# Distill > Human-led curation of accumulated metis and guardrails. Surface patterns across sessions, propose what to promote, compact, or dismiss. Use after multiple sessions, before a new phase, or when search results feel noisy. - Skill: `open-horizon-labs/distill` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add open-horizon-labs/distill` - Raw SKILL.md: https://api.skillmd.com/api/skills/open-horizon-labs/distill/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: open-horizon-labs (https://skillmd.com/u/open-horizon-labs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/open-horizon-labs/distill --- # /distill Surface patterns in accumulated knowledge. Propose what to keep, promote, compact, or dismiss. The human decides. Distill is the complement to `/salvage`: salvage extracts learning from a single session; distill curates the corpus those extractions build over time. Without distill, metis accumulates but never compounds. ## When to Use Invoke `/distill` when: - **After multiple sessions** - The corpus has grown and hasn't been reviewed - **Before a new phase** - Want to know what's actually settled before moving forward - **Search results feel noisy** - RNA `search` returns too much loosely-related content - **Similar learnings keep appearing** - `/salvage` keeps extracting the same insights (the meta-signal) - **End of a successful session** - Even good sessions produce learnings worth capturing before context is lost **Use distill (not `/salvage`) when the session went well.** `/salvage` is for stopping because things went wrong. Distill is for pausing because things went right — or simply finished — and learnings are worth capturing before context is lost. **Do not use when:** You're in the middle of execution. Distill is a pause point, not a mid-flight activity. ## The Human-Led Curation Principle Distill leverages LLMs for what they're good at — pattern recognition, clustering, surfacing similar entries — while keeping judgment with the human. **LLMs do:** find recurring themes, group similar entries, surface candidates. **Humans do:** decide what matters, what's worth promoting, what's stale or context-specific. Auto-promotion is never correct. A theme proposal is not a guardrail until a human writes it. ## The Process ### Step 1: Establish Scope Decide what corpus to work with: - **Session scope** (default, no RNA needed): learnings from this conversation - **Corpus scope** (RNA available): accumulated metis across all sessions, optionally filtered by outcome, phase, or tag ### Step 2: Surface Candidates **Session scope:** Review the conversation. What was learned? What assumptions were validated or invalidated? What constraints were discovered? What would be useful to know at the start of the next session? **Corpus scope (RNA):** Call `search` broadly with `include_artifacts: true`. Cluster returned artifacts by semantic similarity. Identify: - Entries that appear together repeatedly (candidates for compaction) - Patterns across entries (candidates for guardrail promotion) - Entries that contradict each other (candidates for resolution) - Entries that are stale or overly context-specific (candidates for dismissal) ### Step 3: Present for Human Review Present each candidate group with four possible actions: ```text **Theme: [theme name]** Entries: [list with source IDs and one-line summaries] Suggested action: [Keep / Promote / Compact / Dismiss] Reason: [why this action fits] → Your call: ``` **Keep** — leave as individual metis entries, no change. **Promote** — pattern is recurring and stable enough to warrant a guardrail. Distill drafts a stub; the human reviews, edits as needed, and explicitly approves the final content. Only then may an agent write the file: ```markdown --- id: [slug] outcome: [outcome-id] severity: soft title: [one-line constraint] --- [drafted body — human refines, agent writes to .oh/guardrails/] ``` **Compact** — multiple entries say the same thing. Human approves a merged version; originals archived or deleted. **Dismiss** — stale, superseded, or so context-specific it misleads more than it helps. ### Step 4: Write Results Only write what the human approved. No auto-promotion, no auto-deletion. - Every created or modified `.oh/**/*.md` artifact must have valid YAML frontmatter in deterministic BTreeMap key order. - New metis entries → `.oh/metis/.md` after human approval. - Guardrail candidates → human reviews/edits and explicitly approves the draft; only then may the agent write `.oh/guardrails/.md`. - Compactions → write an approved merged entry with frontmatter and note which originals the human may remove. - Session compaction → preserve valid frontmatter; offer to remove stale planning artifacts while keeping settled decisions as brief anchors. ## Output Format ```markdown ## Distill Summary **Scope:** [session | corpus — filtered by: outcome/phase/tag] **Entries reviewed:** [N] ### Proposals **[Theme or entry title]** - Source(s): [file paths or conversation reference] - Suggested: [Keep / Promote to guardrail / Compact / Dismiss] - Reason: [one sentence] → Decision: [human fills this in] [repeat for each proposal] ### Results Written - [what was actually written, with file paths] ``` ## Guardrails - **Never auto-promote.** A theme proposal is not a guardrail until a human writes it. - **Never auto-delete.** Dismissal proposals require human confirmation. - **Preserve provenance.** Every proposal links to source metis IDs or conversation context. - **Phase-aware.** Corpus-mode clustering must surface phase tags — cross-phase metis often misleads. A solution-space learning is not automatically relevant in problem-space. - **Graceful with sparse corpus.** With fewer than 5 entries, surface what exists without manufacturing false patterns. Don't cluster noise. - **Metis is contextual, not universal.** What worked in one context doesn't carry everywhere. The human selects what applies; distill surfaces candidates. ## Adaptive Enhancement ### Base Skill (prompt only) Reads the current conversation. Surfaces what was learned this session. Proposes metis entries for human review and approval. Works after any completed session — the positive complement to `/salvage` (which is for drift and failure). Output: approved entries as markdown, ready to write to `.oh/metis/`. ### With .oh/ session file - Reads `.oh/.md` for session context and prior metis - Writes approved metis entries directly to `.oh/metis/` - After distill, offers to compact the session file itself: remove stale planning content, keep settled decisions as brief anchors - The compacted session file seeds future sessions more cleanly ### With RNA MCP (repo-native-alignment) Corpus mode: RNA `search` across all accumulated metis (or a filtered subset), cluster by semantic similarity, and surface candidate groups for human review. Output is PR-able: distill produces a set of proposed file writes (new guardrail stubs, compacted metis, removal notes) formatted as a markdown summary. The human creates a branch, writes the approved files, and opens a PR — or uses `/oh-plan` to create issues for each promotion. Curation becomes a collaborative act, not a solo one. **Filtering options:** include an outcome ID, phase tag, or recency window in the `search` query to narrow the corpus. Useful when only a domain slice needs curation. ## Position in Framework **Comes after:** Multiple sessions of `/salvage` or `/execute` that have accumulated metis. Or: end of any session where learnings are worth capturing before context is lost. **Leads to:** Cleaner RNA search results. Guardrail candidates for human authoring. A compacted session file that seeds the next session. **Relationship to `/salvage`:** Salvage is per-session extraction (especially from failure). Distill is corpus-level curation (independent of any single session outcome). They're complementary — salvage feeds the corpus; distill keeps it from becoming noise. ## Leads To After distill, typically: - Write approved guardrail files to `.oh/guardrails/` - Open a PR with `.oh/` changes for team review (corpus mode) - Return to `/aim` or `/problem-space` with a cleaner, more settled context --- **Remember:** Distill is not synthesis. The LLM finds patterns; you decide what matters. A corpus that accumulates without curation becomes noise. A corpus that's periodically distilled becomes situated judgment — the kind that actually improves future sessions.