Distill Session

Review the current conversation to extract durable lessons and route each one to the right knowledge layer.

Step 1: Detect Context

Available destinations:

• Project CLAUDE.md / AGENTS.md — .claude/CLAUDE.md (may be a symlink to ../AGENTS.md — resolve it)
• Auto memory — The project-specific memory directory at ~/.claude/projects/<project-hash>/memory/. Read MEMORY.md there if it exists.
• Skills — Project skills at skills/ or .claude/skills/ (resolve symlinks)

Read the project CLAUDE.md/AGENTS.md and MEMORY.md. List all skill directories but do not read them yet — Step 2 needs to run first so you know what to look for.

Step 2: Scan Session for Lessons

Scan the full conversation with this priority:

1. Corrections — Where the user interrupted, said "no", "actually", "stop", "not like that", redirected, or manually fixed something Claude did wrong. Highest-value lessons.
2. Repeated guidance — Instructions the user gave more than once.
3. Skill-shaped knowledge — Domain expertise that was needed repeatedly, tool/API integration details that had to be looked up, decision frameworks that emerged for evaluating options, content templates or writing conventions that were refined, and multi-step workflows where ordering mattered. Also check: did this session create new scripts, automation, or multi-step procedures? Flag each as a potential skill candidate.
4. Preferences — Formatting, naming, style, or tool choices the user expressed.
5. Failure modes — Approaches that failed, with what worked instead.
6. Domain knowledge — Facts or conventions Claude needed but did not have.
7. Improvement opportunities — Out-of-scope improvements noticed during work: code that could be refactored, missing tests, performance issues, readability concerns, or feature ideas that were intentionally skipped to stay focused. Skipped review findings count here: when a code review (codex, PR reviewer, or evaluate-findings) identified a genuine issue but the user chose to skip it for this PR, route it as a project improvement so it isn't lost.
8. Trusted reviewer feedback — Human PR review comments that reveal project conventions, patterns, or corrections. Trusted reviewers are repo collaborators with admin or maintain roles (determine via gh api repos/{owner}/{repo}/collaborators --jq '.[] | select(.role_name == "admin" or .role_name == "maintain") | .login'). Their feedback takes precedence over other reviewers and AI bots when there are contradictions.

After scanning, read all skill SKILL.md files (they are small). This gives Step 4 full context for routing.

Step 3: Filter

Keep only lessons that are:

• Stable — likely to remain true across future sessions
• Non-obvious — Claude would not already know this
• Actionable — can be expressed as a rule or instruction
• Not already documented — absent from the files read in Step 1
• Still a concern — the issue is not already fixed by changes made in this session. If a bug was found and fixed, or a missing feature was added, future sessions will see the corrected code — they don't need a reminder about the old problem.

Discard anything session-specific, speculative, one-off, or already resolved by code changes in this session. If no lessons survive filtering, tell the user and stop.

Step 4: Route Each Lesson

Assign each surviving lesson to exactly one destination:

Destination	Criteria
Existing skill	Lesson would improve a skill's instructions, supporting files, or reference materials, add a missing edge case, correct its workflow, or refine its trigger conditions. Route to any skill whose domain covers the lesson — not just the skill worked on in this session. Prefer this over CLAUDE.md and auto memory when a relevant skill exists.
New skill	A cohesive body of knowledge emerged that deserves its own on-demand context. The test: would this knowledge be too large for a CLAUDE.md section, and should it only be loaded when relevant? See the skill categories table below.
Project CLAUDE.md / AGENTS.md	Intentional project decisions: conventions, architecture, stack choices, build setup, module boundaries.
Auto memory	Discovered knowledge: API quirks, debugging workarounds, compiler gotchas, tool pitfalls, past mistakes, user preferences.
Project improvements	Actionable improvement to existing code: refactoring, performance, reliability, readability, testing, or DX. Not a lesson — a thing to do. Route to .turbo/improvements.md via the /note-improvement skill.
No destination	Does not clearly fit any destination. Drop it. Routing a weak lesson is worse than losing it.

Skill categories:

Category	What it encodes	Example
Domain expertise	Best practices, patterns, API preferences	SwiftUI expert, Core Data guide
Tool/Service integration	API references, operations, ID formats	Paddle, Stripe, Keycloak
Decision framework	Judgment criteria, confidence levels, triage	Evaluate findings, performance audit
Content template	Writing conventions, tone, structure	Drafting, blog post, changelog
Knowledge/Research	Information discovery, schema definitions	Knowledge base, research process
Orchestrated workflow	Stateful multi-step procedures	Process ticket, process income

Splitting heuristic: When a session creates scripts or multi-step procedures, split the lesson: a brief pointer goes to CLAUDE.md (script names, purpose), and the full workflow goes to a skill. Don't collapse them into a single CLAUDE.md entry.

Tiebreakers:

• Skill vs. CLAUDE.md — prefer the skill. Skills are more discoverable, better scoped, and loaded only when relevant.
• Skill vs. auto memory — if a lesson falls within the domain of an existing skill, prefer the skill over auto memory.
• CLAUDE.md vs. auto memory — intentional decisions go to CLAUDE.md. Discovered knowledge (gotchas, workarounds, quirks) goes to auto memory.
• Lesson vs. improvement — if the item is knowledge to remember, it's a lesson. If it's work to do later, it's an improvement. They don't compete — the same session can produce both.

Step 5: Present Routing Plan

Show the user a table before making any changes:

| # | Lesson | Destination | Action |
|---|--------|-------------|--------|
| 1 | Always use X for... | Project AGENTS.md | Append to ## Conventions |
| 2 | The /create-pr skill should... | ~/.claude/skills/create-pr | Update Step 2 |
| 3 | Multi-step deploy workflow | New project skill | Create new skill |
| 4 | User prefers short commit msgs | Auto memory | Append to MEMORY.md |

For each lesson, show: concise summary, target file/skill, whether it's an append, update-in-place, or new creation.

Use AskUserQuestion to let the user approve, reject, or redirect individual lessons.

Step 6: Execute

Apply approved changes in order:

1. Updates to existing files — Read the target, find the right section, append or update in place. Match the tone and format already present.
2. Improvements — For items routed to project improvements, run /note-improvement with the summary, location, and rationale for each.
3. New skills — Enter plan mode, then run /create-skill skill for each new skill. Provide the trigger conditions and relevant context from the session.

Writing Guidelines

• Match the tone and format of the target file
• Use imperative mood and short declarative sentences
• Group related insights under a descriptive heading
• Omit rationale unless the rule would seem arbitrary without it
• Never include temporary state, in-progress work, or task-specific details
• Keep lessons generic—avoid overly concrete examples; state the rule, not the instance
• For AGENTS.md: write as agent documentation — project rules any AI agent on this repo should follow
• For auto memory: write as personal Claude notes — concise, operational, organized by topic
• For skills: follow the conventions in the existing skill collection