Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    Root Cause Debugger

    by Roy Yuen

    2

    An evidence-first debugging workflow for agents to identify, reproduce, and surgically fix software defects.

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    See it in action

    You say

    The auth-flow.spec.ts test is failing with a 401 error on npm test. Debug the root cause and apply a surgical fix.

    Your agent does

    Reproduction: Ran 'npm test' -> Test 'auth-flow.spec.ts' failed with 401. Root Cause: JWT expiration was set to 0 in dev config, causing immediate rejection. Fix: Updated config/dev.json expiry to 3600s. Regression Trace: Added smoke test check_token_validity(). Verification: PASSED.

    What you get

    Identify and fix the source of intermittent flaky test failuresDebug runtime exceptions by tracing bad values back to their sourceResolve dependency and import conflicts without breaking the buildCreate minimal reproduction cases for complex production-like incidentsApply surgical fixes that maintain project style and architectural boundaries

    About this skill

    What it does

    The Root Cause Debugger is a high-precision diagnostic skill for AI agents. Rather than "spraying and praying" with broad code changes, it enforces an evidence-first debugging loop: reproduce, narrow scope, identify root cause, apply a surgical fix, and verify with regression coverage. This prevents the agent from making destructive "guesses" like indiscriminately upgrading dependencies or ballooning timeout values.

    Why use this skill

    Standard LLMs often attempt to fix bugs by rewriting large swaths of code or tweaking configurations until something works. This skill forces a developer-centric workflow that treats debugging as a science. It is particularly effective for complex issues like flaky tests, runtime exceptions, dependency conflicts, and race conditions where the "where" and "why" are not immediately obvious.

    Supported Scenarios

    • Failing Tests: Isolates minimal reproductions to find the boundary of failure.
    • Runtime Exceptions: Traces value transformations backward to find illegal states.
    • Dependency/Build Failures: Audits lockfiles and module formats before suggesting changes.
    • Flaky Behavior: Proves race conditions through targeted logging and state inspection.

    The output is a structured Handoff Report that documents the exact evidence found, the surgical fix applied, and the automated check added to prevent regressions.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Security scanned

    Verified clean 3 months ago

    Listed3 months ago

    Creator

    Roy Yuen
    Roy Yuen

    1098+ total installs · 111 skills on Agensi

    Frequently Asked Questions

    Popular in Testing & QA

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