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    Gauntlet: Adversarial code review and repair

    1

    Pushes your agent to question its work, challenge its assumptions, expose defects, and verify the repairs with evidence.

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    Gauntlet: Adversarial code review and repair

    Gauntlet: Adversarial code review and repair

    Example session with this skill installed

    Finish implementing the CSV export utility for our table. Preserve numeric zero and false, treat null/undefined as empty cells, and escape commas, double quotes, and CR/LF line breaks. Use CRLF between rows, do not mutate the input, and return an empty string for empty input. The table already validates its scalar cells.

    • Read your context and instructions
    • Compiled the gauntlet

    Implemented the CSV exporter, then used Gauntlet with two sub-agents to review, repair, and re-review the implementation.

    Outcome: PASS_WITHIN_SCOPE. Both confirmed defects are fixed; no unresolved finding remained after the fresh review. Focused tier: 2 of 2 rounds used, with 1 clean re-attack after the repair round.

    Implementation and adversarial review

    The first implementation passed its 3 baseline assertions. Gauntlet then assigned separate review lenses to two real sub-agents: scalar preservation and CSV encoding. Their counterexamples exposed gaps in those initial tests.

    Round 1 findingReproduced failureRoot-cause repair
    Zero and false lost[[0, false]] exported as , instead of 0,falseReplace truthiness fallback `value
    Lone CR not quotedA cell containing a carriage return was emitted without surrounding quotesInclude \r in the shared encoder's quote-trigger character class

    I reproduced both findings, merged duplicate reports by mechanism, and added regressions. The regression checks failed on the saved original for exactly those two defects, then passed after the two-line repair.

    Delivered implementation

    export function toCsv(rows) {
      return rows.map(row => row.map(value => {
        const text = String(value ?? '');
        return /[,"\r\n]/.test(text)
          ? `"${text.replaceAll('"', '""')}"`
          : text;
      }).join(',')).join('\r\n');
    }
    

    Fresh review and verification

    Round 2 attacked the repaired snapshot using new inputs and an independent parsing oracle; it was separate from the round that changed the code.

    • Baseline: 3 assertions passed.
    • Directed regressions: 9/9 passed, including original failures, neighboring encodings, row separators, empty input, and immutability.
    • Contract reviewer: 13 additional literal-output cases, mixed records, and mutable/frozen input checks passed.
    • Encoding reviewer: 32,056 deterministic round-trip cases passed using a separate strict CSV reader, plus 12 parser-oracle checks and 3 explicit empty-cell checks.
    • Both sub-agents reported no new substantiated defect or unresolved candidate. The orchestrator inspected the oracle, reran the round-trip verifier, and checked the final patch.

    Final encoder SHA-256: 99aaf5953069b99aa70215962abed6a3042da1ce6605d716bbc96692e12849b8. Checks ran locally on Node v24.18.0.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Gauntlet is a free, open-source agent skill for evidence-driven adversarial review of code, designs, APIs, infrastructure, tests, and agent workflows. It pushes your agent to look beyond passing tests, challenge its assumptions, and uncover bugs, gaps, and missed edge cases.

    How it works

    Gauntlet follows a seven-step loop: map → attack → substantiate → trace the cause → repair → verify → re-attack.

    After implementing a task, the agent uses Gauntlet to challenge the result, with sub-agents when available. It investigates findings, fixes their underlying causes, and then challenges the repaired work from a fresh angle. The loop continues until the scoped checks and review requirements pass, or the review budget runs out with remaining problems clearly reported.

    Evidence before fixes

    A suspicion is a starting point, not a confirmed bug. The agent must back up each finding with evidence—such as a reproducible failure, a test, or a clear explanation grounded in the source. After a repair, it must check that the original problem is resolved and look for new problems introduced by the change.

    A review stays read-only unless your request includes implementing or fixing the work. Findings, fixes, and verification results are tracked so the final report shows what was checked and what remains unresolved.

    Fits your agent

    Use independent sub-agents when the host supports them, or named solo passes when it does not. Maintain state with a host tracker, Markdown ledger, or the optional Python 3.10+ and SQLite helper. The skill itself needs no API key or third-party Python packages; agent access follows your host configuration.

    MIT licensed. Source, installation instructions, and evaluation follow-up are available in the public repository.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Verified clean 13 days ago

    • Free to download with an account

    Requires

    Node.js
    Python

    Listed13 days ago

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