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Query the Code Genome graph for analysis and debugging
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Based on SOC occupation classification
Call graph soundness framework: 9 pattern families, 10 safety vetoes, soundness-vs-precision tradeoffs. Use before changing merge.rs, analyzer, or prune pipeline.
Assess if a CVE in a dep is reachable from project code using 15-signal union (runtime/static/external). Outputs audit trail. Security-grade, conservative.
Runtime+static consensus: 15-signal weights, modes, saturation curve. Scalpel's novel moat. Use before changing tracer merge, prune decision, or adding signal sources.
Use to validate Scalpel's pruning safety on any Python project. Analyzes a real-world Python project with test suite, identifies dead code, stubs it, then runs the full test suite to prove nothing broke. Invoke with: /prune-safety-test <project-url-or-path>
Use to validate ALL Scalpel capabilities on a real-world Python project — static analysis accuracy, genome building, reachability classification, pruning safety, and context generation. Invoke with: /validate-on-project <project-url-or-path>
Use when working on scalpel-core, scalpel-proto, Code Genome graph structure, hybrid merge logic, reachability algorithms, confidence classification, or edge resolution
| name | graph-query |
| description | Query the Code Genome graph for analysis and debugging |
| argument-hint | <natural-language-query> |
| allowed-tools | Bash, Read |
Query the Code Genome graph. Translate the natural language query into a scalpel CLI command.
Available query patterns:
cargo run -p scalpel-cli -- export .scalpel/coverage | jq '.edges[] | select(.target_name | contains("X"))'cargo run -p scalpel-cli -- export .scalpel/coverage | jq '.edges[] | select(.source_name | contains("X"))'cargo run -p scalpel-cli -- prune-report .scalpel/coveragecargo run -p scalpel-cli -- score --coverage-dir .scalpel/coveragecargo run -p scalpel-cli -- score --coverage-dir .scalpel/coveragecargo run -p scalpel-cli -- export .scalpel/coverage | jq '.nodes | sort_by(-.call_count) | .[0:10]'If the query doesn't match a pattern, export the genome as JSON and use jq to answer it. Present results in a readable format with context about what was found.