Individual skill
code-distill
On-demand pattern extraction from a specific GitHub codebase, given a focused query — "how does shadcn/ui implement the design system", "how does opencode use effect-ts", "how does base-ui handle composition" — when no pre-distilled static rule pack exists yet. Distills the generic pattern-extraction moves — classify the query before grepping (component / composition / state / effect / error / build / routing), grep before reading whole files, treat tests and examples/ as canonical intent, follow imports outward for the public surface, follow usages inward for variants, filter boilerplate / legacy / test scaffolding to surface load-bearing code, and capture findings to /knowledge/libraries/ for reuse. Dynamic light sibling of static code-atlas skills (opencode-ts, openai-codex-rust-patterns, nextjs-ppr-patterns). Triggers on "show me how <library> implements X", "find the <pattern> in <repo>", "distill <library>", and ad-hoc /distill-<library>-style invocations.
skills/.experimental/code-distill/SKILL.md
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Observed agent integration: codex
- design Derived category
- testing Derived category
- engineering Derived category
- design-interface Derived category
- engineering-web Derived category
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AGENTS.mddoc · 2,948 bytesSKILL.mdcore · 10,588 bytesSKILL.mdcore · 10,588 bytesSKILL.mdcore · 10,588 bytesSKILL.mdcore · 10,588 bytesSKILL.mdcore · 10,588 bytesSKILL.mdcore · 10,588 bytesmetadata.jsonasset · 1,332 bytesreferences/_sections.mddoc · 3,658 bytesreferences/capture-registry-record.mddoc · 4,477 bytesreferences/filter-load-bearing.mddoc · 3,732 bytesreferences/find-bounded-knowledge-read.mddoc · 3,193 bytesreferences/find-classify-query.mddoc · 3,310 bytesreferences/find-grep-before-read.mddoc · 2,749 bytesreferences/find-tests-show-intent.mddoc · 3,209 bytesreferences/trace-imports-outward.mddoc · 3,146 bytesreferences/trace-usages-inward.mddoc · 3,397 bytes
Read excerpt
# 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/`](../../../knowledge/)), 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