ce-compound

name	ce-compound
description	Document a recently solved problem to compound your team's knowledge or CONCEPTS.md, the project's shared domain vocabulary.
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 .ai/solutions/ with YAML frontmatter for searchability and future reference. Uses parallel subagents for maximum efficiency.

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

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

Check $ARGUMENTS for a mode:headless token. 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 mode token present	Ask Full vs Lightweight, ask about session history (Full only), prompt for Discoverability Check consent, end with "What's next?"
Headless	mode:headless in arguments	No blocking questions. Run Full mode without session history. Apply the Discoverability Check edit silently if a gap exists. 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.

Pre-resolved context

Git branch (pre-resolved): !git rev-parse --abbrev-ref HEAD 2>/dev/null || true

If the line above resolved to a plain branch name (like feat/my-branch), include it in the ce-sessions invocation payload in Phase 1 so the orchestrator does not waste a turn deriving it. If it still contains a backtick command string or is empty, omit it and let ce-sessions derive it at runtime.

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)
• assets/resolution-template.md — section structure for new docs (read when assembling)

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

Execution Strategy

In headless mode, skip both questions below and go directly to Full Mode with session history disabled. Phase 1's session-history step (step 4) is omitted. Proceed straight to research.

In interactive mode, present the user with two options before proceeding, using the platform's blocking question tool: AskUserQuestion in Claude Code (call ToolSearch with select:AskUserQuestion first if its schema isn't loaded), request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension). Fall back to presenting options 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.

1. Full (recommended) — the complete compound workflow. Researches,
   cross-references, and reviews your solution to produce documentation
   that compounds your team's knowledge.

2. Lightweight — same documentation, single pass. Faster and uses
   fewer tokens, but won't detect duplicates or cross-reference
   existing docs. Best for simple fixes or long sessions nearing
   context limits.

In interactive mode, do NOT pre-select a mode, do NOT skip this prompt, and wait for the user's choice before proceeding. (Headless mode bypasses this prompt per the "In headless mode" rule above and runs Full directly — these "do not skip" directives do not apply to headless.)

If the user chooses Full (interactive mode only), ask one follow-up question before proceeding. Detect which harness is running (Claude Code, Codex, or Cursor) and ask:

Would you also like to search your [harness name] session history
for relevant knowledge to help the Compound process? This adds
time and token usage.

If the user says yes, invoke ce-sessions in Phase 1 (see step 4). If no, skip it. Do not ask this in lightweight mode or headless mode.

---

Full Mode

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

Phase 1 subagents return TEXT DATA to the orchestrator. They must NOT use Write, Edit, or create any files. Only the orchestrator writes files. Beyond the Phase 2 solution doc, its other writes are maintenance side effects — not additional deliverables, and creating one when absent is expected, not a violation of this rule:

• CONCEPTS.md — create or update in Phase 2.4 (Vocabulary Capture) when a qualifying domain term surfaces.
• A project instruction file (AGENTS.md or CLAUDE.md) — a small edit when the Discoverability Check finds a gap.

Both ensure future agents can discover and ground in the knowledge store; neither makes the documentation any less the single deliverable. </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 returns text data to the orchestrator.

Dispatch order:

• Launch Context Analyzer, Solution Extractor, and Related Docs Finder in parallel (background)
• Then invoke the ce-sessions skill via the platform's skill-invocation primitive (see step 4 below) — only if the user opted in to session history. The skill call 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(ce-sessions, slowest background subagent), not their sum). Issuing the skill call before the parallel block would serialize ce-sessions 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 .ai/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
• Returns: YAML frontmatter skeleton (must include category: field mapped from problem_type), category directory path, suggested filename, and which track applies
• 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
• 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

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 .ai/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
• Returns: Links, relationships, refresh candidates, and overlap assessment (score + which dimensions matched)

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 .ai/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 via ce-sessions (synchronous skill call, after launching the parallel block — only if the user opted in)

• Skip entirely if the user declined session history in the follow-up question, if running in lightweight mode, or if running in headless mode.

• Invoke the ce-sessions skill via the platform's skill-invocation primitive (Skill in Claude Code, Skill in Codex, the equivalent on Gemini/Pi). Pass the dispatch payload below as the skill argument string. ce-sessions runs in main context -- it owns discovery, branch/keyword filtering, scan-window selection, the deep-dive cap, per-session extraction to a mktemp scratch dir, and dispatch of the synthesis-only session-historian subagent. The compound orchestrator only needs to pass the topic and time window and read back the findings text.

  Dispatch payload — keep tight. A long, keyword-rich payload licenses ce-sessions to keep widening. Use this shape:

  • Pre-resolved context (only if values resolved cleanly above; otherwise omit): repo name, current git branch.

  • 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 branches."

  • 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 ce-sessions license to keep widening the search and rapidly compound wall time. If keyword search is needed, ce-sessions owns that decision internally based on the topic.

  • Returns: structured digest of findings from prior sessions, or "no relevant prior sessions" if none found.
  • ce-sessions 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.

Phase 2: Assembly & Write

<sequential_tasks>

WAIT for all Phase 1 inputs to complete before proceeding — the three parallel subagents and, when the user opted in, the synchronous ce-sessions skill call. ce-sessions is a Phase 1 input even though it is a skill rather than a subagent.

The orchestrating agent (main conversation) performs these steps:

1. Collect all text results from Phase 1 subagents

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 ce-sessions 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 .ai/solutions/[category]/

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

8. Run python3 scripts/validate-frontmatter.py <output-path> to catch silent-corruption parser-safety issues that the prose rules miss: malformed --- delimiter lines, unquoted # in scalar values (silent comment truncation), and unquoted : in scalar values (silent mapping confusion). Exit 0 means the doc is parser-safe; exit 1 means the script's stderr names the offending field(s) and what to fix — quote the value(s), re-write the doc, and re-run until exit 0. Do not declare success while validation fails. The script 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 repo 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.

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 2.5: 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 .ai/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 .ai/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 .ai/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:

   .ai/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
   
   `.ai/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 .ai/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_user in Gemini, 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, apply the edit directly without prompting and surface it in the terminal report under "Instruction-file edit"

5. If CONCEPTS.md exists at repo 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 .ai/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 invoke specialized agents to review the documentation:

• performance_issue -> capture the measured hot path, scale assumption, and verification in the learning; use performance-reviewer only for code-backed follow-up review.
• security_issue -> route explicit vulnerability or secure-default follow-up through the security-review / security-best-practices skills.
• database_issue -> keep migration, backfill, schema snapshot, and persistent data transformation follow-up in the orchestrator unless a current migration specialist agent exists in the active agent shelf.
• Any code-heavy issue -> always run code-simplicity-reviewer for minimal, clear examples. Structural concerns in the diff are already covered when the same work goes through /ce-review (maintainability persona).

</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])"
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 .ai/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 repo 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 (a Full run owns that edit).
5. Skip specialized agent reviews (Phase 3) to conserve context

Lightweight output:

✓ Documentation complete (lightweight mode)

File created:
- .ai/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 .ai/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),
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

Document only when all hold:

• the problem is solved, not in-progress
• the solution has been verified working
• the problem was non-trivial (not a simple typo or obvious error)

What It Creates

Organized documentation:

• File: .ai/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 files like context-analysis.md, solution-draft.md	Subagents return text data; orchestrator writes one final file
Research and assembly run in parallel	Research completes → then assembly runs
Multiple files created during workflow	One solution doc written or updated: .ai/solutions/[category]/[filename].md (plus optional maintenance writes: a CONCEPTS.md create/update from Phase 2.4 and a small instruction-file edit for discoverability)
Creating a new doc when an existing doc covers the same problem	Check overlap assessment; update the existing doc when overlap is high

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: .ai/solutions/<category>/<filename>.md  (created | updated)
Track: <bug | knowledge>
Category: <category>
Overlap: <none | low | moderate — see <path> | high — existing doc updated>
Instruction-file edit: <none needed | applied to <path> | 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

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 same branch, 2 failed approaches surfaced

Specialized Agent Reviews (Auto-Triggered):
  ✓ performance-reviewer: Validated query optimization approach
  ✓ code-simplicity-reviewer: Solution is appropriately minimal

Files written:
- .ai/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.

What's next?
1. Continue workflow (recommended)
2. Link related documentation
3. Update other references
4. View documentation
5. Other

After displaying the interactive success output above, present the "What's next?" options using the platform's blocking question tool: AskUserQuestion in Claude Code (call ToolSearch with select:AskUserQuestion first if its schema isn't loaded), request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension). Fall back to numbered options 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. Do not continue the workflow or end the turn without the user's selection. (Interactive mode only — headless skips this per the headless block above.)

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: .ai/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:
- .ai/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 → .ai/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.

Output

Writes the final learning directly into .ai/solutions/.

Applicable Specialized Agents

Based on problem type, these agents can enhance documentation:

Code Quality & Review

• code-simplicity-reviewer: Ensures solution code is minimal and clear
• pattern-recognition-specialist: Identifies anti-patterns or repeating issues

Specific Domain Experts

• performance-reviewer: Reviews code-backed performance_issue follow-ups
• security-review / security-best-practices: Review security_issue follow-ups
• Migration specialist agent when present: Reviews database_issue migrations, backfills, schema snapshots, and persistent data transforms

Enhancement & Research

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

When to Invoke

• Auto-triggered (optional): Agents can run post-documentation for enhancement
• Manual trigger: User can invoke agents after /ce-compound completes for deeper review

Related Commands

• /ce-plan - Planning workflow (references documented solutions)