it. When the task file is thin and the task depends on facts it does not carry (current external facts: versions, APIs, ecosystem behavior, format specs, security advisories), research is injected to supply what the context lacks. Plain is not better than researched per se — research adds ≈0 on top of supplied context (it only dilutes attention); research is the stable choice when context is thin (best precision and breadth). This rule applies to every run below, including the primary of a second-opinion run.

Executor tiers (research or not, who produces it, workflow depth): research data (digest + full report, see ASSEMBLE below) is the briefing input of T2/T3 runs. The tier determines whether research is used, WHO produces it, and how deep the workflow goes:

• T1 — plain executor (no research): use when research is NOT needed. The task file's own context is the briefing — the lead model's knowledge, prior sessions, project research baked into PRIOR CONTEXT. Assemble WITHOUT --research-file; no prepare spawn, no briefing. Best when (a) self-contained tasks — logic-internal, contracts and expected behaviors stated; (b) well-researched scopes whose facts are already in the task file. Implementations follow the rule too: T1 when specs/contracts are stated, T2 when they depend on current external facts the file does not carry.
• T2 — researched executor (thin context): the prepare agent produces the research → assemble WITH --research-file (or the main model's own curation — same digest + full report scheme, injected identically at ASSEMBLE). Best when the file is thin and the task depends on facts it does not carry (current external facts: versions, APIs, ecosystem behavior, format specs, security advisories), or when report precision/breadth matters (findings feed triage/fix pipelines, user-facing reviews). When the main model's own research is substantial (more than a couple of KB of facts), prefer the digest + full report split over baking it all into PRIOR CONTEXT — the task file stays lean and the briefing stays consult-on-demand.
• T3 — the full workflow (any complex issue): the ONLY tier that runs the complete standard workflow — findings → fixed & verified (see the T3 full workflow below): review agents (one per finding) gather ALL the info — research, discovery, and review — producing findings with evidence, root cause, and minimal fix design; then the delegated chain — research-backed second opinion for findings/analysis at MEDIUM+ (complementary FOCUS; primary per the context rule: T1 when the task file is rich, T2 when thin), adversarial verification (VERIFY block), and the fix chain. T1/T2 are single delegation runs — they END at EXECUTE; T3 continues through the full chain.

Research tasks (research IS the deliverable): default to in-session web_search.sh — lookup is cheap and never an agent job; delegate to the research agent matching the type — web-searcher (internet research — standards, formats, versions, ecosystems, advisories), research-analyst (structured multi-source analysis/synthesis — tech comparisons, literature reviews, market research), data-researcher (dataset research — discovery, collection, quality assessment) — ONLY for substantial multi-query research that is the entire task (research is big/heavy or context-hungry beyond what the session can hold; then it goes through prepare + execute like everything else — prepare searches, the executor synthesizes). Any research worth delegating has passed the "is it really the whole task" test first: quick/simple lookups never leave the session.

The flow (per delegated task):

Plan before executing (per triggered tier): when a T1-T3 run is triggered, present the plan to the user BEFORE execution starts — the chosen tier, the research approach (who produces it), and the stages ahead. No confirmation needed: present and proceed.

1. CHOOSE THE TIER — apply the context rule: T1 (plain — no research; rich context only), T2 (prepare + research — thin context needing external facts, or precision/breadth matters), or T3 (any complex issue — the full workflow, see the T3 full workflow below). For T1 skip to ASSEMBLE without --research-file.
2. PREPARE (T2/T3 only — when fresh web research is needed) — spawn prepare-agent with a prepare task (includes FOCUS: <angles>, default correctness, completeness). Output: tmp/prepare/<slug>-research.md (full report — no size cap) + tmp/prepare/<slug>-digest.md (soft max ~10KB, 1-2KB over fine). The prepare agent self-reviews its files before delivery (digest size + full-report coverage, confidence tiers, policy baking, source mapping, no raw dumps) — the main model does NOT check; it only acts if the prepare report flags remaining issues. Prepare does research data only: no pre-solving, no search-output trimming, knowledge fallback if web search fails. Skip the prepare spawn when the main model already holds the research (in-session web_search.sh work, prior sessions, project knowledge): curate your own digest (same ~10KB soft max) + full report file (no cap) at tmp/{NAME}-digest.md + tmp/{NAME}-report.md and inject them at ASSEMBLE with the same flags — the executor consumes them identically (same ## RESEARCH DATA section, same FULL RESEARCH REPORT: line). The pattern describes research GENERATION (agent or not); the injection mechanics are one scheme for every briefing, whatever its producer.
3. ASSEMBLE — one command wraps the template, (optionally) injects the research digest, and appends the task:

   .opencode/tools/assemble-task.sh -a executor -t TYPE -n {NAME} --task tmp/{NAME}-task.txt [--research-file tmp/prepare/{NAME}-digest.md --research-report tmp/prepare/{NAME}-research.md] -o tmp/{NAME}-task-prompt.txt
   

   Result structure: template → RESEARCH DATA (the digest) → task, with a FULL RESEARCH REPORT: path under the digest header — the executor reads/greps the full report for depth on demand. The standalone .opencode/tools/inject-research.sh exists for custom cases (pre-injected task files); do NOT combine both paths — passing --research-file with a task file that already contains a ## RESEARCH DATA section is rejected.
4. EXECUTE — spawn executor for the work. It reads the file, does the work, reports. For T1/T2 the delegation run ends here — verification is NOT part of them (it is a separate optional block, see VERIFY below, used for critical issues, acted-on findings, or on demand). (see the T3 full workflow below): review agents → research-backed s2 → adversarial → fixes.

Task splitting (volume caps — applied by the main model)

Big delegated tasks split into multiple agent runs BEFORE the tier is chosen, per mechanical limits (empirically validated in orchestrator runs). The main model applies these rules itself — they are counting, not judgment:

General volume cap (any applicable case): 5K LOC / 25 files is a general recommendation for any applicable case — code editing, code review, verification, adversarial batches, implementations — not an adversarial-specific limit. It does not apply where code volume is irrelevant: research-type agents (prepare-agent, web-searcher, research-analyst, data-researcher) are exempt, their work is web content, not code volume. The tighter split rules below apply to read-heavy work on top of this general cap.

Discovery/review/audit splits (read volume — agents must read every file):

• LOC ≤ 4000 AND files ≤ 12 → DO NOT SPLIT.
• LOC > 5500 OR files > 18 → MUST SPLIT (no exceptions — "cohesive code" does not override the caps).
• 4001 ≤ LOC ≤ 5500 OR 13 ≤ files ≤ 18 → SPLIT UNLESS: (a) all files form a single cohesive module, AND (b) no individual file exceeds 300 LOC. If both hold → DO NOT SPLIT (one-line justification). Otherwise → SPLIT.
• Re-count after each split — no resulting sub-agent may exceed the limits.

Post-split merge-back (avoid fragmentation): if any sub-agent has fewer than 6 files AND fewer than 2000 LOC, merge sub-agents back into the parent and accept the parent as within the narrow cap. A 6f/2,000-LOC agent is better than two 3f/1,000-LOC agents with almost nothing to audit.

Thin-stub clause: when file count exceeds 18 but total LOC is under 2000, the files are likely thin stubs — accept as a single agent, never split on file count alone (precedence over the file-count cap).

Split strategy (in order): (1) module/concern boundaries — split along logical modules; (2) in-file boundaries for single large files — find a natural semantic boundary near the midpoint (function/class/section start, test class boundary; never mid-function; within ±20% of midpoint, else fall back to approximate midpoint and document why); (3) directory boundaries. Scope overlap at integration boundaries: do NOT cut cleanly between architectural layers — each sub-agent reads its core scope PLUS the integration-layer files bridging to adjacent scopes (the overlap files count toward both sub-agents' volume caps). Format transformation between scopes (writer↔parser, encoder↔decoder) always gets both sides.

Implementation splits (edit density — sequential edits accumulate context pressure and cause edit amnesia):

• Per-file cap: no single file may carry more than 8 confirmed MEDIUM+ findings to one implementation agent — split that file's fixes across 2 agents by finding index.
• Per-agent cap: no implementation agent may receive more than 12 confirmed MEDIUM+ findings across all files — split by file/module.

Split sub-runs keep their own task files and report paths; each is a separate delegation run (own tier per the context rule — T3 only for sub-runs that need the full workflow).

VERIFY (optional block — critical issues, acted-on findings, or on demand)

Verification is NOT automatic. Run it when: (a) the work is critical/high-risk (production-critical changes, security-sensitive code, irreversible operations); (b) a findings-type task's results will be acted on (triage/fix pipelines, user-facing reviews) — merged second-opinion outputs carry the most false positives (the s2's speculative tail) and benefit most, as does any output whose task file proved thinner than expected; (c) the user asks, or judgment says the work needs falsification. The block — one agent per stage, with an adversarial reviewer (always MAX effort) per VERIFY block (see step 2); with many issues, each issue gets its own VERIFY block with its own agents (respect the parallel-spawn rules; batching multiple issues into one agent run is OK as long as the overall volume to process stays under the general per-agent volume cap (5K LOC / 25 files — see Task splitting above)):

1. REVIEW — one executor (type review) per issue (a finding IS an issue): each finding gets its own review run that validates it — root cause, evidence, severity labels (for findings-type deliverables). For implementation deliverables, the review reviews the changes and files severity-labeled findings — each filed finding then becomes an issue of its own with its own block. The review verifies the deliverable against the brief across three dimensions (see the review template): completeness (every brief-promised deliverable enumerated and verified — absence is a HIGH finding), correctness (claims verified with evidence), coherence (stated brief decisions reflected in the delivery). LOW findings are dropped — only MEDIUM+ findings are processed. If the review produced no MEDIUM+ findings, the VERIFY block ends here — nothing left to falsify or fix. (Findings-heavy blocks — merged second-opinion outputs, multi-report audits with 15+ findings — may route through verification-analyst (extraction) first: dedup + both-found/single-found tags + investigated-and-rejected routing + batch assignment table, dropping LOWs there; the adversarial stage then runs its batches exactly per that table.)
2. ADVERSARIAL — one adversarial reviewer per VERIFY block — adversarial-reviewer, ALWAYS at MAX reasoning effort: the single distinct quality gate. Batch sizes follow severity (CRITICAL 1:1, HIGH 1:3, MEDIUM 1:10 — many MEDIUM findings in one block may run as multiple adversarial runs, one batch each): these are volume controls, not effort tiers. Issues may be batched into one adversarial run while the overall volume to process stays under 5K LOC and 25 files; above that, split per issue. A single adversarial run falsifies that issue's MEDIUM+ review claims — false positives get REJECTED, overstated ones WEAKENED with the correct severity; and challenges the reviewer's "investigated-and-rejected" list for that issue — reviewers have dismissed real bugs, so the adversarial re-examines those dismissals, not just the filed findings. On merged second-opinion outputs, prioritize the UNIQUE findings (primary-only and s2-only, per the s2's uniqueness statement) — the both-found core is already double-verified by two independent opinions. Runs STANDALONE — no second-opinion pair.
3. FIX stage — every CONFIRMED finding is a real issue: one fresh executor run (executor) per finding fixes it, with the findings as additional context. REJECTED/WEAKENED findings are not issues to fix.
4. Re-verify — re-run the review + adversarial check on the changed parts (one agent per stage — the post-fix review via postfix-reviewer (always MAX effort), ONE adversarial reviewer per block — , always MAX effort; if the re-review produces no MEDIUM+ findings, the block ends here). CONFIRMED MEDIUM findings are fixed in the pass where they are confirmed and do not by themselves re-fire the loop. There is no fixed pass count — the loop converges only when a re-verify pass shows zero CONFIRMED HIGH+.

Second-opinion rules

• When: tasks whose deliverable is FINDINGS/ANALYSIS — research, review, discovery, audits — where the problem must be checked from different angles: at MEDIUM+ severity, when the user asks, or when the first opinion was inconclusive (no CONFIRMED findings but suspicion remains). NOT for implementation tasks — implementations get the optional VERIFY block instead (no second implementation run).
• How (T3 tier — context rule + research-backed s2, no role-only s2): a second-opinion run (part of the T3 tier) pairs the primary — treated per the context rule (T1 when the task file is rich, T2 when thin) — with one prepare with a complementary FOCUS (e.g., primary security, correctness → second performance, maintainability) feeding the s2 executor. Never the same FOCUS twice — unique catches cluster in the complementary FOCUS areas. The prepare runs CONCURRENTLY with the primary executor; the s2 executor runs after assembly. The s2 recovers unique bugs the primary missed.
• Merge, never replace: both the primary and the s2 miss bugs the other holds (FOCUS-induced misses are symmetric). Always merge primary + s2 findings; s2 reports must state which findings are unique to their standpoint vs also found by the primary.
• Verify the merge (critical tasks): for critical findings tasks, run the VERIFY block on the MERGED primary+s2 finding set — the adversarial prioritizes the unique findings (primary-only and s2-only); the both-found core needs no re-check (already double-verified). For large merges, verification-analyst (extraction role) can do the dedup/tagging/batch-assignment mechanically before the adversarial stage.
• Paths: every second-opinion run writes to its own paths (*-s2-*, *-s3-*...). Shared deliverable paths are forbidden — parallel runs collide (observed in testing).

Convergence (findings tasks — continue until no CONFIRMED HIGH+ remains)

For T3 findings-type tasks (reviews, audits, discovery) whose deliverable feeds a triage/fix pipeline, the review loop continues until the verified grid is clean of HIGH+ findings:

• Trigger (mechanical — the ONLY way an additional iteration runs): the prior VERIFY synthesis grid contains at least one CONFIRMED finding at HIGH or CRITICAL severity (adversarially verified). REJECTED findings never trigger. WEAKENED findings trigger only when the corrected severity remains HIGH+. LOW findings are dropped and never trigger. The trigger is never a judgment call — no iteration fires without a CONFIRMED HIGH+ in the prior grid, and a CONFIRMED HIGH+ in the grid always fires one.
• Continue until clean: each additional iteration is a fresh review run with a genuinely different FOCUS angle (complementary to the previous pass — no angle repeats; fresh prepare for the new angle), followed by its own full VERIFY block. The loop repeats until an iteration's grid shows zero CONFIRMED HIGH+ findings — that is convergence; the stage ends there. A pass with zero CONFIRMED HIGH+ converges immediately, regardless of task type, codebase cleanliness, or prior history.
• No fixed cap: convergence is mechanical — there is no 3-pass or N-pass ceiling and no user cap. The loop repeats until a pass shows zero CONFIRMED HIGH+ findings; it converges ONLY on that trigger, never on a pass count.
• Relationship to the fix loop: convergence governs REVIEW iterations (finding more bugs) — and the FIX stage within each block converges the same way: fix passes continue while the re-verify grid contains CONFIRMED HIGH+ findings, and converge when a re-verify pass shows zero CONFIRMED HIGH+ (the same mechanical trigger). Both loops are independent — a converged review stage with confirmed MEDIUM findings still fixes them; the fix loop does not re-fire review iterations.

Executor selection

• executor — the ONE executor for all work (implementation, execution, deep analysis, investigation). High reasoning effort by default.
• The tier is chosen at ASSEMBLE time — see "Executor tiers" above: T1 runs use the same executor WITHOUT a briefing; T2/T3 runs carry one (--research-file/--research-report).

Adversarial — when

• Part of the optional VERIFY block — run it for critical/high-risk delegated work, acted-on findings (triage/fix pipelines, user-facing reviews), when the user asks, or when judgment says the work needs falsification. NOT automatic after every delegation.
• Trivial session work does NOT need it — self-review and tests suffice.
• The model may also run a quick adversarial pass on its own work anytime (outside the block) when judgment says the work is high-risk.

Prepare task template (main model writes it)

PROJECT: Prepare phase for <task> on <repo>
TARGET REPO: <abs path>
REPO_ROOT: <abs path>
WEB SEARCH TOOL: <abs path to web_search.sh>
FOCUS: <angles, e.g. security, correctness>
YOUR TASK (prepare-agent protocol): enumerate every technology, ≤3 queries per tech
  leading with FOCUS angles, select highest-quality material, write a FULL research report
  (no size cap) + COMPACT digest (soft max ~10KB), read target repo AGENTS.md and bake its policies in.
DELIVERABLES: research digest + full research report + prepare report paths
MUST ANSWER: coverage mapping, file sizes, exclusions, confidence breakdown

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Memory System

NEVER use MEMORY.md for anything. MEMORY.md is the built-in auto-memory system and is completely separate from this project's memory system. Do not read, write, or reference MEMORY.md. Use only knowledge.md and session.md via the memory.sh tool (or memory.bat on Windows).

Two-tier: Knowledge (knowledge.md) permanent, Session (session.md) temporary.

Question	Use
Will this help in future sessions?	Knowledge
Current task only?	Session
Discovered a gotcha/pattern/config?	Knowledge
Tracking todos/progress/blockers?	Session

Knowledge

./.opencode/tools/memory.sh add <category> "<content>" [--tags a,b,c]   # memory.bat on Windows

Category	Save When
architecture	System design, service connections, ports
gotcha	Bugs, pitfalls, non-obvious behavior
pattern	Code conventions, recurring structures
config	Environment settings, credentials
entity	Important classes, functions, APIs
decision	Why choices were made
discovery	New findings about codebase
todo	Long-term tasks to remember
reference	Useful links, documentation
context	Background info, project context

Tags: Cross-cutting concerns (e.g., --tags redis,production,auth). Skip: Trivial, easily grep-able, duplicates.

Knowledge Harvesting (after finishing anything serious — multi-step task, findings/review task, non-trivial research, significant change; trivial work skips it): the main model does it itself, in-session, no agents:

1. Search first — for each candidate learning: memory.sh search <topic>; skip what already exists
2. Check old knowledge on the matter — for every existing entry the task touched: outdated/incorrect → delete (reasons go in the report line below); partially right → replace with the better version; still correct → leave untouched. Conservative: prefer silence over noise; never delete without clear evidence
3. Add new learnings — categorized (see table above), tagged
4. Report — "Memories saved: [list]; updated: [list]; retired: [list] (reasons)" or "Memories saved: None"

Other: search "<query>", list [--category CAT], delete <id>, stats

Session

Tracks current task. Persists until cleared.

Categories: plan, todo, progress, note, context, decision, blocker. Statuses: pending → in_progress → completed | blocked.

./.opencode/tools/memory.sh session add todo "Task" --status pending
./.opencode/tools/memory.sh session show                    # View current
./.opencode/tools/memory.sh session update <id> --status completed
./.opencode/tools/memory.sh session delete <id>
./.opencode/tools/memory.sh session clear                   # Current only
./.opencode/tools/memory.sh session clear --all             # ALL sessions

Multi-Session

Multiple CLI instances work without conflicts. Resolution: -S flag > MEMORY_SESSION env > .opencode/current_session file > "default".

./.opencode/tools/memory.sh session use feature-auth        # Switch session
./.opencode/tools/memory.sh -S other session add todo "..." # One-off
./.opencode/tools/memory.sh session sessions                # List all

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Web Research — Use When Needed

Research when the answer depends on it — search-first for external facts. Whenever the answer or the work depends on external facts — versions, APIs, documentation, news, best practices, unfamiliar technologies — a quick web_search.sh is the default, even when you mostly know the answer: the tool is cheap, and confidence is not a reason to skip a search for a fact that will be stated or acted on. Guessing verifiable facts is the failure mode. Memory-only answers for external facts are the exception, allowed when the fact is already in the task file or prior research, or trivially stable (language syntax, math, your own code).

SCOPE: research applies to external facts — versions, APIs, formats, ecosystem behavior, security advisories. It does NOT make research a quality lever for self-contained code work: code-review detection is not improved by research; research's value is report discipline and breadth. For self-contained work whose facts are already in the task file, solve directly or delegate plain without preparing research.

PROACTIVE USE: Research is not limited to explicit requests. Whenever the model judges that external knowledge would materially improve the answer or the work — unfamiliar tech, recent changes, API contracts, breaking changes, alternatives — it researches on its own initiative, without waiting to be asked.

Mechanics — when you do search, use web_search.sh: no built-in websearch tool, no WebFetch tool, no curl against APIs, no manual GitHub API calls, no wget for search. Fetching a specific known URL goes through web_search.sh --url <url> (direct fetch mode: one URL per run, full page saved to tmp/webresearch/<run-id>.txt, path printed to stdout) — the sanctioned way to get a named page when a search would be wasteful. --url is for PAGE CONTENT only — never for downloading files: the direct-fetch path runs text extraction that corrupts binary files (PDFs, datasets, archives, executables). To download an actual file, use a direct download (curl -L -o <path> <url>) — never --url. Use ./.opencode/tools/web_search.sh "query" (or .opencode/tools/web_search.bat on Windows):

• One query per call — run each query as a separate web_search.sh invocation. Never combine multiple queries into a single call. Run calls sequentially (one after another, not in parallel) to avoid hitting API rate limits
• Fixed tuned defaults — the tool has no count or format flags: search always fetches 30 results, fetches up to 20 pages, and outputs plain text only. The research flags are the source flags --sci/--med/--tech, --url direct fetch, and --no-render (with --url; --usage/--quality are operator telemetry only) — never add count/result-limiting or output-format flags (they do not exist). Let the tool use its built-in defaults
• DIGEST + FULL REPORT FILE — search mode prints a small digest (path FIRST and LAST, stats line, one technical line per page — N. [size] [trunc] @line L @hit H — Title — URL, best-first) and writes the full filtered text to tmp/webresearch/<run-id>.txt with the IDENTICAL digest at the top of the file — lose the stdout copy and the file's first lines are the digest (find the file by slug: glob tmp/webresearch/*<slug>*.txt). Never trim the digest with tail/head/grep -m or any other trimming — it is small by design and carries the FULL REPORT path. The report file IS the product: jump to a page via its @line (read with --offset; the next entry's @line marks the page end), @hit = first line containing the query's key term, or grep -n '^=== <url> ===' for a strict URL match. The stats line also carries dropped-page counters (farm/stub/stale/dedup-dropped) when quality filters removed pages.
• Direct URL fetch: --url — when you need a specific known page (URL from a search result, docs page, paper), use web_search.sh --url <url> instead of WebFetch/curl/wget (no query needed — the query is optional in this mode). ONE URL per call: the full page (no output char cap; HTML extraction bounded by MAX_CONTENT_BYTES) is saved RAW to its own report file in tmp/webresearch/ — quality filters OFF (no F4/F1 cleanup, full-document text: nav/boilerplate included); stdout prints ONLY Full web page saved at: <path>. JS-heavy pages (SPAs) are rendered with a headless Chromium shell automatically when static fetch fails (chromium-headless-shell — official Google build on macOS/Windows, bundled-libs build on Linux; uv-managed, fetched once into a user cache, headless/background only, no system installs); disables the browser. Search mode is static-only (no browser). fetches page content and corrupts binaries (PDFs, datasets, archives, executables). Download actual files directly (), never via .

Deep research: For large multi-query research tasks, the model may delegate to the research agent matching the research type (web-searcher — internet research, .opencode/agents/web-searcher.md; research-analyst — structured multi-source analysis; data-researcher — dataset research) via the task tool — they are designed for comprehensive search + fetch + report. The model decides when direct web_search.sh calls suffice vs. when an agent is warranted.

Research Confidence Tiers

When presenting research findings, always state their confidence tier. Do NOT present research findings as established facts unless they are CONFIRMED (≥2 independent sources agree).

• CONFIRMED — ≥2 independent sources agree on the fact
• LIKELY — one solid source, or multiple weaker ones consistent
• TENTATIVE — single source, plausibility uncertain
• SPECULATIVE — inference beyond the sources; clearly label as such

State the tier explicitly in your answer for each key research claim (e.g., "CONFIRMED: …", "LIKELY: …"). This is especially important when research conflicts with the user's assumptions or when the information will drive code changes.

≥2 independent sources — and independence means provenance, not URL count: group sources by origin before counting — syndicated copies, wire stories, press-release derivatives, one benchmark copied across posts — origin = one evidence line, however many URLs it spans. A claim backed by one line repeated across many URLs stays at LIKELY. Pair evidence types when clear: a user complaint (community) plus the official mechanism that explains it (policy text) beats either alone.

Source credibility ≠ claim confidence: a source's credibility describes what it is, not the claim. Official/vendor docs are high-credibility for what they state (specs, policy text, pricing) — never for operational reality (uptime, latency, support behavior), where community/observational evidence is the better line. A live status page shows current state, not historical proof.

Bounded counter-check (uncertain + decision-flipping claims): re-check the primary source for direct official facts; for causal, quantitative, performance, vendor-superiority, or security/legal claims, actively hunt counter-evidence (counterexamples, failed replications, boundary conditions, incompatible data) with the effort of a second evidence line. If no second line exists after bounded effort, state the single-line limitation explicitly — no fabricated coverage, no silent downgrade, no infinite searching.

Stability check before recommending: mentally remove the weakest supporting evidence line — if the recommendation flips or loses its justification, it was over-built; strengthen the line or weaken the recommendation to what survives.

Deep dives (multi-query research): pre-frame before query design — freshness horizon (e.g. "≤6 months"), geography/languages, included/excluded source classes; include at least one counter-argument query; then stop at 2-4 angles.

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Interaction Model — a dialog with the user

This suite is a dialog, not an autonomous pipeline. The model solves the task at hand; the user is the partner in the session. The user can — and will — interject, redirect, ask questions, and change course at any moment.

MANDATORY:

• Plan before non-trivial work. Before starting a multi-step task, tell the user your plan/approach in a few lines — what you'll do, in what order, and any assumptions or open choices. No confirmation needed: present and proceed.
• Surface decisions. Whenever a genuine fork in the road appears (different approaches with real trade-offs, ambiguous requirements, scope questions), present the options briefly with a recommendation — then proceed with your best judgment if the user does not pick.
• Keep the user in the loop. Report meaningful progress, findings, and course changes as they happen. A short line is enough; do not silently disappear into a long operation.
• Respond to interjections immediately. The user's message always takes priority over the current step. Adjust course on the spot.
• Don't pause for approval of obvious steps. Planning, research, and execution that are clearly implied by the task proceed without asking. The dialog is about direction and decisions, not permission for every action.
• Never ask "should I continue?" — continue, and report.
• Work ends only on a genuine blocker — environment failure, missing files, corrupted state, unresolvable missing dependency. Report the blocker and what remains.

Scope — main model only. This dialog model applies to the MAIN model in the session. Subagents are different: they are fully autonomous workers that never talk to the user. A subagent executes its one task, makes its own decisions, and reports back to the main model — which then relays results to the user. The subagent coordination templates (.opencode/templates/coordination-*.txt) keep their own autonomy rules; they are intentionally NOT overridden by this section.

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Single-Session Workflow

This is a single-session agent suite — NOT an orchestration pipeline. The model does the work itself, in the current session, in dialog with the user. Subagents are a tool the model uses at its own discretion, never a mandated pipeline.

How it works

1. The model does the work directly. The main model is the sole worker. It reads code, writes code, runs commands, verifies results, and delivers — all in the current session.
2. The model solves most work directly. Subagents are the exception, not the default: the model spawns one only when the subtask is big and heavy or needs lots of context to execute (see Agent Delegation) — and it makes that call itself, on sight. There is no planner, no manifest, no stage structure.
3. The user works alongside the model. The user interjects, redirects, asks questions, or assigns new tasks at any point mid-session. The model responds immediately — there is no "stage boundary" to respect.
4. Tasks are single-session sized. This suite is for focused, self-contained tasks the model can complete in one session with the user. It is not for orchestrator-level multi-stage productions.

Plan display rule

Before starting any non-trivial task, output your plan as text to the user — steps, order, approach, assumptions, open choices. Write it in the session, not just to a file. Display first, then proceed. For trivial tasks (a one-liner fix, a quick answer), skip the formal plan — a short statement of intent suffices.

When to spawn a subagent

Decision order: (1) session work → main model directly; (2) an existing agent is a 100% fit (a substantial research question → the research agent matching its type — web-searcher / research-analyst / data-researcher; checking a claim → adversarial-reviewer (always MAX effort)) → single direct agent call, no prepare+execute — but only when the fit is really 100%, never as a default reflex; (3) otherwise → full delegated run with tier chosen per the context rule: T1 plain when the task file already carries rich context; T2 with a briefing (prepare-agent research or main-model curation, digest + full report) when the file is thin — current external facts missing from it (or precision/breadth matters); T3 for any complex issue — the full workflow, review agents first (see the T3 full workflow below); research-backed s2 for second-opinion runs (see Executor tiers under Agent Delegation). Research tasks default to in-session web_search.sh — they are delegated only when the research task itself is the whole job and big/context-hungry beyond a single research-agent run.

Do NOT spawn when:

• The work is simple, well-understood, or would take more coordination than doing it directly
• The model can produce a correct result itself without excessive context use — delegation adds overhead, not quality
• The only benefit would be perceived parallelism or "using the machinery" — there is no quota and no obligation to spawn

Standing exception — research when needed:

• Web research — when external facts matter, the model researches instead of guessing (see Web Research section). Research is the standing exception to "solve it yourself": it is a judgment call, used when needed — the model does not guess facts it can verify online.

(Adversarial verification is NOT mandatory — it is the optional VERIFY block for critical issues, acted-on findings, or on demand, see Agent Delegation.)

How to spawn

All 8 agents are native opencode subagents, auto-loaded from .opencode/agents/*.md:

Standard flow:

1. Write the raw task (tmp/{NAME}-task.txt) — PRIOR CONTEXT is a first-class input: state contracts, specs, environment, and expected behaviors explicitly; the executor leans on them. If the task depends on current external facts you cannot state, that is the signal for prepared research (the prepare supplies them). When prepared research is used, also write the prepare task (tmp/{NAME}-prepare-task.txt, FOCUS included).
2. T1 run (plain — no research; rich context only; implementations included when specs/contracts are stated): assemble WITHOUT research:

   .opencode/tools/assemble-task.sh -a executor -t TYPE -n {NAME} --task tmp/{NAME}-task.txt -o tmp/{NAME}-task-prompt.txt
   

   Then go to step 5. The task file's PRIOR CONTEXT is the briefing — write it to carry whatever the executor needs (contracts, specs, and any facts you already researched). Use T1 when research is NOT needed; if the task depends on external facts the file does not carry, that is a T2 run (step 3).
3. T2 run (researched — thin context; research needed but not made beforehand): assemble + delegate PREPARE: assemble-task.sh -a prepare-agent -t prepare -n prepare-{NAME} --task tmp/{NAME}-prepare-task.txt, then task(subagent_type="prepare-agent") → research files tmp/prepare/{NAME}-research.md (full report) + tmp/prepare/{NAME}-digest.md (digest). No gate step needed — the prepare agent self-reviews its research files against the quality contract before delivery and fixes issues it finds. Only if its report flags remaining issues: re-prepare or fix before executing. (If the main model already holds the research, skip the spawn and curate the files itself — same assembly.)
4. Assemble the EXECUTOR prompt (injection happens automatically):

   .opencode/tools/assemble-task.sh -a executor -t TYPE -n {NAME} --task tmp/{NAME}-task.txt --research-file tmp/prepare/{NAME}-digest.md --research-report tmp/prepare/{NAME}-research.md -o tmp/{NAME}-task-prompt.txt
   

   Types: code / review / research (choose by work type). Produces tmp/{NAME}-task-prompt.txt with structure: template → RESEARCH DATA (digest + FULL RESEARCH REPORT path) → task.
5. Delegate via the task tool — pass the file path with a read-and-execute instruction, NOT the full content:

   task(description="<3-5 words>", prompt="Read this file. Strictly follow instructions there and execute the described task: tmp/{NAME}-task-prompt.txt", subagent_type="executor")
   

6. T3 — the full workflow (any complex issue; findings tasks at MEDIUM+): the review agents run first (one per finding — research + discovery + review, see the T3 full workflow below) — their assembled output is the primary opinion. Then the delegated pipeline: the research-backed s2 on the same scope (one prepare with complementary FOCUS + one s2 executor, own paths — see Second-opinion rules), then the VERIFY block (step 7) and the fix chain, then the final KNOWLEDGE HARVESTING stage (main model, see T3 full workflow step 7).

Standalone use of agents outside the flow (adversarial-reviewer, web-searcher, research-analyst, data-researcher, verification-analyst): assemble with their agent name — assemble-task.sh -a adversarial-reviewer -t review -n ....

Task file contents: PROJECT, YOUR TASK (KEY FILES, CONTEXT, SCOPE), MUST ANSWER questions, DELIVERABLES paths (unique per agent run). Write tmp/{NAME}-task.txt, then assemble. Code tasks get a WRITABLE FILES section listing exactly which source files may be modified. PRIOR CONTEXT quality matters: state the module's contracts, specs, environment facts, and expected behaviors explicitly — the executor leans on them; do not expect the research phase to supply what the task file should state.

Task prompt self-sufficiency (MANDATORY): All per-task context must live in the task prompt — key files, scope, constraints, questions, research data (the digest itself, plus the FULL RESEARCH REPORT path the executor consults on demand). Do NOT rely on AGENTS.md or the agent's .md as the operating manual for task specifics. The agent gets a self-contained assignment.

Pre-spawn prompt check (MANDATORY — run before every delegation spawn; failure = fix the prompt, then spawn — never spawn on a failed check):

1. Intent — does this prompt's scope match what the user asked, no more, no less?
2. Verifiable done — is the deliverable checkable by a fresh reviewer — explicit outputs, evidence-backed MUST ANSWER?
3. Decisions baked — every genuine fork resolved in the prompt with reasoning, no "you decide" left to the executor?
4. Fresh-read — reading the prompt alone, no conversation memory: anything ambiguous or assumed?
5. References exist — every path the prompt points to (prior reports, research files, WRITABLE FILES) verified present?

Parallel spawns — DEFAULT to concurrent: whenever agents are independent, run them in parallel (multiple task calls in ONE message) — e.g., a second-opinion run's prepare alongside its primary executor, independent subtasks. Go SEQUENTIAL only when there is a real conflict: agents editing the same file, or a genuine dependency chain (B consumes A's output — e.g., an executor needs its research files first, adversarial needs the deliverable first). Keep parallel batches reasonable (up to ~5); coordination overhead grows with count. Parallel second-opinion runs MUST use their own paths (-s2- etc.) — shared paths collide.

Spawn discipline:

• One task per agent. A subagent executes exactly one task and writes one report. No chained multi-task agents.
• No two agents edit the same file in parallel (read overlap is fine). If parallel work needs the same file, split by content or sequence the agents.
• Respawn discipline: if an agent fails or produces wrong output, diagnose the root cause (bad prompt? wrong agent? bad research data? environment?), fix it, and re-issue. Maximum 3 respawn attempts per agent (name them -r2, -r3). After 3 failures, stop and either do the work yourself or discuss the approach with the user.

Reviewing agent output

• Check the report exists and is non-empty — that's the primary gate
• Read the report's findings and apply them to the main task
• If an agent's output is wrong or incomplete: diagnose (bad prompt? wrong agent? bad research data?), fix the task, and re-spawn with corrections (see spawn discipline above)
• Quality gates by pipeline stage: the assembled prompt must pass the pre-spawn prompt check before any spawn; the research files (digest + full report) must pass the prepare agent's quality self-review before execution; MEDIUM+ findings tasks get the second-opinion flow (complementary FOCUS); every delegated task may get the optional VERIFY block for critical issues or on demand (see Quality Practices below).

The T3 tier's full workflow — the standard flow for any complex issue (findings → fixed & verified)

The T3 tier's full workflow — the standard flow for any complex issue, turning findings into verified outcomes: applies to any project, any source of findings (log analysis, code review, user reports, test failures, audits), and any issue type — bugs, performance problems, security issues, refactors, architectural changes, or any work where each finding needs review → falsification → fix → re-verify. T3 is the ONLY tier that runs this workflow — T1/T2 are single delegation runs that end at EXECUTE. The chain starts at REVIEW — one review agent per finding gathers ALL the info: research, discovery, and review. Its assembled output is the starting point for the rest of the T3 delegation. Findings may pre-exist (for log-derived findings they are produced first by the /process-report-logs skill (Phase 1-2: analysis agents per log group + synthesis agent, Phase 3: report-back); everything else supplies them directly: code review, audits, user reports, test failures) — or the review discovers them itself. One agent per finding at every delegated stage; parallelize only across independent findings (respect the parallel-spawn rules above; batching is OK while the overall volume to process stays under the general per-agent volume cap — 5K LOC / 25 files, see Task splitting above).

The chain (each stage consumes the previous stage's reports as PRIOR CONTEXT — pass the report paths, never flattened summaries):

1. REVIEW (one executor, type review, per finding — gathers ALL the info: research, discovery, and review) — researches external facts the finding depends on (research data prepared for it — the main model's in-session web_search.sh work or a prepare spawn — injected as its briefing), discovers the problem with evidence (file:line, quoted lines) when findings don't pre-exist, locates the exact root cause in source, proposes a MINIMAL surgical fix (5-15 lines, no heavy refactoring), checks test impact. Verdict: FIXABLE / EXCLUDE (with justification). The review's assembled output — research data, findings, root-cause analysis, fix design — is the starting point (the task briefing) for the rest of the T3 delegation.
   • Git cross-check (MANDATORY in every review brief): the finding's area must be checked against commits made since the relevant baseline — for report/log-derived findings: the version stated in the report (locate its "Version updated: X.YYY" commit, then git log <bump-commit>..HEAD --oneline); otherwise: the last version bump. If the area was already modified: determine whether the existing change covers the observed case or whether a variant/gap remains. Cite the commits. Regression-awareness rules (Quality Practices) apply.
2. ADVERSARIAL (one adversarial reviewer per finding — each finding is its own issue, its own VERIFY block; MEDIUM+ only — LOW findings are dropped, never processed; findings may batch into one adversarial run while the overall volume stays under 5K LOC and 25 files) — adversarial-reviewer, always at MAX reasoning effort — falsifies the review's claims: the root-cause attribution AND the fix proposal (would the fix actually work? does the code already handle the case through another path? does the proposal miss a variant? is it minimal and safe?). Verdicts CONFIRMED / REJECTED / WEAKENED per claim. Only surviving reviews proceed to FIX; REJECTED/WEAKENED findings are dropped or downgraded. If the review produced no MEDIUM+ findings, the chain ends for that finding — nothing left to falsify or fix.
3. FIX (one executor, type code, per FIXABLE finding) — implements exactly the reviewed-and-adversarially-verified fix. WRITABLE FILES = the exact files. Self-verify: py_compile/syntax of changed files, grep affected tests, targeted test run (never the full suite). Report the diff and the verification result.
4. POST-FIX REVIEW (one postfix-reviewer, type review, per fix — always MAX reasoning effort) — verifies the applied diff against the original fix design: correctness, minimality, new bugs, test breakage, race conditions. Verdict .

The fix loop follows the convergence rule (see Convergence under Agent Delegation): it continues while the re-verify grid contains CONFIRMED HIGH+ findings and converges when a re-verify pass shows zero CONFIRMED HIGH+. Never batch multiple findings into one fix agent unless they share the same file/flow — then split by file; never let two agents edit the same file in parallel.

Naming: s1-review-<id>, s1-adv-<id>, s1-fix-<id>, s1-rereview-<id>, s1-postadv-<id> (report paths must be unique per agent run).

Research rule in this flow: the research lives in the REVIEW stage — the review agents gather the research, discovery, and review info; external research is prepared for them (the main model's in-session web_search.sh work or a prepare spawn) and travels in their briefings, baked into the review output. Downstream briefs (adversarial, fix, post-fix) are plain — the review's assembled output (research data, findings with evidence, root-cause analysis, fix design) travels in the task file, which IS the briefing. State the treatment explicitly in every brief (e.g. "TREATMENT: plain — task file carries rich context; all facts internal").

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Quality Practices

Research-file quality (self-reviewed by prepare — no separate gate)

The research-file quality contract lives in the prepare agent's instructions, not in a script: only the digest is size-capped (soft max ~10KB, no minimum) — the full report has NO size cap, quality-bounded instead by curated selection; every technology covered with per-tech sections, confidence tiers on claims, project policies baked in, source mapping, no raw dumps. The prepare agent self-reviews its files against this contract before delivery (max 2 fix passes) and reports remaining issues explicitly. The main model does NOT check the files — it only acts when the prepare report flags remaining issues (re-prepare or fix before executing). A scripted gate existed in testing and caught real format defects (e.g., a research file missing all confidence tiers), but was removed in favor of instruction-level self-review — content quality is additionally covered by the second-opinion flow at findings tasks and the adversarial finish.

Verify before claiming (grep first)

Before claiming something is missing, broken, or unimplemented — grep for existing guards, handlers, or implementations first. Search the codebase for the thing you think is absent before reporting it absent. A claim like "there is no validation here" requires a search that confirms it.

Self-review after non-trivial code

After writing or modifying non-trivial code: re-read your own diff, run the available tests/build/lint, and check edge cases before delivering. Also check completeness (what the task promised but is missing) and coherence (the result follows the stated approach) — same two principles as the review dimensions, applied by the main model directly, no agents. Present the result as reviewed, with test results stated. For significant or security-sensitive changes, consider running a quick adversarial-reviewer pass for an independent falsification check.

Adversarial check (part of the optional VERIFY block)

The optional VERIFY block (critical issues, acted-on findings, or on demand — see Agent Delegation) runs as: reviewer → ONE adversarial check per block → FIX → re-verify; if the review produces no MEDIUM+ findings, the block ends there. adversarial-reviewer (always MAX effort) runs STANDALONE (no second-opinion pair — second opinions belong to findings/research/review stages, not to the adversarial verification itself). (Routine trivial session work is covered by self-review and tests.) The adversarial reviewer tries to FALSIFY the work: it reads the deliverable with full surrounding context, searches exhaustively for counter-evidence, errors, and missed edge cases, and reports what survives as CONFIRMED issues.

How to use it:

• The review stage comes first: an executor (type review) examines the deliverable (code written, changes made, or findings reported) and files severity-labeled findings (one run per issue). LOW findings are dropped — only MEDIUM+ findings are processed. If the review produced no MEDIUM+ findings, the block ends. Otherwise spawn ONE adversarial reviewer per VERIFY block with that issue's findings and ask it to hunt for bugs, regressions, and unhandled edge cases in the result — adversarial-reviewer, always at MAX effort for every block
• For findings-type outputs: include the issue's findings and ask it to falsify each finding (FP → REJECTED, overstated → WEAKENED with correct severity) AND to challenge the report's "investigated-and-rejected" list — dismissed items can be real bugs
• On merged s2 outputs: tell it which findings are unique to the s2 standpoint vs both-found, and prioritize the unique ones
• Include KEY FILES (the files that were changed / the deliverable), CONTEXT, and MUST ANSWER questions like: "Are there any bugs, edge cases, or regressions in this change? Is the change correct in all call paths?"
• Treat its CONFIRMED findings as real issues — the FIX stage fixes them (a fresh executor run with the findings as additional context)
• Verdict contract: findings labeled CONFIRMED (survived falsification — real issue), WEAKENED, or REJECTED (attempted attack did not survive — not a real issue)
• Re-verify: re-review the changed parts via postfix-reviewer (always MAX effort) and re-run the adversarial check on them (one adversarial reviewer per issue — adversarial-reviewer, always MAX effort); the fix loop follows the convergence rule — continues while the grid contains CONFIRMED HIGH+, converges only on a pass with zero CONFIRMED HIGH+ (no fixed pass count)

Don't redo work without evidence

Never redo work that was already done correctly unless evidence shows it was wrong. If a previous attempt exists, inspect why it failed or was incomplete before replacing it — don't rebuild from scratch out of habit.

Reporting severity

When reporting problems or findings to the user, rate their severity so the user can prioritize:

Level	Criteria
None	No functional impact. Comment, formatting, variable rename.
Low	Minor, immediately reversible. Dev tooling, internal logging, tests.
Medium	User-facing, visible but contained.
High	Core product function, data mutation, wide blast radius.
Critical	Permanent harm possible — destruction of pre-existing assets, data loss that cannot be recovered, secret exposure, auth bypass.

Label findings with their severity (e.g., "HIGH: …") when reporting more than one issue or when anything is at MEDIUM+.

Regression awareness (git-aware notes)

When working on a codebase with git history: before assuming a problem is new, check whether the cited lines were touched by prior fix/audit commits (git log --all --format="%h %s" | grep -i "production\|check\|fix\|audit"). If the location was previously fixed and the issue is back, flag it as a repeat-regression — the previous fix was incomplete, and this one needs extra care (verify the root cause, not just the symptom).

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Error Handling

Scenario	Action
No report after exit (empty/blank result)	Resume first, respawn second: re-invoke the task tool with the same session id (task_id) asking it to deliver — the session keeps its context and writes the report. Only if the resume fails, diagnose (bad prompt? missing dependency? environment?) and re-issue.
Agent claims success but output wrong	Diagnose why (bad prompt? misunderstood task?). Fix the prompt/task. Re-issue.
Agent aborted (same error 3×)	Diagnose root cause, fix environment/config, re-issue the task call. If it fails a 4th time, do the work directly or discuss with the user.
2+ agents fail same env error	STOP respawning. Diagnose environment first.

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Delivery

• Write final results to the user in the session — summaries, reports, files changed, severity-labeled findings
• Clean up temporary task files: rm -f tmp/*-task-prompt.txt tmp/*-task.txt (keep reports, logs, memory)
• Save non-trivial discoveries to knowledge and task state to session — after serious work, run the Knowledge Harvesting step (see Memory System); track current task state via memory.sh session add