Parse and validate Pysa NDJSON taint models with sources, sinks, and tito.
npx skills add https://github.com/facebook/pyre-check --skill pysa-json-models
Please help me install this Agent Skill. Skill: pysa-json-models Source: https://github.com/facebook/pyre-check/tree/main/.llms/skills/pysa-json-models Command: npx skills add https://github.com/facebook/pyre-check --skill pysa-json-models
Pysa JSON model outputs provide structured taint information for static analysis, but they can be hard to read, validate, and reason about without tooling. This skill helps engineers interpret, verify, and debug taint models and issues described in newline-delimited JSON.
Inspect a .models file produced by Pysa and identify the taint sources, sinks, and tito mappings.
High-intent search queries and answers about installing and using this skill.
To parse Pysa JSON taint models, you process newline-delimited JSON objects to inspect taint sources, sinks, tito mappings, and sanitizers across Python projects. This validates the field structure and callable relationships within the static analysis output.
Pysa taint models are structured newline-delimited JSON outputs from Pyre's static analysis that define sources, sinks, and taint-in-taint-out data. You need to debug them to verify taint flows and validate field structures like callables and line numbers.
Validating newline-delimited JSON model outputs involves checking optional fields and correlating relationships between sources, sinks, tito, and callables. Robust validation ensures the taint definitions and issues described across modules are structurally sound.
Yes, you can troubleshoot taint-in-taint-out flows across Python modules by correlating the sources and sinks mapped in the NDJSON output. This helps trace how taint data passes through callables and verify the static analysis results.
Pysa static analysis JSON outputs contain optional fields depending on the specific taint definitions and issues identified. Handling these optional fields robustly is required to accurately interpret sources, sinks, and sanitizers without validation errors.
Yes, this approach works directly with Pyre static analysis for Python projects by interpreting the .models outputs. It reads the newline-delimited JSON to validate taint definitions and debug issues across the analyzed codebase.
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