.claude/skills/code-graph-context/SKILL.md
Structural code graph queries via CodeGraphContext MCP (tree-sitter + KuzuDB). Find callers, callees, class hierarchies, dead code, and module dependencies.
npx skillsauth add oimiragieo/agent-studio code-graph-contextInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Structural code graph queries using the CodeGraphContext MCP server (tree-sitter AST + KuzuDB property graph).
| Need | Tool |
| ---------------------------------------- | ---------------------------------- |
| Who calls foo()? | find_callers (this skill) |
| What does foo() call? | find_callees (this skill) |
| Class hierarchy / interface implementors | get_class_hierarchy (this skill) |
| Dead code / unreachable functions | find_dead_code (this skill) |
| Module-level import graph | get_module_deps (this skill) |
| Keyword / regex search | pnpm search:code or ripgrep |
| Semantic / conceptual search | code-semantic-search skill |
| AST shape matching | code-structural-search skill |
| Compiler-verified definition/references | lsp-navigator skill |
Use this skill when you need relationship traversal across the call graph or import graph, not text matching.
| Tool | Purpose | Key Parameters |
| --------------------- | ---------------------------------------- | -------------------------------- |
| find_callers | All functions/methods that call a symbol | symbol, file?, depth? |
| find_callees | All symbols called by a function | symbol, file?, depth? |
| get_class_hierarchy | Superclasses, subclasses, interfaces | class_name, direction? |
| find_dead_code | Functions with no callers in graph | scope?, min_confidence? |
| get_module_deps | Import/require dependency graph | module, direction?, depth? |
| query_graph | Raw Cypher query against KuzuDB | cypher, params? |
# Install CodeGraphContext MCP server
npm install -g @codetiger/code-graph-context-mcp
# Index your codebase (run from project root)
code-graph-context index --root . --lang typescript,javascript
Add to .claude/settings.json under mcpServers:
"CodeGraphContext": {
"command": "code-graph-context-mcp",
"args": ["--db", ".claude/context/data/code-graph.kuzu"]
}
// 1. Find all callers of a function
mcp__CodeGraphContext__find_callers({ symbol: 'shouldUseWorktree', depth: 2 });
// 2. Trace what a function depends on
mcp__CodeGraphContext__find_callees({ symbol: 'routeRequest', file: 'routing-table.cjs' });
// 3. Dead code candidates (low confidence = more results)
mcp__CodeGraphContext__find_dead_code({ scope: 'src/', min_confidence: 0.8 });
// 4. Raw Cypher for custom traversal
mcp__CodeGraphContext__query_graph({
cypher: 'MATCH (a:Function)-[:CALLS]->(b:Function) WHERE b.name = $name RETURN a',
params: { name: 'handleAuth' },
});
After graph analysis, record structural findings:
MemoryRecord({
type: 'pattern',
content: 'routeRequest has 12 callers — high-risk refactor target',
area: 'architecture',
});
MemoryRecord({
type: 'gotcha',
content: 'worktree-utils dead code: shouldPruneWorktree() never called',
area: 'cleanup',
});
CodeGraphContext supports two operation modes:
CLI mode (batch indexing, one-shot queries):
code-graph-context index --root . --lang typescript,javascript
code-graph-context query --cypher "MATCH (f:Function) RETURN f.name LIMIT 10"
MCP server mode (live, incremental — recommended for agent use):
npx @codetiger/code-graph-context-mcp --watch --db .claude/context/data/code-graph.kuzu
With --watch, the server monitors file changes and re-indexes incrementally. No manual re-index after refactors.
For large codebases (>100K nodes), replace KuzuDB with Neo4j:
code-graph-context index --root . --backend neo4j --uri bolt://localhost:7687
Add to settings.json:
"CodeGraphContext": {
"command": "code-graph-context-mcp",
"args": ["--backend", "neo4j", "--uri", "bolt://localhost:7687"]
}
Generate a browsable call graph:
code-graph-context visualize --output .claude/context/tmp/call-graph.html
For CI/CD pipelines, ship a pre-built graph bundle with the repo:
# In CI: build + archive
code-graph-context index --root . --export .claude/context/data/code-graph.bundle.gz
# In agent startup: restore
code-graph-context restore --bundle .claude/context/data/code-graph.bundle.gz
// Find high-complexity functions (cyclomatic > 10)
mcp__CodeGraphContext__query_graph({
cypher:
'MATCH (f:Function) WHERE f.complexity > $threshold RETURN f.name, f.file, f.complexity ORDER BY f.complexity DESC',
params: { threshold: 10 },
});
query_graph raw Cypher before checking if a purpose-built tool covers the needfind_dead_code results as certain — dynamic dispatch and reflection create false positives--watch MCP mode, manual re-index is not needed — avoid running index --incremental during active MCP sessionsSkill({ skill: 'code-graph-context' });
Invoke for: impact analysis before refactoring, call chain debugging, dead code audits, module dependency reviews, and architectural dependency mapping.
tools
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
tools
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
data-ai
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
development
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.