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Add guidance to SKILL.md on when building a graph is (and isn't) worth it

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Evaluación

Dificultad
1/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
75/100
Tipo de issue
Documentación
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
documentation

Línea de trabajo

Busca el archivo SKILL.md de la skill graphify y lee los pasos de compilación existentes. Añade antes una sección breve que explique, con los casos y las indicaciones del issue, cuándo usar la búsqueda directa y cuándo crear o consultar un grafo. Estará listo cuando la sección cubra los grafos existentes, las consultas puntuales y las situaciones en las que vale la pena crear un grafo.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Ran the skill end-to-end today (sample corpus + a real ~200-file project) and it delivered genuinely good results — correct AST extraction, accurate cross-referencing between code and doc semantic layers, and the cohesion/god-node/surprising-connections analysis surfaced real findings (an architectural smell, a cross-doc duplicate derivation, etc.).

One gap: the skill has no guidance on when the full pipeline is worth its cost vs. when a plain code search would do. In practice:

  • A single targeted lookup ("what calls X", "where is Y defined") on an ordinary-sized codebase is answered just as well, instantly and for free, by grep/ripgrep/Read. Building the graph first is pure overhead for that case — AST extraction is fast and free, but semantic extraction on docs dispatches subagents and can take minutes and tens of thousands of tokens, even for a modest doc set.
  • The graph earns its cost when: there will be many repeated questions (amortized build cost), the corpus is large/unfamiliar and the user needs orientation, the question needs cross-file/cross-type links text search can't see (doc ↔ code, cross-doc semantic duplicates), or the question is structural (cohesion, bridging nodes, import cycles) rather than textual.

Right now the skill (reasonably) defaults to building on /graphify <path>, which is fine when the user explicitly asks for that — but when an agent is deciding on its own whether to reach for graphify vs. a direct search for a one-off question, there's no steering in SKILL.md toward the cheaper option first.

Suggested addition: a short "is a graph worth it here" section before the build steps, something like:

Building costs real time and tokens: AST extraction on code is free and fast, but semantic extraction on docs/papers/images dispatches subagents (minutes, tens of thousands of tokens), and a full pipeline run is overhead you pay once per corpus state. Don't pay it reflexively.

If graphify-out/graph.json already exists for this corpus, always query it rather than re-deriving the answer by reading files or rebuilding — that cost is already sunk and querying is cheap.

If no graph exists yet, check whether the request is a single, targeted lookup a plain code search would answer just as well — "where is X defined", "what calls Y", "find the function that does Z" on a codebase of ordinary size. For these, grep/ripgrep/Read is faster, free, and exact. Say so and just search directly; offer to build the full graph instead only if the user wants it.

Building (or updating) the graph earns its cost when:

  • You expect many future questions against this corpus — the build cost amortizes.
  • The corpus is large or unfamiliar and the user needs orientation (god nodes, communities).
  • The question needs cross-file or cross-type connections text search can't see.
  • The question is structural/architectural, not textual.

Happy to turn this into a PR against SKILL.md if that's useful.

Separately filed one small fix noticed during testing as a PR: #4830 (graphify query --help ran a literal query instead of showing usage). The other thing I initially flagged (hyperedge members/nodes field mismatch) turned out to already be intentionally handled in build.py (#1561) — not a bug, so no PR for that one.

Lenguaje dominante
Python
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Forks
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Preparar el entorno

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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