[Feature]: Skill resources for presets (copy-mode aware composition and preset-owned resources)

Abierto
#5,382 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
python
Área
tooling

Línea de trabajo

Start with presets/_resolver.py at PresetResolver.resolve_content and compare its path rewriting with the copy-mode handling from #5366. Then read extensions/_skill_resources.py and the extension resource lifecycle tests to trace validation, copying, registry ownership, and removal. Done means the acceptance criteria define stacked resource ownership and preset removal behavior, with matching tests for composition and resource lifecycle.

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

Descripción

triage-can-wait

Problem Statement

#5366 lets extension commands declare resources that are copied into their generated agent skills ("copy mode"; skill bodies are not path-rewritten). Presets are not covered:

  1. Composition: when a preset overrides a copy-mode extension command with append/prepend/wrap, PresetResolver.resolve_content (presets/_resolver.py ~l.789) still applies rewrite_extension_paths to the extension base layer. The composed skill therefore references .specify/extensions/<id>/... instead of the copied skill-relative resources. It still resolves, but is inconsistent with copy mode.
  2. Preset-owned resources: presets that override or provide skills cannot bundle their own resources (references, templates, scripts) into the skill directory.

Proposed Solution

  • Make preset composition copy-mode aware for skill outputs: when the winning base layer is a copy-mode extension command and the output is a skill, do not rewrite its paths. Command/template consumers keep today's rewriting.
  • Add an optional resources list to preset command/template entries in preset.yml (same pattern dialect as extension resources), copied into the preset's skill output with hash-tracked ownership in the preset registry, reusing the extension resource helper (extensions/_skill_resources.py).

Alternatives Considered

  • Keep presets on extension-local paths only (today's behaviour): works, but preset authors cannot ship skill-relative files.
  • Only fix composition (item 1) and defer preset-owned resources.

Component

Presets / skills rendering

AI Agent (if applicable)

All SKILL.md-based integrations

Use Cases

  • A team preset appends review rules to an extension skill and ships its own references/team-rules.md.
  • A preset wraps a copy-mode extension command and the composed skill should keep skill-relative references.

Acceptance Criteria

  • Composed skill bodies over a copy-mode extension base are not path-rewritten; non-skill outputs unchanged.
  • Preset resources are validated like extension resources and copied as regular files.
  • Ownership defined for path conflicts between an extension and one or more presets writing the same skill directory (who wins, who owns, what happens on preset removal and priority changes).
  • Defined behaviour for stacked layers (append/prepend/wrap): whose resources are copied.
  • On preset remove, preset-owned resources are removed and the extension's resources/skill are restored.
  • Tests mirror the extension resource lifecycle tests.

Additional Context

Deferred out of #5366 by design; that change only restores extension copy-mode skills (with resources) after preset remove.

AI Disclosure

Drafted with the pi coding agent (orchestrator model: Claude Opus 5.5 via GitHub Copilot; implementation/review subagents: GPT-6.1-terra, GPT-6-astra), human-supervised by the filer. Reproductions were run locally by the agent and reviewed by the filer before filing.

Lenguaje dominante
Python
Estrellas
149k
Forks
13.4k
Merge medio
1 d 5 h
PR fusionados (30 d)
223

Preparar el entorno

Abrir en Codespaces

Inicia el contenedor de desarrollo del proyecto en tu navegador, con tu propia cuenta de GitHub.

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.

Más de github/spec-kit

Todos los issues de github/spec-kit

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.