ce-compound

name	ce-compound
description	Document a recently solved problem or durable project vocabulary in docs/solutions/ or CONCEPTS.md. Use when capturing a learning after work.
argument-hint	[optional: brief context] [mode:headless]

/ce-compound

Coordinate multiple subagents working in parallel to document a recently solved problem.

Purpose

Captures problem solutions while context is fresh, creating structured documentation in docs/solutions/ with YAML frontmatter for searchability and future reference. Uses parallel subagents.

Why "compound"? Each documented solution compounds your team's knowledge. The first time you solve a problem takes research. Document it, and the next occurrence takes minutes. Knowledge compounds.

Usage

/ce-compound                            # Document the most recent fix
/ce-compound [brief context]            # Provide additional context hint
/ce-compound mode:headless              # Non-interactive run for automations
/ce-compound mode:headless [context]    # Non-interactive run with context hint

One learning per run. The workflow's grounding, overlap detection, and cross-referencing all assume a single solved problem. When a session produced multiple distinct learnings, run the skill once per learning, sequentially — each run grounds fresh against the tree. Do not batch several learnings through one run and stitch cross-references between the drafts afterward; drafting-context numbering ("Learning 3") leaking into written docs is the failure this rule prevents.

CONCEPTS.md bootstrap requests

If invoked specifically to create or bootstrap CONCEPTS.md from scratch rather than to document a solved problem, do not run the normal phases — ce-compound populates CONCEPTS.md only as a side effect of documenting a real learning (it seeds the learning's area, not the whole repo; see Phase 2.4). Repo-wide concept-map creation is ce-compound-refresh's job. Redirect a standalone bootstrap request to ce-compound-refresh (which asks whether to build the concept map or run a refresh cycle), then exit.

Mode Detection

Enter headless mode when either holds: the arguments you were invoked with contain the mode:headless token, or the invocation makes non-interactive intent unmistakable — a caller or standing instruction asking to run ce-compound "headless", "non-interactively", "unattended", or "without prompts/questions". The token is the explicit form; a clear natural-language request for a non-interactive run is equivalent. Bare "automatically" or "auto-run" is not on its own a headless signal — it speaks to invoking the skill, not to suppressing its prompts — so an ambiguous or absent signal defaults to interactive. Tokens starting with mode: are flags, not context — strip mode:headless from arguments before treating the remainder as the brief context hint.

Mode	When	Behavior
Interactive (default)	No headless token or clear non-interactive intent	Auto-pick Full vs Lightweight and report the choice; run session history as an automatic probe (Full only); prompt for Discoverability Check consent; end with a plain summary (no "What's next?" menu)
Headless	mode:headless token present, or the invocation makes non-interactive intent unmistakable	No blocking questions. Run Full mode, including the automatic session-history probe (it never prompts, so it preserves headless's non-interactive contract). If the Discoverability Check finds a gap, report it under Instruction-file edit: gap noted, not applied — never edit instruction files (headless is for skill-to-skill / automation handoffs; amending the repo's operating contract needs interactive consent). Skip Phase 3 specialized reviews. End with a structured terminal report — no "What's next?" menu.

Headless mode is intended for automations and skill-to-skill invocation where no human is present to answer questions. The doc itself is identical to what an interactive Full run would produce — classification work (track, category, overlap) follows the same rules and writes nothing extra into the artifact. Once detected, headless mode applies for the entire run.

Session context

Resolve two values at runtime with the shell tool before Phase 1 session-history filtering. Run each as its own command and treat a non-zero exit as a normal state:

• JJ bookmarks — run jj bookmark list -r @ -T 'name ++ "\n"'. Use any returned bookmark names to filter session history. If none are returned or the command exits non-zero, skip bookmark filtering.
• Workspace root — run jj workspace root. Use it as the session-history workspace filter. If it exits non-zero, fall back to the working directory.

Support Files

These files are the durable contract for the workflow. Read them on-demand at the step that needs them — do not bulk-load at skill start.

• references/schema.yaml — canonical frontmatter fields and enum values (read when validating YAML)
• references/yaml-schema.md — category mapping from problem_type to directory (read when classifying)
• references/concepts-vocabulary.md — CONCEPTS.md format and inclusion rules (read in Phase 2.4 when domain terms surface)
• references/agents/session-historian.md — skill-local synthesis prompt for optional session-history compounding context (read only when the user opts into session history)
• references/grounding-validation.md — grounding-validation protocol: flag adjudication rules and the semantic validator prompt (read in Phase 2.45)
• assets/resolution-template.md — section structure for new docs (read when assembling)
• scripts/session-history/ — session discovery and extraction scripts bundled into this skill so session-history support is fully self-contained
• scripts/validate-frontmatter.py — frontmatter parser-safety validator (run in Phase 2 step 8 via the SKILL_DIR anchor)
• scripts/validate-doc-claims.py — mechanical claims validator: cited paths, commit IDs, relative links, dangling drafting scaffold (run in Phase 2.45 via the SKILL_DIR anchor)

When spawning subagents, pass the relevant file contents into the task prompt so they have the contract without needing cross-skill paths.

Execution Strategy

ce-compound does not ask the user which mode to run or whether to search session history. Both are decisions the agent is better positioned to make: mode depends on context budget the agent can observe, and session-history value is unknowable a priori to either party (the payoff is an unrelated earlier session the current agent was never in), so it is resolved by a cheap probe rather than a question. The only interactive prompt in the whole workflow is the Discoverability Check consent, because that one edits a tracked instruction file.

Mode selection (Full vs Lightweight) — decide it, don't ask it.

• Default to Full: the complete workflow (research, cross-referencing, overlap detection, grounding validation). This is the right choice for essentially every documented learning — its token cost is small next to the engineering work that produced the learning and is dwarfed by the value of a doc that compounds.
• Choose Lightweight (single-pass, no subagents — see Lightweight Mode) ONLY under real context pressure: the session is near its context limit, or the fix is trivial enough that cross-referencing would add nothing. These are conditions the agent can observe and the user cannot, which is exactly why this is not a question.
• State the chosen mode and a one-line reason as the first line of the completion output (e.g., "Ran Full mode." / "Ran Lightweight mode — session context was tight."). If Lightweight was the wrong call for the user's taste, re-running is a rare, cheap correction — cheaper than taxing every run with a prompt.

In headless mode, skip mode selection entirely and run Full Mode, including the automatic session-history probe (Phase 1 step 4) — it is non-interactive by construction. Proceed straight to research.

Session history — an automatic probe in Full mode, never a question. The point of searching prior sessions is that an unrelated earlier session may hold related problem-solving; neither the agent nor the user can know that a priori, so asking is pointless. Instead, Full mode always runs the cheap discovery+metadata probe (Phase 1 step 4) — it runs in parallel with the research subagents, so it is near-free on wall-clock — and escalates to the expensive extraction+synthesis only when the probe surfaces genuinely relevant candidate sessions. Lightweight mode skips session history entirely; headless runs the same automatic probe, since it prompts for nothing and so keeps headless non-interactive. This support exists only inside the compounding workflow; there is no standalone session-history product surface.

---

Full Mode

<critical_requirement> The primary deliverable is ONE file - the final documentation.

Phase 1 subagents write their full structured output to a per-run scratch artifact under <workspace-root>/.tmp/rocketclaw/ce-compound/<run-id>/ and return only a compact confirmation containing the artifact path. The orchestrator Reads those artifacts back in Phase 2 assembly. Resolve <workspace-root> with jj workspace root; outside a JJ workspace, use the current working directory so scratch remains under its local .tmp/. Ensure .tmp/ is ignored in .gitignore before writing it. It does not make the scratch files additional deliverables. Only the orchestrator writes product files — the final solution doc and the maintenance side effects below. Subagents must not touch docs/, project instruction files, or any tracked path. Beyond the Phase 2 solution doc, the orchestrator may also write maintenance side effects — not additional deliverables, and creating one when absent is expected, not a violation of this rule. There are three write-target classes; only the first two are unconditional:

• docs/solutions/... — the primary deliverable (always).
• CONCEPTS.md — create or update in Phase 2.4 (Vocabulary Capture) when a qualifying domain term surfaces (Full mode; every mode that reaches vocabulary capture may refine an existing file).
• A project instruction file (AGENTS.md or CLAUDE.md) — a small edit when the Discoverability Check finds a gap, only in interactive Full mode after consent. Headless and lightweight never apply this edit (they report or tip instead). Callers that hand off headless after an approval gate must not see an unreviewed change to the repo's operating contract.

CONCEPTS.md and the instruction-file edit ensure future agents can discover and ground in the knowledge store; neither makes the documentation any less the single deliverable.

Why the scratch artifact (issue #956): a subagent asked to return a long prose body as its inline response intermittently returns an executive summary instead ("Doc body complete — six sections filled. Returning above."), and the original prose is then unrecoverable from the orchestrator side. Writing to disk first means the full output always survives; the inline confirmation is just a pointer, and the orchestrator falls back to whatever the subagent did return inline only when the artifact is missing. </critical_requirement>

Phase 0.5: Auto Memory Scan

Before launching Phase 1 subagents, check the auto-memory block injected into your system prompt for notes relevant to the problem being documented.

1. Look for a block labeled "user's auto-memory" (Claude Code only) already present in your system prompt context — MEMORY.md's entries are inlined there
2. If the block is absent, empty, or this is a non-Claude-Code platform, skip this step and proceed to Phase 1 unchanged
3. Scan the entries for anything related to the problem being documented -- use semantic judgment, not keyword matching
4. If relevant entries are found, prepare a labeled excerpt block:

## Supplementary notes from auto memory
Treat as additional context, not primary evidence. Conversation history
and codebase findings take priority over these notes.

[relevant entries here]

5. Pass this block as additional context to the Context Analyzer and Solution Extractor task prompts in Phase 1. If any memory notes end up in the final documentation (e.g., as part of the investigation steps or root cause analysis), tag them with "(auto memory [claude])" so their origin is clear to future readers.

If no relevant entries are found, proceed to Phase 1 without passing memory context.

Phase 1: Research

Launch research subagents. Each writes its full output to a per-run scratch artifact and returns only the artifact path to the orchestrator.

Run ID and run dir (before dispatching any subagent): generate a unique run identifier and create the run directory. This scopes every Phase 1 artifact file to the same directory so the orchestrator can Read them back in Phase 2.

WORKSPACE_ROOT="<resolved JJ workspace root, or current working directory outside JJ>";
RUN_ID=$(date +%Y%m%d-%H%M%S)-$$
mkdir -p "$WORKSPACE_ROOT/.tmp/rocketclaw/ce-compound/$RUN_ID"

Resolve the agnostic project orientation from the shared cache (before dispatching subagents). The question-agnostic orientation the Context Analyzer and Related Docs Finder rely on — the project's CONCEPTS.md vocabulary and the root instruction-file conventions/digests — is identical for every run at this JJ revision, so reuse it instead of re-deriving. Set SKILL_DIR to this skill's directory and run the helper (full protocol in references/repo-profile-cache.md):

SKILL_DIR="<absolute path of the directory containing the SKILL.md you just read>";
python3 "$SKILL_DIR/scripts/repo-profile-cache.py" get

• On HIT: load the profile JSON and use its vocabulary (CONCEPTS canonical terms) and conventions (root instruction/convention digests) as the agnostic orientation; do not re-derive them.
• On MISS: dispatch a generic subagent seeded with references/agents/repo-profiler.md to derive the profile, write its JSON to a file, then persist with python3 "$SKILL_DIR/scripts/repo-profile-cache.py" put <file> (re-set SKILL_DIR in that call — shell vars don't persist between Bash invocations).
• On NO-CACHE (no JJ workspace or no writable cache): derive the orientation inline this run and skip put.

The cache is an optimization, never a correctness dependency — if the helper errors or returns nothing usable, fall back to deriving the orientation inline and continue. Pass the resolved vocabulary/conventions into the Context Analyzer (for vocabulary and instruction-file convention grounding) so it does not re-derive them.

CRITICAL — the docs/solutions/ enumeration is NEVER cached; the Related Docs Finder must glob it FRESH every run. ce-compound writes new learnings into docs/solutions/, so a cached index would miss a doc added moments ago (even an uncommitted one). The cached profile supplies only the agnostic orientation above; the docs/solutions/ search in step 3 always runs against the live tree.

Pass {run_id} (the resolved $RUN_ID value) and {workspace_root} into every Phase 1 subagent prompt. Each subagent writes its full structured output to its own file under {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/, confirms the write succeeded (the file exists and is non-empty), and then returns only a one-line confirmation containing the artifact path — not the prose body inline. Artifact filenames by subagent:

• Context Analyzer → {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/context.json (frontmatter skeleton, category path, filename, track)
• Solution Extractor → {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/solution.md (the full doc-body prose sections)
• Related Docs Finder → {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/related.json (links, refresh candidates, overlap assessment)
• Session History synthesis subagent (when run) → {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/session-history.md (prose findings)

Return the full output inline whenever the artifact write did not succeed. This covers both cases where the orchestrator's Phase 2 inline fallback would otherwise have nothing to read: (a) {run_id} is empty or did not resolve (non-Claude-Code platforms where the pre-resolution failed), so there is no path to write to; and (b) {run_id} resolved but the write itself failed — tool permission denied, absolute-path writes unavailable, disk error, or the post-write existence check came back empty. In either case the subagent must return its complete structured output inline instead of a path, because the path would point at a file that does not exist. Return only the bare path when — and only when — the write is confirmed on disk. The artifact pattern is a reliability improvement, not a hard requirement; the orchestrator handles a missing artifact in Phase 2 by using the inline return.

Dispatch order:

• Launch Context Analyzer, Solution Extractor, and Related Docs Finder in parallel (background)
• Then run the internal session-history discovery/extraction/synthesis flow (see step 4 below) in Full mode, including headless — skipped only in lightweight. Its cheap discovery+metadata probe always runs; it escalates to extraction+synthesis only on a relevance hit (see step 4's Escalation gate). This flow is synchronous from this orchestrator's main-context turn, but the already-dispatched background subagents continue running in parallel underneath, so the wall-clock benefit is preserved (max(session-history, slowest background subagent), not their sum). Running session history before the parallel block would serialize it in front of the research subagents and regress wall-clock time.

<parallel_tasks>

1. Context Analyzer

• Extracts conversation history
• Reads references/schema.yaml for enum validation and track classification
• Determines the track (bug or knowledge) from the problem_type
• Identifies problem type, component, and track-appropriate fields:
  • Bug track: symptoms, root_cause, resolution_type
  • Knowledge track: applies_when (symptoms/root_cause/resolution_type optional)
• Incorporates auto memory excerpts (if provided by the orchestrator) as supplementary evidence
• Reads references/yaml-schema.md for category mapping into docs/solutions/
• Suggests a filename using the pattern [sanitized-problem-slug].md — no date suffix, even if existing files in the target directory have one; the date: frontmatter field is the canonical creation date
• Writes to context.json: YAML frontmatter skeleton (must include category: field mapped from problem_type), category directory path, suggested filename, and which track applies. Returns only the artifact path.
• Does not invent enum values, categories, or frontmatter fields from memory; reads the schema and mapping files above
• Does not force bug-track fields onto knowledge-track learnings or vice versa

2. Solution Extractor

• Reads references/schema.yaml for track classification (bug vs knowledge)
• Adapts output structure based on the problem_type track
• Writes the full doc-body prose (all track-appropriate sections below) to solution.md and returns only the artifact path. This is the subagent most prone to the issue #956 summary-collapse, so its prose must land on disk rather than only in the inline return.
• Incorporates auto memory excerpts (if provided by the orchestrator) as supplementary evidence -- conversation history and the verified fix take priority; if memory notes contradict the conversation, note the contradiction as cautionary context
• Grounds code-behavior claims in source, not conversation memory. Before asserting how code behaves (enum values, status semantics, limits, defaults), Read the defining line in the current working copy and cite file:line alongside the claim. A claim that cannot be verified there is softened or attributed ("per this session's conclusion…"), never stated as fact
• Writes merge-state claims for time. Cite PR numbers rather than bare commit IDs — commit IDs can change when revisions are rewritten or a forge creates a different landed revision, and may not exist in other workspaces. Verify a "fixed in X" claim against GitHub with gh pr view; otherwise phrase it as pending ("fix opened in #1608, unmerged as of this writing")

Bug track output sections:

• Problem: 1-2 sentence description of the issue
• Symptoms: Observable symptoms (error messages, behavior)
• What Didn't Work: Failed investigation attempts and why they failed
• Solution: The actual fix with code examples (before/after when applicable)
• Why This Works: Root cause explanation and why the solution addresses it
• Prevention: Strategies to avoid recurrence, best practices, and test cases. Include concrete code examples where applicable (e.g., gem configurations, test assertions, linting rules)

Knowledge track output sections:

• Context: What situation, gap, or friction prompted this guidance
• Guidance: The practice, pattern, or recommendation with code examples when useful
• Why This Matters: Rationale and impact of following or not following this guidance
• When to Apply: Conditions or situations where this applies
• Examples: Concrete before/after or usage examples showing the practice in action

3. Related Docs Finder

• Searches docs/solutions/ for related documentation
• Identifies cross-references and links
• Finds related GitHub issues
• Flags any related learning or pattern docs that may now be stale, contradicted, or overly broad
• Assesses overlap with the new doc being created across five dimensions: problem statement, root cause, solution approach, referenced files, and prevention rules. Score as:
  • High: 4-5 dimensions match — essentially the same problem solved again
  • Moderate: 2-3 dimensions match — same area but different angle or solution
  • Low: 0-1 dimensions match — related but distinct
• Writes to related.json: Links, relationships, refresh candidates, and overlap assessment (score + which dimensions matched). Returns only the artifact path.

Search strategy (grep-first filtering for efficiency):

1. Extract keywords from the problem context: module names, technical terms, error messages, component types
2. If the problem category is clear, narrow search to the matching docs/solutions/<category>/ directory
3. Use the native content-search tool (e.g., Grep in Claude Code) to pre-filter candidate files BEFORE reading any content. Run multiple searches in parallel, case-insensitive, targeting frontmatter fields. These are template patterns -- substitute actual keywords:
   • title:.*<keyword>
   • tags:.*(<keyword1>|<keyword2>)
   • module:.*<module name>
   • component:.*<component>
4. If search returns >25 candidates, re-run with more specific patterns. If <3, broaden to full content search
5. Read only frontmatter (first 30 lines) of candidate files to score relevance
6. Fully read only strong/moderate matches
7. Return distilled links and relationships, not raw file contents

GitHub issue search:

Prefer the gh CLI for searching related issues: gh issue list --search "<keywords>" --state all --limit 5. If gh is not installed, fall back to the GitHub MCP tools (e.g., unblocked data_retrieval) if available. If neither is available, skip GitHub issue search and note it was skipped in the output.

</parallel_tasks>

4. Session History (internal flow after launching the parallel block — automatic in Full mode, including headless)

• Skip entirely in lightweight mode. In Full mode (including headless) it always runs as a two-stage probe: the cheap discovery+metadata pass (below) always executes, and the expensive extraction+synthesis executes only when the probe clears the relevance gate (see Escalation gate below).
• Run session discovery, bookmark/keyword filtering, scan-window selection, deep-dive selection, and per-session extraction directly inside this skill using scripts/session-history/.
• Read the skill-local synthesis prompt at references/agents/session-historian.md, then dispatch a generic subagent using that prompt content. Do not dispatch a standalone agent by type/name.

Session-history payload — keep tight. A long, keyword-rich payload licenses widening. Use this shape:

• Session context (only if values resolved cleanly above; otherwise omit): workspace name, current JJ bookmark(s).

• Time window: explicit 7 days unless the documented problem clearly spans a longer arc.

• Problem topic: one sentence naming the concrete issue — error message, module name, what broke and how it was fixed. Not a paragraph; not a bullet list of related topics.

• Filter rule (one line): "Only surface findings directly relevant to this specific problem. Ignore unrelated work from the same sessions or bookmarks."

• Output schema:

  Structure your response with these sections (omit any with no findings):
  - What was tried before
  - What didn't work
  - Key decisions
  - Related context
  

Do not append additional context blocks, exclusion lists, or topic-keyword bullets — verbose payloads give the session-history flow license to keep widening the search and rapidly compound wall time. If keyword search is needed, the internal flow owns that decision based on the topic.

• Returns: structured digest of findings from prior sessions, or "no relevant prior sessions" if none found.
• Session history is the final Phase 1 input, not a workflow stop. When it returns, proceed directly to Phase 2 with its output as the last input — do not emit a summary and do not pause for the user. A "no relevant prior sessions" return is still a valid input; the documentation gets written without session context.

Script resolution. Set SKILL_DIR to the absolute path of the directory containing the SKILL.md you just read, and run the bundled scripts from "$SKILL_DIR/scripts/session-history/". Set SKILL_DIR inline in each bash block below (shell state does not persist between commands). If the bundled scripts are genuinely not present on disk under "$SKILL_DIR/scripts/session-history/", skip session history visibly with: "Session history bundled scripts were not found in this skill's directory; skipping the session-history probe for this run." Continue Phase 2 without session context.

Discovery pipeline. Infer the scan window from the problem topic, starting with 7 days. Run discovery and metadata extraction:

SKILL_DIR="<absolute path of the directory containing the SKILL.md you just read>";
if [ -f "$SKILL_DIR/scripts/session-history/discover-sessions.sh" ] && [ -f "$SKILL_DIR/scripts/session-history/extract-metadata.py" ]; then WORKSPACE_ROOT=$(jj workspace root 2>/dev/null || pwd); WORKSPACE_NAME=$(basename "$WORKSPACE_ROOT"); SCAN_DAYS="7"; bash "$SKILL_DIR/scripts/session-history/discover-sessions.sh" "$WORKSPACE_NAME" "$SCAN_DAYS" --cwd "$WORKSPACE_ROOT" | tr '\n' '\0' | xargs -0 python3 "$SKILL_DIR/scripts/session-history/extract-metadata.py" --cwd-filter "$WORKSPACE_ROOT"; else echo "Session history bundled scripts were not found in this skill's directory; skipping the session-history probe for this run."; fi

Pi sessions are included when present under ~/.pi/agent/sessions/; they carry cwd like Codex but no JJ bookmark. If _meta.files_processed is 0, return no relevant prior sessions. Claude Code's transcript field is named gitBranch; treat its value only as legacy bookmark metadata. If the first pass finds no relevant bookmark matches, or if processing Codex or Pi sessions, derive 2-4 keywords from the topic and re-run metadata extraction with --keyword K1,K2,.... Keep at most 5 sessions across Claude Code, Codex, Cursor, and Pi, ranked by bookmark match, keyword match count, file size over 30KB, and recency. Exclude the current session.

Escalation gate. The discovery+metadata pass above is the cheap probe and always runs in Full mode. Escalate to the extraction and synthesis stages below only when at least one retained candidate clears the relevance bar: a current-bookmark match, or ≥2 topic-keyword matches. If no candidate clears the bar (including the _meta.files_processed is 0 case), stop here, record no relevant prior sessions as the session-history input, and skip extraction and synthesis. This gate is what keeps the always-on probe cheap — the expensive synthesis is paid for only when a prior session is genuinely relevant.

Extraction pipeline. Set SCRATCH="$WORKSPACE_ROOT/.tmp/rocketclaw/ce-compound/$RUN_ID/sessions" and create it with mkdir -p "$SCRATCH". For each selected session, write extracted content to scratch files:

SKILL_DIR="<absolute path of the directory containing the SKILL.md you just read>";
if [ -f "$SKILL_DIR/scripts/session-history/extract-skeleton.py" ]; then python3 "$SKILL_DIR/scripts/session-history/extract-skeleton.py" --output "$SCRATCH/<session-id>.skeleton.txt" < <session-file>; else echo "Session history bundled scripts were not found in this skill's directory; skipping the session-history probe for this run."; fi

Use extract-errors.py selectively when dead ends or recurring errors are likely useful. Pass only the scratch file paths and metadata to the synthesis subagent.

Synthesis dispatch. Build a generic subagent prompt containing:

• the full content of references/agents/session-historian.md
• problem_topic
• scratch_dir
• a sessions array with extracted file paths and metadata
• the output schema above
• the filter rule above

The subagent reads only the scratch paths, writes its prose findings to {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/session-history.md, and returns only that artifact path once the write is confirmed (same #956 reliability rationale — session-history findings are long-form prose prone to summary-collapse). If {run_id} did not resolve or the artifact write failed, it returns the prose inline instead (per the inline-fallback rule above). If synthesis fails, note the failure and continue without session context.

Phase 2: Assembly & Write

<sequential_tasks>

WAIT for all Phase 1 inputs to complete before proceeding — the three parallel subagents and, in Full mode, the internal session-history flow (which may have stopped at the probe with no relevant prior sessions). Session history is a Phase 1 input even though it runs in the orchestrator rather than as a public skill.

The orchestrating agent (main conversation) performs these steps:

1. Collect Phase 1 results from the run artifacts. For each Phase 1 subagent, Read its artifact file under {workspace_root}/.tmp/rocketclaw/ce-compound/{run_id}/ (context.json, solution.md, related.json, and session-history.md when session history ran). The artifact holds the subagent's full output. Fall back to the subagent's inline return only when its artifact file is absent or empty (e.g., {run_id} did not resolve, or the subagent failed to write). The artifact is authoritative when present — this is what makes the workflow resilient to the issue #956 summary-collapse, where the inline return is only an executive summary.

2. Check the overlap assessment from the Related Docs Finder before deciding what to write:

   Overlap	Action
   High — existing doc covers the same problem, root cause, and solution	Update the existing doc with fresher context (new code examples, updated references, additional prevention tips) rather than creating a duplicate. The existing doc's path and structure stay the same.
   Moderate — same problem area but different angle, root cause, or solution	Create the new doc normally. Flag the overlap for Phase 2.5 to recommend consolidation review.
   Low or none	Create the new doc normally.

   The reason to update rather than create: two docs describing the same problem and solution will inevitably drift apart. The newer context is fresher and more trustworthy, so fold it into the existing doc rather than creating a second one that immediately needs consolidation.

   When updating an existing doc, preserve its file path and frontmatter structure. Update the solution, code examples, prevention tips, and any stale references. Add a last_updated: YYYY-MM-DD field to the frontmatter. Do not change the title unless the problem framing has materially shifted.

3. Incorporate session history findings (if available). When the internal session-history flow returned relevant prior-session context:

   • Fold investigation dead ends and failed approaches into the What Didn't Work section (bug track) or Context section (knowledge track)
   • Use cross-session patterns to enrich the Prevention or Why This Matters sections
   • Tag session-sourced content with "(session history)" so its origin is clear to future readers
   • If findings are thin or "no relevant prior sessions," proceed without session context

4. Assemble complete markdown file from the collected pieces, reading assets/resolution-template.md for the section structure of new docs

5. Validate YAML frontmatter against references/schema.yaml, including the YAML-safety quoting rule for array items (see references/yaml-schema.md > YAML Safety Rules)

6. Create directory if needed: mkdir -p docs/solutions/[category]/

7. Write the file: either the updated existing doc or the new docs/solutions/[category]/[filename].md

8. Validate parser-safety of the written frontmatter to catch silent-corruption issues the prose rules miss: malformed --- delimiter lines, unquoted # in scalar values (silent comment truncation), and unquoted : in scalar values (silent mapping confusion). The bundled validator ships inside the skill bundle; the runtime Bash tool's CWD is the user's project, so run it through the cross-platform SKILL_DIR anchor:

   SKILL_DIR="<absolute path of the directory containing the SKILL.md you just read>";
   python3 "$SKILL_DIR/scripts/validate-frontmatter.py" <output-path>
   

   • Exit 0 means parser-safe; exit 1 means stderr names the offending field(s) — quote the value(s), re-write the doc, and re-run until exit 0. Do not declare success while validation fails.
   • If the anchored script still cannot be resolved or executed, apply the validator's checks by hand, matching its exact scope — checking more broadly risks edits the validator would not require. Fix any violation by quoting the whole value before continuing:
     1. The opening and closing frontmatter delimiters are each a line whose content is --- (trailing whitespace is fine; ---- or ---extra is not a valid delimiter).
     2. For each top-level mapping entry (key: value, no leading indentation) whose value is not already quoted or structured (does not start with ", ', [, {, |, or >): the value must contain no unquoted # (space-then-hash — YAML treats it as a comment and silently truncates) and no unquoted : (colon-then-space — strict YAML may read it as a nested mapping). Quote the whole value if either appears. Nested values, array items, and already-quoted values are out of scope here (array-item quoting is handled by the schema/YAML-safety step above). Then state in the completion output that the bundled script validator was unavailable on this platform and the checks were applied manually.

   The validator does not enforce schema rules and does not flag YAML reserved-indicator characters (those produce loud parser errors downstream rather than silent corruption — out of scope). Uses Python 3 stdlib only (no PyYAML or other deps).

When creating a new doc, preserve the section order from assets/resolution-template.md unless the user explicitly asks for a different structure.

</sequential_tasks>

Phase 2.4: Vocabulary Capture

First, read references/concepts-vocabulary.md. This is unconditional. Do not pre-judge from memory that nothing qualifies — the reference's criteria are non-obvious and qualifying terms often live in the surrounding conversation rather than the new doc itself. Reading the reference is what makes the rest of the phase possible.

Then, applying those criteria, scan the new doc and the surrounding conversation for qualifying domain terms. If CONCEPTS.md exists at the workspace root, add missing qualifying terms and refine existing entries when new precision surfaced. If it does not exist and at least one qualifying term surfaced, create it.

Verify behavior assertions against source before writing them. When an entry asserts how code behaves (states, transitions, limits, semantics), Read the defining source in the current working copy first — an entry drafted from a session-level summary is exactly how wrong semantics enter the glossary. Phase 2.45 re-checks these entries, but the cheap fix is to not write the error.

Seed the learning's area at creation — don't write a lone term. When CONCEPTS.md does not yet exist, alongside the surfaced term also seed the core domain nouns of the area this learning touched, following the Seed goal and Scope of a seed rules in references/concepts-vocabulary.md. The seed is scoped to the learning's area (the modules and domain the fix touched) and defines only terms investigated here — it does not reach for repo-wide nouns. This anchors the surfaced term so it does not dangle against undefined siblings. A repo-wide concept map is ce-compound-refresh's bootstrap path, not this one.

At creation, hold the qualifying bar conservatively for borderline terms. A borderline term, or a class/table/file name dressed up as an entity, defers to a later run — clear core nouns are seeded, borderline ones wait. The conservatism is about quality, not count; updates to an existing file follow the normal criteria.

When bootstrapping the file, start with this preamble under the # Concepts heading, then add the qualifying entries below it:

Shared domain vocabulary for this project — entities, named processes, and status concepts with project-specific meaning. Seeded with core domain vocabulary, then accretes as ce-compound and ce-compound-refresh process learnings; direct edits are fine. Glossary only, not a spec or catch-all.

Refresh the coherence neighborhood of any entry you touch. When adding or editing an entry, also inspect its coherence neighborhood — its cluster siblings and the terms it cross-references or that reference it. Within that neighborhood, do two things: fix glossary violations (implementation specifics — file paths, class names, function signatures, current-config values), and refresh entries the learning's own evidence shows have drifted. Bounds: neighborhood only, never a full-file audit; refresh only on evidence already in hand; if judging a neighbor would require investigation this learning did not do, flag it for ce-compound-refresh rather than editing on a guess. The test: after the edit, would a reader find the touched entry's siblings or referenced terms inconsistent with it? Broader audit is ce-compound-refresh's job.

If no terms qualified after applying the reference's criteria, record that outcome explicitly in the success output (e.g., "Vocabulary capture: scanned, no qualifying terms"). Do not silently skip — the visible scan-and-no-result record is the audit signal that the reference was consulted.

Apply edits silently in every mode — no user prompt in interactive, lightweight, or headless. Vocabulary capture is a side effect of compounding, not a decision the user makes per run. Lightweight mode reaches this through its own single-pass step (see Lightweight Mode), and runs an update-only version — it refines an existing CONCEPTS.md but defers creation/seeding to a Full run.

Phase 2.45: Grounding Validation

The doc (and any CONCEPTS.md entries from Phase 2.4) is about to become permanent, trusted knowledge. Validate its claims against the current working copy and revision graph before it compounds. Read references/grounding-validation.md now — it holds the adjudication rules and the validator prompt; the steps below are only the trigger.

1. Mechanical claims check (every mode, including headless). Optionally run jj git fetch --all-remotes first (best-effort — skip silently offline; the network is never a correctness dependency). Then run the bundled validator against the written doc:

   SKILL_DIR="<absolute path of the directory containing the SKILL.md you just read>";
   python3 "$SKILL_DIR/scripts/validate-doc-claims.py" <doc-path>
   

   Exit 0 means nothing flagged. Exit 1 means flags to adjudicate, not auto-fix — each flagged path, commit ID, link, or scaffold pattern is fixed, annotated as historical, or confirmed intentional per the reference's adjudication table. A doc may legitimately cite a path deleted by the very fix it documents; a flag is a question, not a failure. If the script cannot be resolved on this platform, apply the reference's manual checklist and say so in the output — never silently skip.

2. Semantic grounding validator (Full and headless; lightweight skips it). Dispatch one read-only generic subagent built from the prompt template in the reference, covering the written doc plus any CONCEPTS.md entries added or edited this run. It verifies code-behavior claims by quoting the defining source line, merge-state claims against remote truth (gh primary, JJ revset reachability fallback), and internal completeness of countable assertions. Apply its verdicts per the reference (fix contradicted claims from the quoted evidence; soften or drop unverifiable ones; mark offline merge-state checks as degraded), then re-run the mechanical check if the body changed.

Phase 2.5: Selective Refresh Check

After writing the new learning, decide whether this new solution is evidence that older docs should be refreshed.

ce-compound-refresh is not a default follow-up. Use it selectively when the new learning suggests an older learning or pattern doc may now be inaccurate.

It makes sense to invoke ce-compound-refresh when one or more of these are true:

1. A related learning or pattern doc recommends an approach that the new fix now contradicts
2. The new fix clearly supersedes an older documented solution
3. The current work involved a refactor, migration, rename, or dependency upgrade that likely invalidated references in older docs
4. A pattern doc now looks overly broad, outdated, or no longer supported by the refreshed reality
5. The Related Docs Finder surfaced high-confidence refresh candidates in the same problem space
6. The Related Docs Finder reported moderate overlap with an existing doc — there may be consolidation opportunities that benefit from a focused review

It does not make sense to invoke ce-compound-refresh when:

1. No related docs were found
2. Related docs still appear consistent with the new learning
3. The overlap is superficial and does not change prior guidance
4. Refresh would require a broad historical review with weak evidence

Use these rules:

• If there is one obvious stale candidate, invoke ce-compound-refresh with a narrow scope hint after the new learning is written
• If there are multiple candidates in the same area, ask the user whether to run a targeted refresh for that module, category, or pattern set
• If context is already tight or you are in lightweight mode, do not expand into a broad refresh automatically; instead recommend ce-compound-refresh as the next step with a scope hint
• In headless mode, never invoke ce-compound-refresh and never ask the user. Surface the recommended scope hint in the terminal report's "Refresh recommendation" line and let the caller decide

When invoking or recommending ce-compound-refresh, be explicit about the argument to pass. Prefer the narrowest useful scope:

• Specific file when one learning or pattern doc is the likely stale artifact
• Module or component name when several related docs may need review
• Category name when the drift is concentrated in one solutions area
• Pattern filename or pattern topic when the stale guidance lives in docs/solutions/patterns/

Examples:

• /ce-compound-refresh plugin-versioning-requirements
• /ce-compound-refresh payments
• /ce-compound-refresh performance-issues
• /ce-compound-refresh critical-patterns

A single scope hint may still expand to multiple related docs when the change is cross-cutting within one domain, category, or pattern area.

Do not invoke ce-compound-refresh without an argument unless the user explicitly wants a broad sweep.

Always capture the new learning first. Refresh is a targeted maintenance follow-up, not a prerequisite for documentation.

Discoverability Check

After the learning is written and the refresh decision is made, check whether the project's instruction files would lead an agent to discover and search docs/solutions/ before starting work in a documented area. This runs every time — the knowledge store only compounds value when agents can find it.

1. Identify which root-level instruction files exist (AGENTS.md, CLAUDE.md, or both). Read the file(s) and determine which holds the substantive content — one file may just be a shim that @-includes the other (e.g., CLAUDE.md containing only @AGENTS.md, or vice versa). The substantive file is the assessment and edit target; ignore shims. If neither file exists, skip this check entirely.

2. Assess whether an agent reading the instruction files would learn three things:

   • That a searchable knowledge store of documented solutions exists
   • Enough about its structure to search effectively (category organization, YAML frontmatter fields like module, tags, problem_type)
   • When to search it (before implementing features, debugging issues, or making decisions in documented areas — learnings may cover bugs, best practices, workflow patterns, or other institutional knowledge)

   This is a semantic assessment, not a string match. The information could be a line in an architecture section, a bullet in a gotchas section, spread across multiple places, or expressed without ever using the exact path docs/solutions/. Use judgment — if an agent would reasonably discover and use the knowledge store after reading the file, the check passes.

3. If the spirit is already met, no action needed — move on.

4. If not: a. Based on the file's existing structure, tone, and density, identify where a mention fits naturally. Before creating a new section, check whether the information could be a single line in the closest related section — an architecture tree, a directory listing, a documentation section, or a conventions block. A line added to an existing section is almost always better than a new headed section. Only add a new section as a last resort when the file has clear sectioned structure and nothing is even remotely related. b. Draft the smallest addition that communicates the three things. Match the file's existing style and density. The addition should describe the knowledge store itself, not the plugin — an agent without the plugin should still find value in it.

   Keep the tone informational, not imperative. Express timing as description, not instruction — "relevant when implementing or debugging in documented areas" rather than "check before implementing or debugging." Imperative directives like "always search before implementing" cause redundant reads when a workflow already includes a dedicated search step. The goal is awareness: agents learn the folder exists and what's in it, then use their own judgment about when to consult it.

   Examples of calibration (not templates — adapt to the file):

   When there's an existing directory listing or architecture section — add a line:

   docs/solutions/  # documented solutions to past problems (bugs, best practices, workflow patterns), organized by category with YAML frontmatter (module, tags, problem_type)
   

   When nothing in the file is a natural fit — a small headed section is appropriate:

   ## Documented Solutions
   
   `docs/solutions/` — documented solutions to past problems (bugs, best practices, workflow patterns), organized by category with YAML frontmatter (`module`, `tags`, `problem_type`). Relevant when implementing or debugging in documented areas.
   

   c. In full interactive mode, explain to the user why this matters — agents working in this repo (including fresh sessions, other tools, or collaborators without the plugin) won't know to check docs/solutions/ unless the instruction file surfaces it. Show the proposed change and where it would go, then use the platform's blocking question tool to get consent before making the edit: AskUserQuestion in Claude Code (call ToolSearch with select:AskUserQuestion first if its schema isn't loaded), request_user_input in Codex, ask_question in Antigravity CLI (agy), ask_user in Pi (requires the pi-ask-user extension). Fall back to presenting the proposal in chat only when no blocking tool exists in the harness or the call errors (e.g., Codex edit modes) — not because a schema load is required. Never silently skip the question. In lightweight mode, output a one-liner note and move on. In headless mode, do not edit instruction files — surface the gap in the terminal report as Instruction-file edit: gap noted, not applied (headless scope is documentation capture, not project-config edits; a human-invoked interactive run applies the edit with consent)

5. If CONCEPTS.md exists at the workspace root, run a parallel discoverability check for it. Assess whether the instruction file would lead an agent to discover the project's shared domain vocabulary. Use the same workflow as the docs/solutions/ check above: same target file, same edit-placement judgment, same consent-then-edit interaction shape per mode. A line in an existing section is almost always better than a new headed section. Example calibration when nothing else fits:

   CONCEPTS.md  # shared domain vocabulary (entities, named processes, status concepts) — relevant when orienting to the codebase or discussing domain concepts
   

   Skip this step entirely if CONCEPTS.md does not exist — never nag for an artifact the project has not adopted. When skipped, this step produces no output and no edit.

Phase 3: Optional Enhancement

WAIT for Phase 2 to complete before proceeding.

Skip Phase 3 entirely in headless mode to bound token usage — the caller does not have a human-in-the-loop to act on reviewer findings, and downstream automations can run specialized reviewers themselves if they want that pass.

<parallel_tasks>

Based on problem type, optionally dispatch generic subagents seeded with local prompt assets from references/agents/ to review the documentation. Do not dispatch standalone agents by type/name.

• performance_issue → references/agents/performance-oracle.md
• security_issue → references/agents/security-sentinel.md
• database_issue → references/agents/data-integrity-guardian.md
• Any code-heavy issue → preserve code simplification as a read-only documentation review. Inspect the solution draft's code examples and explanatory claims inline, or dispatch a generic subagent seeded with a local prompt only to return suggestions. Do not invoke ce-simplify-code from this phase and do not mutate product code unless the user explicitly asks for a separate code-simplification pass. Do not use the deleted code-simplicity-reviewer. Example: review the solution draft's examples for speculative abstractions, redundant wrappers, dead paths, and just-in-case parameters. Apply edits only to the documentation/examples being written by ce-compound; leave any working-copy code changes untouched.

</parallel_tasks>

---

Lightweight Mode

<critical_requirement> Single-pass alternative — same documentation, fewer tokens.

This mode skips parallel subagents entirely. The orchestrator performs all work in a single pass, producing the same solution document without cross-referencing or duplicate detection.

Headless mode forces Full and does not enter Lightweight — automations get the cross-reference and overlap detection benefits without the interactive overhead. </critical_requirement>

The orchestrator (main conversation) performs ALL of the following in one sequential pass:

1. Extract from conversation: Identify the problem and solution from conversation history. Also scan the "user's auto-memory" block injected into your system prompt, if present (Claude Code only) -- use any relevant notes as supplementary context alongside conversation history. Tag any memory-sourced content incorporated into the final doc with "(auto memory [claude])". Before asserting how code behaves (enum values, status semantics, limits, defaults), Read the defining line in the current working copy — soften or attribute any claim you cannot verify. Cite PR numbers over bare commit IDs, and phrase unmerged fixes as pending
2. Classify: Read references/schema.yaml and references/yaml-schema.md, then determine track (bug vs knowledge), category, and filename
3. Write minimal doc: Create docs/solutions/[category]/[filename].md using the appropriate track template from assets/resolution-template.md, with:
   • YAML frontmatter with track-appropriate fields, applying the YAML-safety quoting rule for array items (see references/yaml-schema.md > YAML Safety Rules)
   • Bug track: Problem, root cause, solution with key code snippets, one prevention tip
   • Knowledge track: Context, guidance with key examples, one applicability note
4. Vocabulary capture (update-only): if CONCEPTS.md exists at the workspace root, read references/concepts-vocabulary.md, then scan the new doc and the conversation for qualifying terms and add/refine entries silently (same criteria as Phase 2.4). Do not bootstrap or seed in lightweight mode — if CONCEPTS.md does not exist, defer creation to a Full run, which owns seeding. Record the outcome in the output (e.g., "Vocabulary: 1 entry refined" or "scanned, no qualifying terms"). If you refined CONCEPTS.md and a quick read of AGENTS.md/CLAUDE.md shows it isn't surfaced there, add the discoverability tip to the output below — lightweight tips, it does not edit instruction files (an interactive Full run owns that edit after consent; headless Full also tips/reports only).
5. Mechanical claims check: run scripts/validate-doc-claims.py against the written doc exactly as in Phase 2.45 step 1 (same SKILL_DIR anchor, same adjudicate-not-auto-fix rule — read references/grounding-validation.md for the adjudication table when it flags anything). Lightweight skips only the semantic validator subagent, not this deterministic check.
6. Skip specialized agent reviews (Phase 3) and the semantic grounding validator (Phase 2.45 step 2) to conserve context

Lightweight output:

✓ Documentation complete (lightweight mode)

File created:
- docs/solutions/[category]/[filename].md

[If discoverability check found instruction files don't surface the knowledge store:]
Tip: Your AGENTS.md/CLAUDE.md doesn't surface docs/solutions/ to agents —
a brief mention helps all agents discover these learnings.

[If CONCEPTS.md was refined this run and isn't surfaced in the instruction files:]
Tip: Your AGENTS.md/CLAUDE.md doesn't surface CONCEPTS.md —
a one-line mention helps agents find the shared vocabulary.

Note: This was created in lightweight mode. For richer documentation
(cross-references, detailed prevention strategies, specialized reviews,
semantic grounding validation), re-run /ce-compound in a fresh session.

No subagents are launched. No parallel tasks. The solution doc is the one deliverable (Phase 2.4's update-only vocabulary capture may also refine an existing CONCEPTS.md).

In lightweight mode, the overlap check is skipped (no Related Docs Finder subagent). This means lightweight mode may create a doc that overlaps with an existing one. That is acceptable — ce-compound-refresh will catch it later. Only suggest ce-compound-refresh if there is an obvious narrow refresh target. Do not broaden into a large refresh sweep from a lightweight session.

---

What It Captures

• Problem symptom: Exact error messages, observable behavior
• Investigation steps tried: What didn't work and why
• Root cause analysis: Technical explanation
• Working solution: Step-by-step fix with code examples
• Prevention strategies: How to avoid in future
• Cross-references: Links to related issues and docs

Preconditions

Problem has been solved (not in-progress) Solution has been verified working Non-trivial problem (not simple typo or obvious error)

What It Creates

Organized documentation:

• File: docs/solutions/[category]/[filename].md

Categories auto-detected from problem:

Bug track:

• build-errors/
• test-failures/
• runtime-errors/
• performance-issues/
• database-issues/
• security-issues/
• ui-bugs/
• integration-issues/
• logic-errors/

Knowledge track:

• architecture-patterns/ — architectural or structural patterns (agent/skill/pipeline/workflow shape decisions)
• design-patterns/ — reusable non-architectural design approaches (content generation, interaction patterns, prompt shapes)
• tooling-decisions/ — language, library, or tool choices with durable rationale
• conventions/ — team-agreed way of doing something, captured so it survives turnover
• workflow-issues/
• developer-experience/
• documentation-gaps/
• best-practices/ — fallback only, use when no narrower knowledge-track value applies

Common Mistakes to Avoid

❌ Wrong	✅ Correct
Subagents write product files into docs/ or edit tracked paths	Subagents write only scratch artifacts under <workspace-root>/.tmp/rocketclaw/ce-compound/<run-id>/ and return the path; orchestrator writes the one final doc
Subagent returns a long prose body only as its inline response	Subagent writes full output to its run artifact; orchestrator Reads it back (inline return is fallback only)
Research and assembly run in parallel	Research completes → then assembly runs
Multiple files created during workflow	One solution doc written or updated: docs/solutions/[category]/[filename].md (plus optional maintenance writes: a CONCEPTS.md create/update from Phase 2.4, and — interactive Full only, after consent — a small instruction-file edit for discoverability)
Headless Discoverability Check edits AGENTS.md/CLAUDE.md	Headless reports Instruction-file edit: gap noted, not applied; only interactive Full applies the edit after consent
Creating a new doc when an existing doc covers the same problem	Check overlap assessment; update the existing doc when overlap is high
Asserting code behavior or merge-state from conversation memory	Read the defining source line before asserting; cite PR numbers over commit IDs; soften unverifiable claims (Phase 1 extractor rules, re-checked in Phase 2.45)
Batching several learnings through one run and stitching cross-references between drafts	One learning per run; run the skill sequentially for each additional learning

Success Output

Headless mode

Emit a structured terminal report and end the turn. No "What's next?" question, no blocking prompt. End with Documentation complete as the terminal signal so callers can detect completion.

✓ Documentation complete (headless mode)

File: docs/solutions/<category>/<filename>.md  (created | updated)
Track: <bug | knowledge>
Category: <category>
Overlap: <none | low | moderate — see <path> | high — existing doc updated>
Grounding: <clean | N flags adjudicated (X fixed, Y annotated, Z confirmed) | N claims softened or corrected | degraded — merge-state claims unverified offline>
Instruction-file edit: <none needed | gap noted, not applied>
CONCEPTS.md: <scanned, no qualifying terms | created with N entries (M seeded from the learning's area) | updated — N added, N refined>
Refresh recommendation: <none | scope hint for /ce-compound-refresh>

Documentation complete

When no doc was written (e.g., headless invoked on a session where the problem is not yet solved), emit a structured failure instead and end with Documentation skipped so callers can distinguish success from no-op:

✗ Documentation skipped (headless mode)

Reason: <one-sentence explanation — e.g., "no solved problem detected in
conversation history" or "solution not yet verified">

Documentation skipped

Interactive mode

✓ Documentation complete

Ran Full mode.
Auto memory: 2 relevant entries used as supplementary evidence

Subagent Results:
  ✓ Context Analyzer: Identified performance_issue in brief_system, category: performance-issues/
  ✓ Solution Extractor: 3 code fixes, prevention strategies
  ✓ Related Docs Finder: 2 related issues
  ✓ Session History: 3 prior sessions on the same bookmark, 2 failed approaches surfaced

Grounding Validation:
  ✓ Mechanical check: 14 paths, 2 commit IDs, 3 links checked — 1 flag annotated as historical
  ✓ Semantic validator: 9 claims verified, 1 merge-state claim softened to pending

Specialized Agent Reviews (Auto-Triggered):
  ✓ performance-oracle: Validated query optimization approach
  ✓ Code simplification review: Code examples are appropriately minimal

Files written:
- docs/solutions/performance-issues/n-plus-one-brief-generation.md (created)
- CONCEPTS.md (created with 3 entries: BriefSystem, EmailQueue, Brief Status)

This documentation will be searchable for future reference when similar
issues occur in the Email Processing or Brief System modules.

Refresh recommendation: none

End the turn after the summary — ce-compound does not present a "What's next?" menu. The doc is written and any cross-references the workflow found are already in it. Cross-doc maintenance (fixing references in other docs, consolidation) is deferred to ce-compound-refresh via the Refresh recommendation line above — the skill designed for it — not auto-applied here, which would edit tracked docs beyond the one deliverable. If the user wants to view the file or take a follow-up action, they will ask. (Interactive mode only.)

Alternate interactive output (when updating an existing doc due to high overlap): in headless mode, this case is communicated via the Overlap: high — existing doc updated line of the headless terminal report above, not as a separate output block.

✓ Documentation updated (existing doc refreshed with current context)

Overlap detected: docs/solutions/performance-issues/n-plus-one-queries.md
  Matched dimensions: problem statement, root cause, solution, referenced files
  Action: Updated existing doc with fresher code examples and prevention tips

File updated:
- docs/solutions/performance-issues/n-plus-one-queries.md (added last_updated: 2026-03-24)

The Compounding Philosophy

This creates a compounding knowledge system:

1. First time you solve "N+1 query in brief generation" → Research (30 min)
2. Document the solution → docs/solutions/performance-issues/n-plus-one-briefs.md (5 min)
3. Next time similar issue occurs → Quick lookup (2 min)
4. Knowledge compounds → Team gets smarter

The feedback loop:

Build → Test → Find Issue → Research → Improve → Document → Validate → Deploy
    ↑                                                                      ↓
    └──────────────────────────────────────────────────────────────────────┘

Each unit of engineering work should make subsequent units of work easier—not harder.

Auto-Invoke

<auto_invoke> <trigger_phrases> - "that worked" - "it's fixed" - "working now" - "problem solved" </trigger_phrases>

<manual_override> Use /ce-compound [context] to document immediately without waiting for auto-detection. </manual_override> </auto_invoke>

Output

Writes the final learning directly into docs/solutions/.

Applicable Specialized Local Prompts

Based on problem type, these local prompt assets can enhance documentation:

Code Quality & Review

• Read-only code simplification review: Checks solution examples and documentation claims for unnecessary complexity without mutating product code
• references/agents/pattern-recognition-specialist.md: Identifies anti-patterns or repeating issues

Specific Domain Experts

• references/agents/performance-oracle.md: Analyzes performance_issue category solutions
• references/agents/security-sentinel.md: Reviews security_issue solutions for vulnerabilities
• references/agents/data-integrity-guardian.md: Reviews database_issue migrations and queries

Enhancement & Research

• references/agents/best-practices-researcher.md: Enriches solution with industry best practices
• references/agents/framework-docs-researcher.md: Links to framework/library documentation references

When to Invoke

• Auto-triggered (optional): Generic subagents seeded with local prompts can run post-documentation for enhancement
• Manual trigger: User can run surviving skills such as ce-simplify-code after /ce-compound completes for deeper code review and mutation

Related Commands

• /research [topic] - Deep investigation (searches docs/solutions/ for patterns)
• /ce-plan - Planning workflow (references documented solutions)