Prepare a regression investigation by fixing the exact before and after source versions, preserving PR or commit context, and producing a sealed preparation record that explains change boundaries and evidence collection limits.
Use when pinning before/after sources and collecting PR or commit context for a regression investigation.
Review the instructions the agent will receive before you install.
Input: PR URL or repository plus commit/range, and registry assignment. Follow project guidance after resolving the target commit.
The same inputs have a verified preparation record; reuse it.
uv run --no-project with
repository, S0, S1 and a new directory, or equivalent Git commands for special
source needs. Preserve dependency/submodule/LFS revisions. Exclude later history;
keep exports pristine and build in separate copies.prepare.json with boundary explanations, frozen context and
source artifacts. If blocked, preserve collected evidence and name the prerequisite.Keep detailed evidence in the sealed package. Return only:
complete or blocked, matching the record.prepare.json path, or why none was produced.No reviews yet. Tell others what you thought.
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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.