compound

name	compound
description	This skill should be used when documenting a recently solved problem to compound your team's knowledge.

/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 knowledge-base/project/learnings/ 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

skill: soleur:compound               # Document the most recent fix
skill: soleur:compound [brief context]  # Provide additional context hint
skill: soleur:compound --headless    # Headless mode: auto-approve all prompts

Headless Mode Detection

If $ARGUMENTS contains --headless, set HEADLESS_MODE=true. Strip --headless from $ARGUMENTS before processing remaining args.

Branch safety check (defense-in-depth): Run git branch --show-current. If the result is main or master, abort immediately with: "Error: compound cannot run on main/master. Checkout a feature branch first." This check fires in all modes (headless and interactive) as defense-in-depth alongside PreToolUse hooks -- it fires even if hooks are unavailable (e.g., in CI).

When HEADLESS_MODE=true, forward --headless to the compound-capture invocation (e.g., skill: soleur:compound-capture --headless).

Phase 0: Setup

Load project conventions:

# Load project conventions
if [[ -f "CLAUDE.md" ]]; then
  cat CLAUDE.md
fi

Read CLAUDE.md if it exists - apply project conventions during documentation.

Phase 0.5: Session Error Inventory (MANDATORY)

HARD RULE: Before writing any learning, enumerate ALL errors encountered in this session. Output a numbered list to the user. This step cannot be skipped even if the session felt clean.

Check for session-state.md: Run git branch --show-current. If on a feat-* branch, check if knowledge-base/project/specs/feat-<name>/session-state.md exists. If it does, read it and include any forwarded errors from ### Errors in the inventory. These errors occurred in preceding pipeline phases (e.g., plan+deepen subagent) whose context was compacted.

Include:

• Errors forwarded from session-state.md (if present)
• Skill or command not found errors (e.g., wrong plugin namespace)
• Wrong file paths, directories, or branch confusion
• Failed bash commands or unexpected exit codes
• API errors or unexpected responses
• Wrong assumptions that required backtracking
• Tools or agents that returned errors
• Permission denials or hook rejections

If genuinely no errors occurred (including no forwarded errors), output: "Session error inventory: none detected."

This list feeds directly into the Session Errors section of the learning document. Every item on this list MUST appear in the final output unless the user explicitly excludes it.

FAILURE MODE THIS PREVENTS: Compound runs in pipeline mode, the model judges the session as "clean," and silently drops errors that happened earlier in the conversation (e.g., a skill-not-found error from one-shot Step 1 gets omitted because compound focuses only on the main implementation task).

Recurring-vs-One-Off Tech-Debt Triage (MANDATORY)

HARD RULE: Immediately after the Session Error Inventory, classify EVERY inventory item (plus any non-error friction the session hit — a flaky gate, a slow manual workaround, a surprising tool behavior) as recurring or one-off. Do not skip this even when the inventory is short. The operator should never have to ask "are there any of these we could fix now to avoid stumbling on them again?" — this step answers it by default, separating genuine recurring tech debt from one-offs.

For each item, decide:

• recurring — the same class will bite again on a future session/PR (a flaky blocking gate, a missing guard, a foot-gun in a shared script, an inaccurate comment that misleads the next reader). Then pick a disposition:
  • fix-now-inline — ≤30 lines AND ≤2 files AND in a subsystem this PR already touches → fold the fix into THIS work. (The first two conjuncts mirror the cost-of-filing gate in review/SKILL.md §5; the subsystem clause is the scope-discipline overlay — a different-subsystem defect is file-tracked, never inlined here.)
  • file-tracked — larger, a different subsystem, or security/infra-sensitive → file a deferred-scope-out GitHub issue via the review/SKILL.md §5 mechanics (cost-of-filing + net-issue-flow: consolidate sibling follow-ups into one tracker; do not net-grow the backlog for trivia). A discovered defect in a DIFFERENT subsystem MUST stay its own issue and PR — never bundle it into an unrelated feature branch (scope discipline).
• one-off — a typo, a transient flake that already self-resolved, or a context-specific misstep with no recurrence vector. Note it in the learning's Session Errors and move on (no fix, no issue).

Output a short triage table (item | recurring? | disposition) to the user before writing the learning. fix-now-inline items feed the Error-to-Workflow Feedback step below; file-tracked items file directly via the review §5 mechanics above; and any recurring item that is a missing workflow rule (not a subsystem bug) additionally feeds Constitution Promotion / Route-to-Definition. Why: a flaky blocking gate left untriaged re-flakes for every future PR in that path — surfacing the recurring-vs-one-off split by default (instead of on request) turns each session's friction into a fix rather than shelf-ware.

Post-Documentation Verification Gate (MANDATORY)

After the learning file is written (by compound-capture Step 6), read it back and verify:

1. If Phase 0.5 produced a non-empty error inventory, the learning file MUST contain a ## Session Errors section with at least as many items as the inventory.
2. For each session error, the learning MUST include a **Prevention:** line proposing how to avoid it in future sessions.
3. If the verification fails (section missing or item count mismatch), append the missing errors to the learning file immediately. Do not proceed to Constitution Promotion until the learning is complete.

This gate closes the gap where errors were enumerated in conversation but never made it into the persisted document.

Error-to-Workflow Feedback (MANDATORY)

After verifying session errors are in the learning, determine if any error warrants a workflow change. For each session error, ask: "Could a rule, hook, or skill instruction have prevented this?"

• If yes, produce a proposal in the same format as Phase 1.5 Deviation Analyst (rule text + enforcement tier) and feed it into Constitution Promotion alongside any deviation proposals.
• If no (the error was a one-off or already covered by existing rules), skip.

This ensures session errors don't just get documented — they feed back into the rules and definitions that govern future sessions. The goal is a closed loop: error happens → gets documented → workflow changes → error cannot recur.

Execution Strategy: Parallel Subagents

This command launches multiple specialized subagents IN PARALLEL to maximize efficiency:

1. Context Analyzer (Parallel)

• Extracts conversation history
• Identifies problem type, component, symptoms
• Validates against solution schema
• Returns: YAML frontmatter skeleton

2. Solution Extractor (Parallel)

• Analyzes all investigation steps
• Identifies root cause
• Extracts working solution with code examples
• Returns: Solution content block

3. Related Docs Finder (Parallel)

• Searches knowledge-base/project/learnings/ for related documentation
• Identifies cross-references and links
• Finds related GitHub issues
• Returns: Links and relationships

4. Prevention Strategist (Parallel)

• Develops prevention strategies
• Creates best practices guidance
• Generates test cases if applicable
• Returns: Prevention/testing content

5. Documentation Writer (Parallel)

• Determines optimal knowledge-base/project/learnings/ category
• Validates category against schema
• Suggests filename based on slug
• Assembles complete markdown file
• Validates YAML frontmatter
• Formats content for readability
• Creates the file in correct location

6. Optional: Specialized Agent Invocation (Post-Documentation)

Based on problem type detected, automatically invoke applicable agents:

• performance_issue --> performance-oracle
• security_issue --> security-sentinel
• database_issue --> data-integrity-guardian
• Any code-heavy issue --> kieran-rails-reviewer + code-simplicity-reviewer

Phase 1.5: Deviation Analyst (Sequential)

After all parallel subagents complete and before Constitution Promotion, scan the session for workflow deviations against hard rules. This phase runs sequentially (not as a parallel subagent) to respect the max-5 parallel subagent limit.

Purpose

Close the gap between "we learned X" and "X is now enforced." The project has proven that hooks beat documentation — all existing PreToolUse hooks were added after prose rules failed. This phase detects deviations and proposes the strongest viable enforcement.

Procedure

1. Gather rules. Read AGENTS.md and extract only ## Hard Rules and ## Workflow Gates items (Always/Never). Skip Prefer rules — they are advisory and flagging them adds noise.

2. Gather session evidence. Two sources:

   • session-state.md (if present): read knowledge-base/project/specs/feat-<name>/session-state.md for forwarded errors from preceding pipeline phases (pre-compaction deviations)
   • Current context: scan the conversation for post-compaction actions — tool calls, command outputs, file edits

3. Detect deviations. For each hard rule, check if session evidence shows a violation. Common examples:

   • Editing files in main repo when a worktree is active
   • Committing directly to main
   • Running git stash in a worktree
   • Skipping compound before commit
   • Treating a failed command as success
   • Manual browser steps in prose output: Scan all text output (summaries, handoffs, "next steps" lists) for browser tasks labeled as manual without a preceding Playwright MCP attempt. Phrases like "set up X in the browser", "go to the portal and configure", "manually create an account" are violations of the Playwright-first rule unless the session log shows a mcp__plugin_playwright_playwright__browser_navigate call for that task. This catches laziness in handoff text that hooks cannot detect.

3.5. Ingest recent hook incidents. Read .claude/.rule-incidents.jsonl if present (gitignored single-file log written by .claude/hooks/lib/incidents.sh). Filter to events emitted since the session started (use the earliest timestamp in the session log, or the last 30 minutes if no anchor is available). Filter to event_type ∈ {deny, bypass} AND ignore lines where error is set — the latter are telemetry-drop sentinels (issue #3509), not deviation evidence. Treat each recent deny and bypass as evidence for the Deviation Analyst — denies confirm a hook caught a violation; bypasses signal a rule the user actively skipped. Per plan ADR-1, this step does NOT mutate any learning's frontmatter — counter aggregation lives exclusively in knowledge-base/project/rule-metrics.json (written weekly by the aggregator). If the file is absent or empty, note "no recent incidents" and continue.

4. Propose enforcement. For each detected deviation, first check if an existing PreToolUse hook already covers it by scanning .claude/hooks/*.sh comment headers. If a hook already enforces the rule, note "already hook-enforced" and skip the proposal. If no hook covers it, propose enforcement following the hierarchy:

   • PreToolUse hook (preferred) — mechanical prevention, cannot be bypassed
   • Skill instruction — checked when skill runs, can be overridden
   • Prose rule (last resort) — requires agent compliance, weakest enforcement

5. Format output. For each deviation, produce:

   ### Deviation: [short description]
   - **Rule violated:** [exact text from AGENTS.md or constitution.md]
   - **Evidence:** [what happened in the session]
   - **Existing enforcement:** [hook name if already covered, or "none"]
   - **Proposed enforcement:** [hook/skill_instruction/prose_rule]
   

   For hook proposals, include an inline draft script following .claude/hooks/ conventions:

   #!/usr/bin/env bash
   # PreToolUse hook: [what it blocks]
   # Source rule: [AGENTS.md or constitution.md reference]
   set -euo pipefail
   INPUT=$(cat)
   # [detection logic]
   # If violation detected:
   # jq -n '{ hookSpecificOutput: { permissionDecision: "deny", permissionDecisionReason: "BLOCKED: [reason]" } }'
   

6. Feed into learning document. For each detected deviation, add it to the learning file's ## Session Errors section (if not already present from Phase 0.5). Format: **[description]** — Recovery: [what fixed it] — Prevention: [proposed enforcement]. This ensures workflow violations are documented in the learning, not just proposed as hooks.

7. Feed into Constitution Promotion. Present each deviation to the user via the existing Accept/Skip/Edit gate in the Constitution Promotion section below. Accepted hook proposals should be manually copied to .claude/hooks/ after testing — never auto-install.

8. Rule budget count. After deviation analysis, measure both rule count and byte size of always-loaded AGENTS.md:

   Commit-gate: lint-agents-rule-budget.py is the authoritative pre-commit reject (warns at B_ALWAYS ≥ 20000, rejects at > 22000, rejects any rule body > 600 B); the step 8 output below is advisory-only.

   Emit rule-application telemetry (records that the byte-cap / why-single-line policy ran — see AGENTS.md cq-agents-md-why-single-line):

   source "$(git rev-parse --show-toplevel)/.claude/hooks/lib/incidents.sh" && \
     emit_incident cq-agents-md-why-single-line applied \
     'AGENTS.md rules cap at ~600 bytes; `**Why:**` is o'
   

   • Per-file bytes:
     • B_INDEX=$(wc -c < AGENTS.md) — index, loaded every turn via @AGENTS.md
     • B_CORE=$(wc -c < AGENTS.core.md 2>/dev/null || echo 0) — sidecar injected on every SessionStart
   • B_ALWAYS=$((B_INDEX + B_CORE)) — always-loaded payload
   • B_TOTAL=$(cat AGENTS.md AGENTS.core.md AGENTS.docs.md AGENTS.rest.md 2>/dev/null | wc -c) — full registry (informational)
   • Rules: A=$(grep -h '^- ' AGENTS*.md 2>/dev/null | wc -l)
   • Longest: L=$(grep -h '^- ' AGENTS*.md 2>/dev/null | awk '{print length}' | sort -n | tail -1)
   • Constitution: C=$(grep -c '^- ' knowledge-base/project/constitution.md 2>/dev/null) (tracked separately)

   Output:

   Rule budget:
     index (always-loaded):  B_INDEX bytes
     core (always-loaded):   B_CORE bytes
     always-loaded total:    B_ALWAYS bytes (warn > 18000 / critical > 22000)
     registry total:         B_TOTAL bytes / A rules (longest rule: L bytes)
     constitution.md:        C rules
   

   Append warnings:

   • If B_ALWAYS > 18000: "[WARNING] always-loaded payload (B_ALWAYS/18000) exceeded — apply the placement gate (see Route Learning to Definition) and discoverability litmus (wg-every-session-error-must-produce-either) before adding any new rule; already-enforced and domain-scoped insights MUST route to a skill/agent, NOT AGENTS.core.md; consider retiring an existing rule via scripts/retired-rule-ids.txt."
   • If B_ALWAYS > 22000: "[CRITICAL] always-loaded payload exceeds harness performance threshold (22k) — shrink required before next rule; demote wg-* class-specific rules from AGENTS.core.md to AGENTS.rest.md (per CPO sign-off PR #3496, only wg-* may be demoted — never hr-*). When trimming **Why:** lines to fit, preserve per-issue mechanism labels (text after each #N); strip redundant prose only. Correct: Why: #2618 per-command-ack; #2880 non-interactive exec.Over-trimmed:Why: #2618; #2880.(loses the per-issue mechanism distinction that downstream readers use to map a rule to its triggering incident class). Before demoting any wg-*, verify loader-class fit:sed -n '88,115p' .claude/hooks/session-rules-loader.sh — if the rule fires on docs-only sessions but AGENTS.rest.md does not load on docs-only, KEEP in core."
   • If L > 600: "[WARNING] longest rule is L bytes — cap per-rule length at ~600 (see cq-agents-md-why-single-line) by moving context to learning files."
   • If A > 115: "[ADVISORY] rule count (A/115) — bytes-first policy per cq-agents-md-why-single-line; count is informational."
   • If C > 300: "[WARNING] constitution.md is large (C/300) — consider migrating narrow rules to skill/agent instructions."

   B_TOTAL is informational only — the per-turn cost is B_INDEX, the per-session-first-turn cost is B_ALWAYS; cross-class sidecars (docs / rest) add to first-turn cost when their class fires but do not load every turn.

   Additionally, if the repo has a rule-metrics aggregator at ./scripts/rule-metrics-aggregate.sh, run it in --dry-run mode and parse summary.rules_unused_over_8w. Do not fail the phase if the aggregator is missing, but do NOT silently swallow an aggregator crash — a stderr line tells the reader why the hint is absent:

   if [[ -x ./scripts/rule-metrics-aggregate.sh ]]; then
     if unused=$(bash ./scripts/rule-metrics-aggregate.sh --dry-run 2>/dev/null | jq -r '.summary.rules_unused_over_8w // "unknown"' 2>/dev/null); then
       if [[ -n "$unused" && "$unused" != "0" && "$unused" != "unknown" ]]; then
         echo "[INFO] $unused rules have zero hits over 8 weeks. Run /soleur:sync rule-prune to surface pruning candidates."
       fi
     else
       echo "[WARN] rule-metrics-aggregate.sh --dry-run failed; skipping unused-rules hint." >&2
     fi
   fi
   

Empty Case

If no deviations are detected, output: "Deviation Analyst: no violations found." followed by the rule budget count from step 8, then proceed to Phase 1.6.

Phase 1.6: Token-Efficiency Analysis (sequential, advisory)

Run the cost-efficiency report:

bash "$(git rev-parse --show-toplevel)/plugins/soleur/skills/compound/scripts/token-efficiency-report.sh"

Prints top-3 cost table; emits te-* warn to .claude/.rule-incidents.jsonl on outliers (rolled up into knowledge-base/project/rule-metrics.json by weekly cron). Proposals route through Phase 1.5 step 7's gate.

Rubric

• Floor: wc -c AGENTS.md × 25 turns.
• Payload: SKILL.md bytes from .skill-invocations.jsonl (session_id).
• Envelopes: total_tokens from .session-tokens.jsonl (R6 self-exclusion).
• Lines: git diff --shortstat. Skip <50.
• Triggers: subagent >100k → te-subagent-overshoot; payload >200k → te-skill-payload-floor; ratio >2k/line → te-agents-md-turn-cost (gated, #3497).
• Budget: ≤1.5k tokens/fire (script bash-exec).

Sharp Edges

Advisory. Only large outliers (subagent >100k OR payload >200k) warrant follow-up. Activation lag: PostToolUse hook fires on next session restart post-merge.

Knowledge Base Integration

If knowledge-base/ directory exists, compound saves learnings there and offers constitution promotion:

Cross-Session Promotion Loop (Layer 2)

A weekly cron (.github/workflows/scheduled-compound-promote.yml) consumes accumulated learnings and proposes skill or AGENTS.core.md edits via draft PR when N=5 learnings cluster around the same root cause. Default OFF; opt in via knowledge-base/project/promotion-config.yml. See knowledge-base/engineering/operations/runbooks/compound-promote-runbook.md. Issue: #2720.

Save Learning to Knowledge Base

If knowledge-base/ directory exists, save the learning file to knowledge-base/project/learnings/YYYY-MM-DD-<topic>.md (using today's date). Otherwise, fall back to knowledge-base/project/learnings/<category>/<topic>.md.

Learning format for knowledge-base/project/learnings/:

# Learning: [topic]

## Problem
[What we encountered]

## Solution
[How we solved it]

## Key Insight
[The generalizable lesson]

## Tags
category: [category]
module: [module]

Constitution Promotion (Manual or Auto)

HARD RULE: This phase MUST run even when compound is invoked inside an automated pipeline (one-shot, ship). The model has historically rationalized skipping this as "pipeline mode optimization" -- that is a protocol violation. Constitution promotion and route-to-definition are the phases that prevent repeated mistakes across sessions. If the pipeline is time-constrained, present proposals with a 5-second timeout per item, but never skip entirely.

Headless mode: If HEADLESS_MODE=true, auto-promote using LLM judgment. Review recent learnings, determine if any warrant constitution promotion, select the domain and category using LLM judgment, generate the principle text, and check for duplicates via substring match against existing rules in constitution.md. Skip any principle that is already covered. Append non-duplicate principles and commit. Do not prompt the user. For deviation analyst proposals, auto-accept hook proposals that have clear rule-to-hook mappings and skip ambiguous ones.

Interactive mode: After saving the learning, present two categories of proposals:

1. Deviation Analyst proposals (if any): If Phase 1.5 produced deviations, present each one with Accept/Skip/Edit. For accepted hook proposals, display the draft script and instruct the user to manually copy it to .claude/hooks/ after testing. For accepted skill instruction or prose rule proposals, apply the edit to the target file.

2. Constitution promotion: Prompt the user:

Question: "Promote anything to constitution?"

If user says yes:

1. Show recent learnings (last 5 from knowledge-base/project/learnings/)
2. User selects which learning to promote
3. Ask: "Which domain? (Code Style / Architecture / Testing)"
4. Ask: "Which category? (Always / Never / Prefer)"
5. User writes the principle (one line, actionable)
6. Append to knowledge-base/project/constitution.md under the correct section
7. Commit: git commit -m "constitution: add <domain> <category> principle"

If user says no: Continue to next step

Route Learning to Definition

HARD RULE: This phase MUST run even in automated pipelines. See constitution promotion rule above.

After constitution promotion, compound routes the captured learning to the skill, agent, or command definition that was active in the session. This feeds insights back into the instructions that directly govern behavior, preventing repeated mistakes.

AGENTS.md placement gate (mandatory). Before proposing any edit that targets AGENTS.md, classify each insight. These placement classes are distinct from rule-audit.sh's enforcement tiers (hooks/AGENTS.md/constitution/agents/skills) — they govern where a new rule lives, not the enforcement layer count.

• Already-enforced: A hook or skill step already prevents the violation. Action: ensure the target skill/hook carries the Why; do NOT add to AGENTS.md. If an equivalent AGENTS.md rule already exists, collapse it to a one-line pointer ([<id>] [skill-enforced: <skill> <step>]. Full rule: <path>) and append the full body to the skill.
• Domain-scoped: The violation only happens inside a specific skill, tool, or file pattern (tests, Terraform, CF, Playwright, Pencil, CI workflows, Next.js route files, content/docs). Action: edit the owning skill/agent/reference file. AGENTS.md is OUT OF SCOPE regardless of impact.
• Cross-cutting session invariant: The violation can happen on any turn without a specific trigger (e.g., blast-radius safety, silent-failure traps that span every code path, environment constraints loaded by every session). Only these qualify for AGENTS.md.

Routing mechanics:

1. Detect which skills, agents, or commands were invoked in this conversation. Also check session-state.md ### Components Invoked for components from preceding pipeline phases.

2. Route two categories of insights:

   • Solution insight: The main learning (what was solved and how). Classify with the placement gate above, then propose a one-line bullet edit to the target file.
   • Error prevention: For each session error that could have been prevented by a skill instruction, classify with the placement gate, then propose a one-line bullet to the target. Example: if a plan skill prescribed wrong paths, add a bullet to the plan skill's Sharp Edges saying "Verify relative paths by tracing each ../ step before prescribing them."

3. Default action (interactive and headless): Apply the edit directly to the target skill/agent/AGENTS.md file. Always use worktree-absolute paths (<worktree-root>/plugins/soleur/skills/<skill>/SKILL.md) for Edit/Write calls — never ../../plugins/... relatives from inside a worktree, which escape the worktree and resolve to the bare repo root where tracked files exist as stale synced copies. Verify after the edit with git status --short in the worktree: if the expected file is not listed as modified, the edit landed outside the working tree and must be re-applied. Commit with skill: route <basename> <summary>. Sanitize <basename> and <summary> before interpolation — BASENAME=$(basename "$TARGET" | tr -cd '[:alnum:]._-') — or pass the message via a heredoc (git commit -m "$(cat <<EOF\nskill: route ...\nEOF\n)") so backticks or $(...) in a learning-file-derived basename cannot command-substitute. The edit surface is BOUNDED per file: each affected file gets a single bullet-point append, a single Sharp Edges entry, or a ≤3-line instruction clarification. Multi-file edits are allowed when the insight is convergent — one rule with parallel per-skill bullets (e.g., "when an enum-gate exists, plan must enumerate AND review must verify"). Each file's surface still has to satisfy the bounded-surface budget on its own. Edits that change existing bullet semantics or modify AGENTS.md rule wording remain OUT OF SCOPE for direct edit — file an issue instead.

4. File-issue exception: File a GitHub issue when the edit meets one of: contested-design (competing valid approaches), agents-md-semantic-change (modifies existing rule text), tier-ambiguous (insight straddles Tier 2 and Tier 3 and a reviewer's judgment is load-bearing), divergent multi-file (two or more unrelated edits across skills that don't share a single insight — these warrant separate scrutiny), or any single file's edit exceeds the bounded-surface budget (>1 bullet, >1 Sharp Edges entry, or >3 instruction lines). "Cross-skill" alone is NOT a trigger — a convergent insight with parallel per-skill bullets applies inline. Title: compound: route-to-definition proposal for <target-basename>. Body: proposed edit text + target path + source learning path + ## Scope-Out Justification naming the criterion. Flags: --label deferred-scope-out --milestone "Post-MVP / Later".

5. Interactive confirmation for direct edits: If HEADLESS_MODE is unset, show the proposed diff and ask Accept/Skip/Edit-then-Accept before committing. In headless mode, apply directly without prompting — the bounded surface (single bullet append) is safe without per-edit approval.

See compound-capture Step 8 for the full flow.

Graceful degradation: Skips if plugins/soleur/ does not exist or no components detected in the session.

Managing Learnings (Update/Archive/Delete)

Update an existing learning: Read the file in knowledge-base/project/learnings/, apply changes, and commit with git commit -m "learning: update <topic>".

Archive an outdated learning: Move it to knowledge-base/project/learnings/archive/: mkdir -p knowledge-base/project/learnings/archive && git add knowledge-base/project/learnings/<category>/<file>.md && git mv knowledge-base/project/learnings/<category>/<file>.md knowledge-base/project/learnings/archive/. The git add ensures the file is tracked before git mv.Commit with git commit -m "learning: archive <topic>".

Delete a learning: Only with user confirmation. git rm knowledge-base/project/learnings/<category>/<file>.md and commit.

Managing Constitution Rules (Edit/Remove)

Edit a rule: Read knowledge-base/project/constitution.md, find the rule, modify it, commit with git commit -m "constitution: update <domain> <category> rule".

Remove a rule: Read knowledge-base/project/constitution.md, remove the bullet point, commit with git commit -m "constitution: remove <domain> <category> rule".

Automatic Consolidation & Archival (feature branches)

On feature branches (feat-*, feat/*, fix-*, or fix/*), consolidation runs automatically after the learning is documented and before the decision menu. This ensures artifacts are always cleaned up as part of the standard compound flow, rather than relying on a manual menu choice.

The automatic consolidation:

1. Discovers artifacts -- extracts the feature slug by stripping feat/, feat-, fix/, or fix- prefix from the branch name, then globs knowledge-base/project/{brainstorms,plans}/*<slug>* and knowledge-base/project/specs/feat-<slug>/ (excluding */archive/)
2. Extracts knowledge -- a single agent reads all artifacts and proposes updates to constitution.md, component docs, and project README.md
3. Approval flow -- Headless mode: auto-accept all proposals (idempotency still checked via substring match). Interactive mode: proposals presented one at a time with Accept/Skip/Edit; idempotency checked via substring match
4. Archives sources -- runs bash ./plugins/soleur/skills/archive-kb/scripts/archive-kb.sh to move all discovered artifacts to archive/ subdirectories via git mv with YYYYMMDD-HHMMSS timestamp prefix. Headless mode: auto-confirm archival without prompting
5. Single commit -- project edits and archival moves committed together for clean git revert

If no artifacts are found for the feature slug, consolidation is skipped silently. See the compound-capture skill for full implementation details.

Worktree Cleanup (Manual)

Headless mode: If HEADLESS_MODE=true, skip worktree cleanup entirely (cleanup-merged handles this post-merge).

Interactive mode: At the end, if on a feature branch:

Question: "Feature complete? Clean up worktree?"

If user says yes:

git worktree remove .worktrees/feat-<name>

If user says no: Done

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
• Session errors: Process mistakes, failed commands, and wrong approaches from the session
• 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: knowledge-base/project/learnings/[category]/[filename].md

Categories auto-detected from problem:

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

Success Output

✓ Parallel documentation generation complete

Primary Subagent Results:
  ✓ Context Analyzer: Identified performance_issue in brief_system
  ✓ Solution Extractor: Extracted 3 code fixes
  ✓ Related Docs Finder: Found 2 related issues
  ✓ Prevention Strategist: Generated test cases
  ✓ Documentation Writer: Classified to performance-issues/, created complete markdown

Specialized Agent Reviews (Auto-Triggered):
  ✓ performance-oracle: Validated query optimization approach
  ✓ kieran-rails-reviewer: Code examples meet Rails standards
  ✓ code-simplicity-reviewer: Solution is appropriately minimal
  ✓ every-style-editor: Documentation style verified

File created:
- knowledge-base/project/learnings/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?  (Headless mode: auto-selects "Continue workflow")
1. Continue workflow (recommended)
2. Add to Required Reading
3. Link related documentation
4. Update other references
5. View documentation
6. Other

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 → knowledge-base/project/learnings/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 skill: soleur:compound [context] to document immediately without waiting for auto-detection. </manual_override> </auto_invoke>

Routes To

compound-capture skill

Applicable Specialized Agents

Based on problem type, these agents can enhance documentation:

Code Quality & Review

• kieran-rails-reviewer: Reviews code examples for Rails best practices
• code-simplicity-reviewer: Ensures solution code is minimal and clear
• pattern-recognition-specialist: Identifies anti-patterns or repeating issues

Specific Domain Experts

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

Enhancement & Documentation

• best-practices-researcher: Enriches solution with industry best practices
• every-style-editor: Reviews documentation style and clarity
• framework-docs-researcher: Links to Rails/gem documentation references

When to Invoke

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

Related Commands

• /research [topic] - Deep investigation (searches knowledge-base/project/learnings/ for patterns)
• soleur:plan skill - Planning workflow (references documented solutions)