Build and run systems before and after a change, then execute the same reproduction procedure against both. Save environment details, inputs, commands, and separate logs to document observed behavior and any setup blockers.
Use when deploying before/after systems and building a live-system PoC for a regression candidate.
Review the instructions the agent will receive before you install.
Input: sealed preparation/discovery packages, candidate and registry assignment. Follow project guidance before target work.
Independent verification of an existing PoC; use replay.
reproduce.json. If blocked, retain attempted setup and exact prerequisites.
Build and deploy only inside your attempt directory, and list every build directory in build_dirs.Keep detailed evidence in the sealed package. Return only:
complete or blocked, matching the record.reproduce.json path, or why none was produced.Completion means the experiment finished, not that the regression is confirmed.
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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.