/distill
Surface patterns in accumulated knowledge. Propose what to keep, promote, compact, or dismiss. The human decides.
Salvage extracts learning from a single session; distill curates the corpus those extractions build. 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 settled before moving forward
- Search results feel noisy -
oh_search_contextreturning too much loosely-related content - Similar learnings keep appearing -
/salvagekeeps 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 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
LLMs find patterns; humans decide what matters. Auto-promotion is never correct. A theme 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
Filtering (RNA): Pass outcome ID, phase tag, or recency window to oh_search_context to narrow the corpus when only a domain slice needs curation.
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? Exclude generic advice unless it demonstrably changes decisions in this context.
Corpus scope (RNA): Call oh_search_context broadly. Cluster 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, generic, or overly context-specific (candidates for dismissal)
Step 3: Present for Human Review
Present each candidate group with four possible actions:
**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; human approves the content (editing as needed), then an agent writes the file:
---
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, generic, 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.
- New metis entries →
.oh/metis/<slug>.md - Guardrail candidates → draft for human to write to
.oh/guardrails/<slug>.md - Compactions → new merged entry + note which originals can be removed
- Session file compaction → offer to remove stale planning artifacts, keep settled decisions as brief anchors
Output Format
## 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 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.
- Prefer situated metis over generic advice. If a note does not add local leverage beyond what a foundation model would already know, treat it as dismissal or compaction candidate.
- 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.
Position in Framework
Comes after: Multiple sessions of /salvage or /execute that accumulated metis, or end of any session with learnings worth capturing. Leads to: Cleaner search results. Guardrail candidates for human authoring. A compacted session file for the next session. Relationship to /salvage: Salvage is per-session extraction (especially from failure). Distill is corpus-level curation. They're complementary.