| name | codebase-knowledge-graph |
| description | Router skill for graph-based codebase/corpus queries. Picks the right tool: code-review-graph (CRG, MCP-native, Tree-sitter, 23 langs, auto-update) for code structure; graphify for mixed corpora (docs + papers + images + tweets). Use when: onboarding, mapping architecture, tracing dependencies, blast-radius analysis, user says 'graph this', 'map the codebase', 'show dependencies'. |
Codebase Knowledge Graph (Router)
Two graph tools live in this system. They are complementary, not redundant — this skill routes tasks to the right one.
Decision tree
Is the corpus code-only (Python, TS, Go, Rust, Java, etc.)?
├── YES → code-review-graph (CRG) — MCP-native, Tree-sitter, 23 langs
└── NO (docs + papers + images + tweets, or research corpus)?
└── graphify — LLM-assisted semantic extraction
Already have .code-review-graph/graph.db? → CRG (query via MCP)
Already have graphify-out/graph.json? → graphify (query via CLI)
Tool A — code-review-graph (primary for code)
MCP-native. Tree-sitter. 22 tools. Auto-updates on Write/Edit. Registered at user scope via claude mcp add -s user code-review-graph uvx code-review-graph serve (stored in ~/.claude.json).
When to use
- Onboarding to a codebase (Python, TS, TSX, Go, Rust, Java, Scala, C#, Ruby, Kotlin, Swift, PHP, Solidity, C/C++, Dart, R, Perl, Lua, Zig, PowerShell, Julia, Vue, Svelte, Jupyter
.ipynb)
- Mapping architecture before a refactor
- Blast-radius analysis: "what breaks if I change
login()?"
- Risk-scored PR/commit review
- Finding hub nodes, bridges, surprise couplings
- Pre-loading context for Flow
/flow:map or /flow:discover
Core usage via MCP tools
| Task | Tool |
|---|
| "Give me just enough context to start" | get_minimal_context(task="...") — ~100 tokens |
| "What calls this function?" | query_graph(pattern="callers_of", node="...") |
| "What does this call?" | query_graph(pattern="callees_of", node="...") |
| "What's affected if I change X?" | get_impact_radius(node="...") |
| "Find classes/functions by name" | semantic_search_nodes_tool(query="...") |
| "Full review context for a PR" | get_review_context(diff="...") |
| "Architecture overview" | prompt: architecture_map |
| "Pre-merge check" | prompt: pre_merge_check |
Follow the next_tool_suggestions field in every response — it's the optimal next step.
CLI (when MCP isn't available)
uvx code-review-graph build
uvx code-review-graph update
uvx code-review-graph status
uvx code-review-graph detect-changes
uvx code-review-graph visualize
uvx code-review-graph wiki
uvx code-review-graph register <path>
Auto-update
PostToolUse hook fires code-review-graph update on Write/Edit/MultiEdit if .code-review-graph/graph.db exists. No manual updates needed.
Output
.code-review-graph/graph.db — SQLite WAL store- Via
visualize: interactive D3.js HTML
- Via
wiki: Obsidian vault with wikilinks
- Via exports: GraphML (Gephi/yEd), Neo4j Cypher, SVG
Tool B — graphify (for mixed corpora)
LLM-assisted semantic extraction. Handles docs, papers, images, tweets, code together.
When to use
- Research corpus (PDFs + tweets + screenshots + notes)
- Karpathy-style
/raw folder workflow
- Cross-document concept mining
- Any corpus where LLM vision/semantic matters (e.g. chart images, handwritten notes)
- Code corpora where you want
INFERRED/AMBIGUOUS edge tagging and Q&A memory loop
Core usage (slash command)
/graphify # current dir → Obsidian vault
/graphify <path> # specific path
/graphify <path> --update # incremental
/graphify query "<question>" # BFS traversal
/graphify path "A" "B" # shortest path
/graphify explain "Node" # node explanation
/graphify --mcp # MCP stdio server
Output
graphify-out/graph.json — persistent graphgraphify-out/GRAPH_REPORT.md — god nodes, surprises, suggested questionsgraphify-out/graph.html — interactive viz- Optional: Obsidian vault, SVG, GraphML, Neo4j push
Confidence-Scored Relationships (graphify only)
| Tag | Meaning | Confidence |
|---|
EXTRACTED | Found directly in source (import, call, inheritance, citation) | 1.0 |
INFERRED | Reasonable deduction from patterns | 0.4–0.9 |
AMBIGUOUS | Flagged for human review | 0.1–0.3 |
CRG uses a simpler three-tier edge confidence (EXTRACTED / INFERRED / AMBIGUOUS) plus float scores.
Integration with Flow
With /flow:map
For code-heavy repos, build CRG first → use get_minimal_context to guide mapping agents.
For mixed corpora, build graphify first → GRAPH_REPORT.md guides mapping.
With /flow:discover
Prefer CRG MCP tools for code queries (no LLM extraction cost, auto-updates).
Use graphify for discovery questions that span docs + code + research notes.
When NOT to use graph tools
- Single-file tasks (overhead exceeds benefit)
- Already familiar with the codebase
- Quick lookups where Grep/Glob is faster
Performance
| Tool | Corpus | Reduction | Build time |
|---|
| CRG | Code-only, 500 files | 8.2× avg, up to 49× monorepo | ~10s |
| CRG | Incremental (auto) | N/A | <2s |
| graphify | Mixed, 50+ files | ~71.5× | Minutes (LLM) |
| graphify | Incremental | N/A | <2s code, minutes docs |
Retired: graphify install prerequisite
Previous versions of this skill required pip install graphifyy && graphify install as prereq. Now:
graphifyy is already installed (pip).code-review-graph is installed via uv tool install code-review-graph.- CRG is registered at user scope via
claude mcp add -s user code-review-graph uvx code-review-graph serve (writes to ~/.claude.json — the authoritative MCP registry). Graphify is invoked as a CLI (python -m graphify) rather than a persistent MCP server.
- No
graphify install or code-review-graph install needed — ATLAS wires them manually to preserve settings harmony. Do NOT use code-review-graph install (it injects its own skills/hooks/CLAUDE.md text and clobbers the ATLAS layout).
