Code Distill

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 running code-distill against 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?" use docs-search. This skill is for source code, not docs.

How to Use

The four categories are orthogonal moves. Match the symptom to the move:

For category overviews and ordering rationale, see references/_sections.md.

Rule Categories

Quick Reference

1. Find

• find-classify-query — Classify the query (component / composition / state / effect / error / build / routing) before grepping; the classification picks the grep targets and folder hints
• find-grep-before-read — Grep narrowly first to pinpoint files; never start by reading whole files
• find-tests-show-intent — Tests, examples/, and e2e/ dirs are the authors' canonical demonstration of intent; consult them before forming a hypothesis from prose
• find-bounded-knowledge-read — Filename is the index: read only knowledge/libraries/<slug>.md, never scan the dir; bounds per-invocation token cost regardless of knowledge-store size

2. Trace

• trace-imports-outward — From the implementation file, follow imports outward to discover the pattern's public surface and dependencies
• trace-usages-inward — From the public surface, find call sites inside the same repo to see variants, edge cases, and how the pattern evolves

3. Filter

• filter-load-bearing — Cut boilerplate (re-exports, builders, helpers), legacy paths (deprecated dirs, _old/, legacy/), and test scaffolding from the answer; only the load-bearing pattern is the answer

4. Capture

• capture-registry-record — At end of a successful session, write the code: section of knowledge/libraries/<library>.md (repo URL, branch, SHA, folder map, naming conventions, AGENTS.md flag, samples-dir location) so the next lookup skips discovery

Knowledge Store

Per-library code topography records live in the repo-root shared knowledge graph at /knowledge/libraries/. The same files are written by docs-search — each skill owns one section (code: for this skill, docs: for docs-search) and never overwrites the other. See knowledge/README.md for the merged schema, wiki-link conventions, and the merge discipline.

The knowledge store is intentionally empty at v0.1.0. The radical-simplification recommendation that produced this skill said: do not pre-empt; add the first entry from a real lookup. If you find yourself adding a record for a repo you have not actually queried, stop — wait for the real need.

Read discipline: when the user names a library, do exactly read knowledge/libraries/<slug>.md. Never scan knowledge/libraries/ to "see what's available" — the filename is the index, and lazy access is what keeps per-invocation token cost bounded regardless of knowledge-store size.

Related Skills

• docs-search — Symmetric sibling for documentation instead of source code; same shape (methodology + shared knowledge graph); writes the docs: section of the same knowledge/libraries/<lib>.md files this skill writes the code: section of
• library-reference-distillation — Authoring playbook for full library-ref rule packs from upstream docs; the heavy docs-source sibling
• (Future) code-atlas-distillation — Not yet built. Authoring playbook for full static code-atlas skills like opencode-ts. Extract when you ship your 4th or 5th static code-atlas skill and the empirical patterns are observable.
• opencode-ts, openai-codex-rust-patterns, nextjs-ppr-patterns — Heavy curated outputs for specific libraries; use them in preference to this skill when they exist
• codebase-comprehension-algorithms — Heavy algorithmic toolkit (Leiden, MoJoFM, SBM) for full-codebase domain mapping; orthogonal to focused-query extraction
• radical-simplification — Thinking layer above this skill; this skill itself is the third instance of the "constrain → name the invariant" move applied to the recurring "N skills per X" trap

Authoring Note

These rules are load-bearing, not decorative. Each names a wrong default an agent has when reaching for unfamiliar source code: reading whole files before grepping, ignoring tests as canonical intent, conflating boilerplate with load-bearing pattern, treating each lookup as one-shot. If a rule restates something a capable model already does correctly, cut it. Coverage is proven by /dev-skill:eval on real "how does X implement Y" prompts, not by rule count. The registry grows organically as real lookups demand entries; when a library gets queried more than ~3 times, that is the signal to graduate it to a full static code-atlas skill.