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.
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Use when assembling or refreshing an evidence-linked regression report from saved campaign decisions.
安装前可以查看交给 AI 代理的指令内容。
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.
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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.
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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.
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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.
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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.
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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.
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