augur

name	augur
description	Run IRIS neurosymbolic SAST. Trigger on: run IRIS, run augur, neurosymbolic SAST, custom CodeQL taint analysis, LLM-labeled static analysis, find vulns with CodeQL, generate custom taint queries from LLM-labeled sources/sinks.

Read /Users/Shayaun.Nejad/VibeCode/open-sourced/augur/WORKFLOW.md and follow the two-phase pipeline exactly.

Example invocations:

• Run augur on DB /path/to/codeql-db for all passes
• Run IRIS/augur on /path/to/codeql-db for pass 1 and pass 5
• Generate labels and stop at checkpoint for review

Required input parameters:

1. db_path: absolute path to a CodeQL database
2. passes: optional subset of 1..13 (default all)
3. model: LLM identifier for labeling
4. project_root: optional source root for richer framework detection

Expected outputs:

• iris/exploration/* detection and run metadata
• iris/extraction/* candidate extraction artifacts
• iris/labels/labels.json (schema-valid)
• iris/libraries/*.qll generated source/sink/sanitizer/summary libraries
• iris/queries/Pass*.ql rendered pass queries
• iris/results/pass*.sarif sequential analysis outputs
• iris/report/iris_report.md filtered final report

Execution rules:

1. Run Step 0 pre-flight before any query execution.
2. Load reference files on-demand based on detected language/framework.
3. Run extraction and taint queries sequentially.
4. Present checkpoint summary. In interactive mode, pause for human review before Phase 2; in automated mode, log the summary and continue.
5. Use scripts/filter_and_report.py to produce final report.