doc_kg

Expert knowledge for installing, configuring, and using DocKG — a hybrid semantic + structural knowledge graph for document corpora (.md and .txt files). Use this skill when the user asks about: setting up DocKG in a project, adding doc-kg as a Poetry dependency, building the SQLite or LanceDB knowledge graph from documents, running the multipass analysis pipeline (dockg pipeline run/embed/manifold), configuring .mcp.json for Claude Code or Kilo Code, configuring .vscode/mcp.json for GitHub Copilot, configuring claude_desktop_config.json for Claude Desktop, using the dockg CLI (dockg build, dockg build-graph, dockg build-index, dockg query, dockg pack, dockg analyze, dockg semantic-analyze, dockg pipeline, dockg viz, dockg mcp, dockg snapshot), using the graph_stats / query_docs / pack_docs / get_node MCP tools, or troubleshooting DocKG errors.

Expert knowledge for KGRAG — the unified cross-KG registry and federated query layer for CodeKG, DocKG, and MetaKG. Use this skill when: (1) Setting up or configuring KGRAG in projects, (2) Querying across multiple knowledge graphs simultaneously, (3) Extracting code/doc snippets for LLM context, (4) Integrating with Claude Code or Claude Desktop, (5) Running architectural analyses, (6) Managing the KG registry, or (7) Troubleshooting KG-related issues.

Step-by-step release workflow for publishing code-kg to PyPI using Poetry. Use this skill when the user wants to cut a release, bump the version, update the CHANGELOG, tag a commit, build and publish to PyPI, or run any part of the release pipeline for the code-kg project.