Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tianhanz/ears --skill distillgit clone --depth 1 https://github.com/tianhanz/earsWrote 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/tianhanz/ears/distill)<a href="https://agentmods.dev/skills/tianhanz/ears/distill"><img src="https://agentmods.dev/badge/skills/tianhanz/ears/distill.svg" alt="Measured on agentmods" 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 | $0.00133 | $0.00696 |
| Opus 5 | $0.00067 | $0.00348 |
| Sonnet 5 | $0.00027 | $0.00139 |
| Haiku 4.5 | $0.00013 | $0.00070 |
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 7d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/distill — Pattern Extraction from Traces
Extract patterns from trace.md files into reusable knowledge files. Turns scattered experience into distilled, actionable knowledge.
Trigger
User mentions: "distill", "extract patterns", "what have we learned", "summarize traces", "knowledge distillation". Also use after completing a batch of reproductions to extract emergent knowledge.
Workflow
Step 1 — Gather traces
Search for trace.md files across the project:
find . -name 'trace.md' -not -path './.git/*' | sort
Read each trace file. Look for:
- Errors and their root causes
- Surprising results or unexpected behaviors
- Parameter choices that worked (or didn't)
- Workflow decisions and their outcomes
Step 2 — Identify patterns
A pattern is a recurring observation that appears in 2+ traces. Tag each pattern:
[N=<count>, <weak|moderate|strong>]
weak: 2 occurrences, may be coincidencemoderate: 3-4 occurrences with consistent mechanismstrong: 5+ occurrences, well-understood mechanism
Quantitative patterns with specific thresholds beat qualitative summaries. Example:
- Good: "Grid resolution of 15+ points per characteristic length scale is needed for 1% accuracy [N=7, strong]"
- Bad: "Use fine grids [N=7, strong]"
Step 3 — Write concept file
Create knowledge/<concept>.md with:
# <Concept Name>
## Established Patterns [N >= 3]
- Pattern 1 [N=5, strong] — Source: trace1, trace2, ...
- Pattern 2 [N=3, moderate] — Source: ...
## Emerging Patterns [N = 2]
- Pattern 3 [N=2, weak] — Source: ...
## Open Questions
- Question that traces surface but don't answer
## Action Items
- [ ] Concrete steps to validate or apply these patterns
Step 4 — Check for promotion
If any pattern reaches [N >= 5, strong], it is a candidate for promotion to CLAUDE.md or other project-level documentation. Flag these to the user — only the project owner decides what goes into the foreground.
Anti-Patterns
- Don't distill from a single trace. Wait for patterns to emerge from 2+ traces.
- Don't write platitudes ("be careful with parameters"). Be specific and quantitative.
- Don't duplicate existing knowledge. Check concept files before writing.
- Don't promote prematurely. A pattern at N=2 is weak — it may be coincidence.
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.
- 7d ago First seen · 76 lines · 133 tokens per session scan A f77c66cdf30d
distill is a skill published in the GitHub repository tianhanz/ears (5 stars, last pushed 4mo ago), licensed MIT. It adds 133 tokens to every session and 696 once invoked, about $0.0007 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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