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 pproenca/dot-skills --skill code-distillgit clone --depth 1 https://github.com/pproenca/dot-skillsWrote 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/pproenca/dot-skills/code-distill)<a href="https://agentmods.dev/skills/pproenca/dot-skills/code-distill"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/code-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/pproenca/dot-skills/code-distill"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/code-distill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00223 | $0.02475 |
| Opus 5 | $0.00112 | $0.01238 |
| Sonnet 5 | $0.00045 | $0.00495 |
| Haiku 4.5 | $0.00022 | $0.00248 |
Grade A, and why
code-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 5d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code-Distill — Pattern Extraction Methodology for GitHub Codebases
Methodology distillation of the generic moves an agent makes when distilling code patterns on demand from a specific GitHub codebase, given a focused query. Not a per-library skill — one skill plus a thin per-library record in the shared knowledge graph (/knowledge/libraries/), because 90% of the work is the same regardless of which repo.
This is the dynamic light sibling of your static code-atlas distillations: opencode-ts, openai-codex-rust-patterns, nextjs-ppr-patterns. Those skills are heavy curated outputs — they distilled patterns from a single repo ahead of time. This skill is the on-demand alternative: when no static skill exists for the library yet, point at the repo and let the methodology run.
When to Apply
Use this skill when:
- The user asks "how does
<library>implement<feature>?" and points at (or names) a real GitHub repo - The query is focused on a single subsystem (design system, composition, state, error handling, effects, build, routing) — not "what is this whole codebase?"
- No static code-atlas skill exists for the library yet (or the existing one is stale)
- An ad-hoc invocation looks like
/distill <library> <query>or "show me X in repo Y" in natural language - The library has too small a surface area, or too short a lifespan, to justify authoring a full static code-atlas distillation
This skill is NOT for:
- Libraries with a static code-atlas skill — use
opencode-ts,openai-codex-rust-patterns,nextjs-ppr-patterns, or other shipped per-library skills first. They are faster (already curated) and incorporate failure-gap lessons this skill cannot rediscover on demand. - Full-codebase architecture mapping — when the question is "what does this whole codebase do, by domain?", use
codebase-comprehension-algorithms. That skill is the heavy algorithmic toolkit (Leiden, MoJoFM, SBM); this skill is focused-query extraction. - Authoring a full static code-atlas skill — the methodology playbook for that authoring task is a separate (not-yet-built) skill, the code-source sibling of
library-reference-distillation. When you find yourself runningcode-distillagainst the same library more than ~3 times, that is the signal to graduate to a full static skill. - Documentation lookup — for "where do I find X in
<library>docs?" usedocs-search. This skill is for source code, not docs.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 2.9 KB
- metadata.json 1.3 KB
- references/_sections.md 3.6 KB
- references/capture-registry-record.md 4.4 KB
- references/filter-load-bearing.md 3.6 KB
- references/find-bounded-knowledge-read.md 3.1 KB
- references/find-classify-query.md 3.2 KB
- references/find-grep-before-read.md 2.7 KB
- references/find-tests-show-intent.md 3.1 KB
- references/trace-imports-outward.md 3.1 KB
- references/trace-usages-inward.md 3.3 KB
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.
- 5d ago First seen · 96 lines · 223 tokens per session scan A 73a30d7f9ff0
code-distill is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 223 tokens to every session and 2,475 once invoked, about $0.0011 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 2026-09-03.
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