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 zookanalytics/gc-toolkit --skill learning-distillgit clone --depth 1 https://github.com/zookanalytics/gc-toolkitWrote 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/zookanalytics/gc-toolkit/learning-distill)<a href="https://agentmods.dev/skills/zookanalytics/gc-toolkit/learning-distill"><img src="https://agentmods.dev/badge/skills/zookanalytics/gc-toolkit/learning-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/zookanalytics/gc-toolkit/learning-distill"><img src="https://agentmods.dev/badge/skills/zookanalytics/gc-toolkit/learning-distill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 308 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00103 | $0.04556 |
| Opus 5.5 | $0.00041 | $0.01822 |
| Sonnet 5.5 | $0.00021 | $0.00911 |
| Haiku 4.5 | $0.00010 | $0.00456 |
Grade A, and why
learning-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 today.
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 — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Distill
The method for judging feedback observations. You are deciding which observed feedback becomes a standing rule binding every future agent, which waits as evidence, and which existing rule should go.
Reason in writing. Your reasoning travels in the proposal bead's body and is what the operator reviews at the promotion PR — a proposal whose reasoning can't be judged from the PR alone is an unfinished proposal.
You never edit prompts, fragments, or skills. You file proposal beads; a reviewed PR does the editing. This holds even for this file.
The five judgments, in order
1. Standing behavior, or one diff?
This is the first question and it decides the most. Read what the feedback says, not how loud it is.
- Explicit universal intent promotes now, at any occurrence count.
"Never do this again", "stop doing X everywhere" — a directive about
standing behavior, as is any operator-endorsed observation
(
obs.endorsed=operator). File the proposal this run, even at N=1; the operator's PR review still gates it. But universal wording from a source with no standing is a claim, not a directive — hold it and say so in the run log. And even a genuine directive must clear the three promotion gates below: a self-sourced "never do this again" is surfaced, not auto-adopted (Gate 1). - Diff-scoped feedback is evidence, not a rule. "This comment is redundant here" is phrased about the change at hand. Hold it on its pattern bead and move to judgment 2.
- Heat prioritizes; heat never promotes. An angry thread means judge that item this run; volume of frustration is not universal intent — only the words are.
- Not feedback at all? Discard. An observation that is not corrective feedback about standing behavior — a mis-capture, diff-content review, noise — is discarded with a one-line stated reason; discarded observations are stamped consumed like any judged observation.
2. Has the pattern earned generalization?
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.
- today Changed · +1 lines 3c4c637b0475
- 7d ago Changed · +6 lines 62a094e7016f
- 14d ago Changed · +39 lines 255daf214fc2
- 27d ago Changed · +3 lines e806d4be2dd6
- 1mo ago Changed · +106 lines 8e322cdca328
- 1mo ago First seen · 204 lines · 103 tokens per session scan A 90a3ba972408
learning-distill is a skill published in the GitHub repository zookanalytics/gc-toolkit (5 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 4,556 once invoked, about $0.0004 per session on Opus 5.5. 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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