Borrowing it
Nothing to install: this file belongs to open-horizon-labs/repo-native-alignment. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/open-horizon-labs/repo-native-alignment/main/.agents/skills/distill/SKILL.mdgit clone --depth 1 https://github.com/open-horizon-labs/repo-native-alignmentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/distill)<a href="https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/distill"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/distill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/open-horizon-labs/repo-native-alignment/distill"><img src="https://agentmods.dev/badge/skills/open-horizon-labs/repo-native-alignment/distill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.01803 |
| Opus 5 | $0.00023 | $0.00901 |
| Sonnet 5 | $0.00009 | $0.00361 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
distill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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
searchreturns 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 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?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 170 lines · 47 tokens per session scan A 0cb991d68dc5
distill is a skill published in the GitHub repository open-horizon-labs/repo-native-alignment (5 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 1,803 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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