gh-compound

name	gh:compound
description	Document a recently solved problem to compound your team's knowledge or update CONCEPTS.md, the project's shared domain vocabulary.

/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 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

/gh:compound                    # Document the most recent fix
/gh:compound [brief context]    # Provide additional context hint

Pre-resolved context

Repo name (pre-resolved): !bash scripts/resolve-repo-name.sh

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

If the lines above resolved to plain values (a folder name like my-repo and a branch name like feat/my-branch), pass them into the Session Historian dispatch in Phase 1 so the agent does not waste a turn deriving them. If they still contain backtick command strings or are empty, omit them from the dispatch and let the agent derive them 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

Before presenting mode options to the user, log the skill start event so this execution appears on the task board:

1. Run gale-task log skill_started --skill gh:compound --title "<compound-topic>" to register this execution on the task board.
2. If gale-task is not on PATH or the command fails, skip and continue — this must never block the skill.

Config: At the start of execution, use your native file-read tool to read .compound-engineering/config.local.yaml from the repository root. If the file is missing in the current worktree, check the main repository root (the parent of .git/worktrees). If the file is missing or unreadable, do not block the workflow — proceed silently with default settings.

If the config file contains language: en, write documents in English. If the file is missing, contains language: zh-CN, or has no language key, write documents in Chinese (default).

Present the user with two options before proceeding, using the platform's blocking question tool (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension)). If no question tool is available, present the options and wait for the user's reply.

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.

Do NOT pre-select a mode. Do NOT skip this prompt. Wait for the user's choice before proceeding.

If the user chooses Full, 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, dispatch the Session Historian in Phase 1. If no, skip it. Do not ask this in lightweight mode.

---

Full Mode

<critical_requirement> The primary output 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: the solution doc in Phase 2, CONCEPTS.md when Phase 2.4 finds qualifying vocabulary, and — if the Discoverability Check finds a gap — a small edit to a project instruction file (AGENTS.md or CLAUDE.md). The instruction-file edit and vocabulary update are maintenance side effects, not second deliverables; they ensure future agents can discover and ground in the knowledge store. </critical_requirement>

Phase 0.4: HKTMemory Retrieve

Before Phase 0.5, query the vector memory database for related solutions:

1. Extract a 1-2 sentence search query from: problem being documented, error messages, component names, fix approach
2. Run (requires env vars HKT_MEMORY_API_KEY, HKT_MEMORY_BASE_URL, HKT_MEMORY_MODEL):

   memory_root="$(gale-memory resolve-root 2>/dev/null || true)"
   [ -n "$memory_root" ] && export HKT_MEMORY_DIR="$memory_root"
   hkt-memory retrieve \
     --query "<extracted query>" \
     --layer all --limit 10 --min-similarity 0.35 \
     --vector-weight 0.7 --bm25-weight 0.3
   

3. If results returned, prepare block and pass to Phase 1 subagents as extra context:

   ## Supplementary notes from HKTMemory
   Source: vector database. Treat as additional context, not primary evidence.
   [results here, each tagged with (similarity: X.XX)]
   

4. If no results or command error, proceed silently without blocking Phase 0.5.

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 dispatch session-historian in foreground — it reads session files outside the working directory that background agents may not have access to
• The foreground dispatch runs while the background agents work, adding no 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]-[date].md
• 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 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
• 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 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 Historian (foreground, after launching the above — only if the user opted in)

• Skip entirely if the user declined session history in the follow-up question
• Dispatched as galeharness-cli:session-historian
• Dispatch in foreground — this agent reads session files outside the working directory (~/.claude/projects/, ~/.codex/sessions/, ~/.cursor/projects/) which background agents may not have access to
• Omit the mode parameter so the user's configured permission settings apply
• Dispatch on the mid-tier model (e.g., model: "sonnet" in Claude Code) — the synthesis feeds into compound assembly and doesn't need frontier reasoning

Dispatch prompt — keep tight. A long, keyword-rich prompt licenses the agent to keep widening. Use this shape:

• Pre-resolved context (only if values resolved cleanly above; otherwise omit and let the agent derive): 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 dispatch prompts give the agent license to keep widening the search and rapidly compound wall time. If the agent needs keyword search, it owns that decision via the --keyword mode on gh-session-inventory.

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

Phase 2: Assembly & Write

<sequential_tasks>

WAIT for all Phase 1 subagents to complete before proceeding.

Document Language: When language: zh-CN (or default), write all prose content in Chinese. Keep section headers (## Problem, ## Solution, etc.) and YAML frontmatter keys in English. Translate paragraphs, list items, and table content. Do NOT translate code blocks, inline code, file paths, or URLs.

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 the Session History Researcher 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

Knowledge Repository Write Path

Before writing the solution document, resolve the target directory:

1. Run gale-knowledge resolve-path --type solutions to get the target directory path (the command outputs a plain path string). If the command fails or gale-knowledge is not available, fall back to docs/solutions.
2. Use <resolved-path>/[category]/ as the primary target directory for the solution document.
3. Also write a copy to docs/solutions/[category]/<filename>.md (dual-write). If this secondary write fails, log a warning but do not fail the workflow.

5. Validate YAML frontmatter against references/schema.yaml
6. Create directories if needed for both primary (<resolved-path>/[category]/) and secondary (docs/solutions/[category]/) paths.
7. Write the file to the primary path (<resolved-path>/[category]/[filename].md) and the secondary path (docs/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 2.3: HKTMemory Store

After successfully writing the solution doc to docs/solutions/:

1. Compose a concise summary (2-4 sentences) covering: the problem, the solution, key decisions, and the repo-relative file path to the solution document
2. Extract title and category values from its YAML frontmatter
3. Run:

   memory_root="$(gale-memory resolve-root 2>/dev/null || true)"
   [ -n "$memory_root" ] && export HKT_MEMORY_DIR="$memory_root"
   hkt-memory store \
     --content "<summary + repo-relative file path>" \
     --title "<frontmatter title>" \
     --topic "<frontmatter category>" \
     --layer all
   

4. Log: Stored to HKTMemory: [title] on success, or note the error (non-blocking — do not fail the compound workflow if HKTMemory is unavailable).

Rationale: The vector database's job is discovery, not full-text storage. The document already lives in a git-managed file. Store the summary and path so retrieval can surface it; the agent reads the actual file when details are needed.

5. After a successful HKTMemory store, run gale-task log memory_linked --memory-title "<frontmatter title>" to log the memory_linked event. If gale-task is not on PATH or the command fails, skip and continue.

Knowledge Repository Commit

After the solution document is written and stored to HKTMemory:

1. Run gale-knowledge extract-project to get the project name. If the command fails or is not available, use the current directory basename as the project name instead.
2. Run gale-knowledge commit --project "<project-name>" --type solution --title "<frontmatter-title>" to commit the knowledge document. If this command fails, log the error but continue — the document has already been written to disk.
3. If gale-knowledge is not on PATH, skip both steps and continue — this must never block the skill.

Phase 2.4: Vocabulary Capture

After the solution doc is written, validated, and stored, scan the new or updated doc for project-specific terms whose meaning would not be obvious to a new engineer. Read references/concepts-vocabulary.md before deciding what qualifies.

• If CONCEPTS.md already defines the term accurately, do not edit it.
• If a qualifying term is missing, add a concise definition to CONCEPTS.md.
• If a definition conflicts with the newly documented learning, update it only when the new learning contains verified evidence; otherwise leave a refresh recommendation in the final report.
• Do not add general programming vocabulary or transient implementation details.

Treat CONCEPTS.md as a glossary side effect of the learning capture, not as the primary deliverable.

Phase 2.5: Selective Refresh Check

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

gh: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 gh: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 gh: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 gh: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 gh:compound-refresh as the next step with a scope hint

When invoking or recommending gh: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:

• /gh:compound-refresh plugin-versioning-requirements
• /gh:compound-refresh payments
• /gh:compound-refresh performance-issues
• /gh: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 gh: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 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 (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension)) to get consent before making the edit. If no question tool is available, present the proposal and wait for the user's reply. In lightweight mode, output a one-liner note and move on

Phase 3: Optional Enhancement

WAIT for Phase 2 to complete before proceeding.

<parallel_tasks>

Based on problem type, optionally invoke specialized agents to review the documentation:

• performance_issue → galeharness-cli:performance-oracle
• security_issue → galeharness-cli:security-sentinel
• database_issue → galeharness-cli:data-integrity-guardian
• Any code-heavy issue → always run galeharness-cli:code-simplicity-reviewer, and additionally run the gale reviewer that matches the repo's primary stack:
  • Ruby/Rails → also run galeharness-cli:gale-rails-reviewer
  • Python → also run galeharness-cli:gale-python-reviewer
  • TypeScript/JavaScript → also run galeharness-cli:gale-typescript-reviewer
  • Other stacks → no gale reviewer needed

</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. </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 docs/solutions/[category]/[filename].md using the appropriate track template from assets/resolution-template.md, with:
   • YAML frontmatter with track-appropriate fields
   • Bug track: Problem, root cause, solution with key code snippets, one prevention tip
   • Knowledge track: Context, guidance with key examples, one applicability note
4. Skip specialized agent reviews (Phase 3) 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.

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

No subagents are launched. No parallel tasks. One file written.

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 — gh:compound-refresh will catch it later. Only suggest gh: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:

• best-practices/
• workflow-issues/
• developer-experience/
• documentation-gaps/

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: docs/solutions/[category]/[filename].md (plus an optional small edit to a project instruction file 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

✓ 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-oracle: Validated query optimization approach
  ✓ gale-rails-reviewer: Code examples meet Rails conventions
  ✓ code-simplicity-reviewer: Solution is appropriately minimal

File created:
- docs/solutions/performance-issues/n-plus-one-brief-generation.md

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 success output, present the "What's next?" options using the platform's blocking question tool (AskUserQuestion in Claude Code, request_user_input in Codex, ask_user in Gemini, ask_user in Pi (requires the pi-ask-user extension)). If no question tool is available, present the numbered options and wait for the user's reply before proceeding. Do not continue the workflow or end the turn without the user's selection.

Alternate output (when updating an existing doc due to high overlap):

✓ 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 /gh: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 Agents

Based on problem type, these agents can enhance documentation:

Code Quality & Review

• galeharness-cli:gale-rails-reviewer: Reviews code examples for Rails best practices
• galeharness-cli:gale-python-reviewer: Reviews code examples for Python best practices
• galeharness-cli:gale-typescript-reviewer: Reviews code examples for TypeScript best practices
• galeharness-cli:code-simplicity-reviewer: Ensures solution code is minimal and clear
• galeharness-cli:pattern-recognition-specialist: Identifies anti-patterns or repeating issues

Specific Domain Experts

• galeharness-cli:performance-oracle: Analyzes performance_issue category solutions
• galeharness-cli:security-sentinel: Reviews security_issue solutions for vulnerabilities
• galeharness-cli:data-integrity-guardian: Reviews database_issue migrations and queries

Enhancement & Research

• galeharness-cli:best-practices-researcher: Enriches solution with industry best practices
• galeharness-cli: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 /gh:compound completes for deeper review

Related Commands

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

Task Lifecycle End

After the compound workflow is fully complete (documentation written, discoverability check done), log the completion event:

1. Run gale-memory store-session-transcript --skill gh:compound --phase completed --source-mode compound_artifact --importance high --summary "<learning document title and reusable pattern>" --content "<problem, root cause, solution, artifacts written, verification, links>" to make the completed compound artifact session available to list-recent and session-search.
2. If gale-memory is not on PATH or the command fails, skip and continue — this must never block the skill.
3. Run gale-task log skill_completed to record the completion event.
4. If gale-task is not on PATH or the command fails, skip and continue — this must never block the skill.