knowledge-layer

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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.

AI & Automation 1,325 stars 265 forks Updated 2 days ago MIT

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Skill Content

# 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=".", quick=False)` - `ask_filesystem(question, workspace=".")` ## 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.

Details

Author: study8677
Repository: study8677/repobrain
Created: 9 months ago
Last Updated: 2 days ago
Language: Python
License: MIT

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