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Ccg

skill-tae2089-code-context-graph-ccg · by tae2089

code-context-graph — code knowledge graph for token-efficient code understanding. Routes user intent to the right tool (search/analyze/docs/annotate/namespace).

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Install

$ agentstack add skill-tae2089-code-context-graph-ccg

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.

Preview Execution monitoring

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About

ccg — Routing & Search

ccg is a Tree-sitter-based code graph tool. Complementary to Grep/Read, not a replacement. Choose based on task type.

Task Routing (most important)

| User intent | Tool | Why | | ------------------------------------------ | ----------------- | --------------------------------- | | "Where is X?" — simple location lookup | Grep + Read | Faster and cheaper than ccg | | "Find code related to X" — semantic search | ccg search | Annotation/keyword semantic match | | "What's affected if I change X?" | /ccg-analyze | Graph traversal | | "Understand structure/architecture" | /ccg-docs (RAG) | retrieve_docs first, then tree | | "Document intent/rules in code" | /ccg-annotate | AI annotation workflow | | "Manage multiple service codebases" | /ccg-namespace | MSA namespace isolation |

Don't use ccg when Grep is enough. ccg MCP context costs hundreds to thousands of tokens per task. For trivial tasks, it's pure overhead.

Core Commands

ccg build .          # Build graph + search index (first time or after big changes)
ccg update .         # Incremental — changed files only
ccg search "" # FTS search (includes annotations)
ccg status           # Graph statistics
ccg docs --out docs  # Generate docs + default RAG index
ccg serve            # Start MCP server (stdio)

For remote or self-hosted MCP over Streamable HTTP, use ccg-server instead of ccg serve. Local ccg serve is stdio-only.

For detailed flags, use ccg --help or refer to MCP schema.

ccg search Patterns

Search by domain keywords (Korean works too). Annotation tags (@intent, @domainRule) are indexed alongside code.

ccg search "결제"               # All payment-related functions (Korean)
ccg search "authentication"     # Auth-related
ccg search --path internal/auth "login"  # Path-scoped

Difference from Grep: Grep matches text, search matches semantic intent. Searching "결제" finds payment functions through their @intent 결제 처리 annotation.

Build Freshness

  • ccg build . fails with schema error → run ccg migrate, retry
  • PostgreSQL or upgrading existing DB → ccg migrate first
  • Graph feels stale after code changes → ccg update .

Core MCP Tools (commonly used)

| Tool | When | | ------------------ | -------------------------------------------- | | search | Semantic search | | query_graph | Structured queries (callers/callees/imports) | | get_node | Lookup by qualified name | | list_graph_stats | Graph size check | | get_minimal_context | Compact project snapshot and tool suggestions |

For other tools, see /ccg-analyze, /ccg-docs skills.

Agent Entry Pattern

For broad natural-language questions, start with generated docs/RAG before pulling exact graph edges:

ccg build .
ccg docs --out docs

Then use MCP retrieve_docs for bounded Markdown evidence, get_rag_tree to expand module context, and only then query_graph, get_node, or trace_flow for exact symbols and relationships.

Response Budget Rule

For LLM-agent use, prefer bounded graph queries. Start with limit=50 or limit=100 and follow has_more / next_offset rather than asking for a bulk result first.

Tools with explicit pagination:

| Tool | Parameters | | ---- | ---------- | | query_graph | limit, offset | | list_flows | limit, offset | | list_communities | limit, offset | | get_community | member_limit, member_offset when include_members=true | | get_architecture_overview | community_limit, community_offset, coupling_limit, coupling_offset |

High-volume analysis tools such as find_dead_code, find_suspect_fallback_edges, and broad architecture/onboarding prompts should be scoped by namespace, path, or a narrower first question before use.

Trade-offs (verified)

  • Search tasks → ccg dominates (50–60% token reduction vs rg)
  • Single location lookup → Grep+Read is cheaper (ccg is overhead)
  • Miss-prevention matters (PR review, etc.) → ccg catches domain rules Grep misses
  • Frequently changing code → factor in graph rebuild cost

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.