LLMSkillHub
mirroredCopied from study8677/repobrain under MIT. study8677 has not claimed this page, and is not affiliated with LLM SkillHub.

study8677 / knowledge-layer

High-level deployment wrapper over RepoBrain core with graph-first knowledge injection and all-file support. Exposes `refresh_filesystem` and `ask_filesystem` for building and querying the knowledge graph.

0
SKILL.md
1.7 KB

Knowledge Layer Skill

Purpose

Provide a high-level deployment wrapper over RepoBrain core, with graph-first
knowledge injection and all-file support (code, docs, data, media metadata).

Inputs

  • refresh_filesystem(workspace=".", full=False)
  • ask_filesystem(question, workspace=".")

Refresh automatically chooses a first complete build, matching interrupted-task
resume, committed-diff update, or no-op. full=True always starts a new complete
build. The workspace defaults to the current directory and must be a clean Git
worktree. Ask stays read-only and only suggests rb-refresh when knowledge is
stale. Both refresh surfaces report failures without replacing usable knowledge.

Outputs

  • Refresh writes graph-first artifacts under .repobrain/:

    • knowledge_graph.json
    • knowledge_graph.md
    • knowledge_graph.mmd
    • document_index.md
    • data_overview.md
    • media_manifest.md
    • plus existing conventions.md and structure.md
  • Ask returns a grounded answer with source paths.

Boundaries

  • Skill is a wrapper layer only; no standalone runtime.
  • Core Hub/Agent/Pipeline architecture remains the source of truth.

Compatibility

  • Existing commands (rb-refresh, rb-ask, rb-mcp) remain valid.
  • High-level aliases can be disabled with RB_ENABLE_LAYER_ALIASES=0.

Degrade Strategy

  • If graph artifacts are unavailable, ask falls back to structure.md and
    conventions.md context.