Turn saved regression decisions into evidence-linked HTML, Markdown, and JSON reports. Validate records and coverage, preserve assigned ratings, and distinguish confirmed defects from existing bugs, rejected claims, and unresolved work.
Use when assembling or refreshing an evidence-linked regression report from saved campaign decisions.
Review the instructions the agent will receive before you install.
Input: frozen scope.json, campaign index, progress ledger, sealed stage packages
and registry assignment.
Follow project guidance for every scoped change before reporting.
Adjudicating evidence or repairing records; return those needs to the caller.
uv run --no-project with the campaign
directory and --output ASSIGNED_DIRECTORY/reports to render HTML, Markdown and JSON.
Exit 1 means changed evidence or an invalid index; return its diagnostics and record the stage as blocked. Exit 0 may include UNRESOLVED findings; report each unresolved_reason and the verification level of confirmed findings. Never repair decisions, invent
evidence or edit hashes to pass validation.report-stage.json with the assigned inputs and generated reports as artifacts;
record validation failures as blocked.Keep detailed findings in the reports. Return only:
complete or blocked for report production.report-stage.json and validated output paths; identify incomplete outputs.A complete report may describe an incomplete campaign.
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Build and test code with AddressSanitizer, a tool that detects invalid memory access during execution. Configure checks for C/C++ and unsafe Rust, investigate crash reports, and adjust settings for fuzzing, or testing with generated inputs.
Assess source code and live reproduction evidence to decide whether a change introduced a regression, a newly caused defect. Record supported conclusions, exclusions, severity, and unresolved gaps in an evidence-linked decision file.
Sets up AFL++ to test C/C++ programs with varied generated inputs, a technique called fuzzing. Covers running tests across CPU cores, monitoring progress, reducing input collections, and reproducing crashes for investigation.
Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.
Reviews Algorand smart contracts written in TEAL or PyTeal for 11 vulnerability patterns. Produces a complete coverage table and findings with code references, severity ratings, and suggested fixes, with optional automated checks using Tealer.
Set up Atheris to test Python code and C extensions with automatically varied inputs. It guides test harness creation, coverage tracking to explore more code, and AddressSanitizer setup for finding memory errors.