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-`/ccg-annotate annotate [file|dir]` - AI annotation generation workflow
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When looking for code locations, related implementations, call relationships, impact radius, or architecture context,
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use ccg MCP tools and Agent Skills first.
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- For natural-language code understanding, module exploration, and architecture context, prefer the `/ccg-docs` skill: use `search_docs` to find relevant docs, then `get_doc_content` to read one (with `get_rag_tree` for module context).
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- For natural-language code understanding and module exploration, prefer the `/ccg-docs` skill: use `search_docs` to find relevant docs, then `get_doc_content` to read one.
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- For exact symbol locations, call relationships, and graph metadata, use ccg MCP `query_graph`, `get_node`, `get_minimal_context`, or the `/ccg` skill.
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- For annotation/keyword-based candidate search, use ccg MCP `search` or `ccg search` as a supplement.
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- For impact radius, flows, dead code, and structural analysis, prefer the `/ccg-analyze` skill and related MCP tools (`get_impact_radius`, `trace_flow`, `find_dead_code`, `get_architecture_overview`).
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- For impact radius, flows, and change-risk analysis, prefer the `/ccg-analyze` skill and related MCP tools (`get_impact_radius`, `trace_flow`, `detect_changes`, `get_affected_flows`).
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- For simple string checks, file existence checks, or cases where the ccg index is missing or stale, use `rg` as a supplement. If needed, refresh the graph with `ccg build .` or `ccg update .`.
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## Documentation
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See the `guide/` directory for detailed documentation:
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-[CLI Reference](guide/cli-reference.md) - all commands, flags, and config files
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# code-context-graph
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Local code analysis tool that parses codebases via Tree-sitter into a knowledge graph. Supports 12 languages, 33 MCP tools, and custom annotation search.
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Local code analysis tool that parses codebases via Tree-sitter into a knowledge graph. Supports 12 languages, 17 MCP tools, and custom annotation search.
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CCG is built primarily for GPT, Claude, Codex, and other LLM-based coding agents. It acts as local or self-hosted context infrastructure: agents can search code by intent, inspect call graphs, trace impact, retrieve docs, and keep responses bounded instead of reading entire repositories into context.
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-**33 MCP tools**: parse, search, impact analysis, flow tracing, dead code detection, postprocess operations, namespace file management, and more
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-**17 MCP tools**: parse, search, graph queries, impact analysis, flow tracing, change detection, documentation discovery, and more
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-**Evidence-driven code exploration**: DB-backed retrieval returns small file-level candidates with matched fields, evidence nodes, and optional docs before agents drill into exact graph nodes
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-**Browser Wiki UI**: `ccg-server` can serve generated docs, tree search, DB-backed retrieval, Context Tray copying, and an Obsidian-style graph viewer
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-**Custom annotations**: `@intent`, `@domainRule`, `@sideEffect`, `@mutates`, `@index` — search code by business context ([details](guide/annotations.md))
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-**Webhook sync**: GitHub / Gitea push events → auto clone + build with per-repo branch filtering and `.ccg.yaml``include_paths` auto-loading ([details](guide/webhook.md))
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-**Eval**: Golden corpus-based parser accuracy (P/R/F1) and search quality (P@K, MRR, nDCG) evaluation ([details](guide/eval.md))
|[Postprocess Failure Policy](guide/postprocess-failure-policy.md)| Status rules, failure causes, and automatic degraded/fail_closed policy for build and postprocess tools |
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|[Runtime Layout](guide/runtime-layout.md)|`ccg`, `ccg-server`, Wiki serving, and shared `ccg-core` ownership boundaries |
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|[Development](guide/development.md)| Dev guide, integration test, project structure |
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|[Namespace Migration](guide/namespace-migration.md)| Default namespace change and migration guide |
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# Custom Annotations
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Add structured metadata to your code so that AI, generated docs, RAG, and
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focused search can leverage business context. Annotations are indexed for
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`ccg search` and are also rendered into generated Markdown that feeds the RAG
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index.
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Add structured metadata to your code so that AI, generated docs, and focused
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search can leverage business context. Annotations are indexed for `ccg search`
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and are also rendered into generated Markdown.
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For LLM-agent natural-language exploration, prefer the docs/RAG path first:
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For LLM-agent natural-language exploration, prefer the generated-docs path first:
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`ccg docs`, then use MCP `search_docs` to find relevant docs and `get_doc_content`
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to read one (with `get_rag_tree` for module context). Use `ccg search` when you
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to read one. Use `ccg search` when you
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need a focused list of annotation/keyword-matched symbol candidates.
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Annotation quality is validated by `ccg lint`. For category meanings such as `unannotated`, `incomplete`, `dead-ref`, `contradiction`, and `drifted`, see [Lint Guide](lint.md).
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