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    Verify Before Shipping

    by Ifásola

    Stop guessing and start proving: Force your AI agent to provide verifiable execution logs for every 'done' claim.

    Updated Jun 2026
    0 installs

    Free

    Included in download

    • Downloadable skill package
    • Works with Cursor, Codex CLI
    • 2 permissions declared
    • Instant install

    Sample input

    Finish the fix for the login auth bug and show me the proof that it works now.

    Sample output

    [PROOF: PASS]

    • pytest tests/test_auth.py: Status 0
    • ruff check .: Status 0

    Report:

    • Ran auth unit tests: All 4 passed.
    • Linting: No violations. Verification complete. The login regression is resolved.

    About This Skill

    What it does

    This skill eliminates the #1 failure mode in AI-assisted development: "hallucinated success." It transforms the definition of 'done' from a verbal claim to a verified report. By integrating a lightweight evidence-capture utility, it forces your agent to run actual terminal commands, capture exit codes, and present raw logs before reporting a task as complete.

    How it works

    The skill provides a specialized proof.py utility (Python stdlib only) that gates progress. Instead of just running commands, the agent executes them through this wrapper. The wrapper produces a structured evidence block including the command string, the status code, and the specific stdout/stderr logs. If any check fails, the skill prevents the agent from claiming success.

    Why use this skill

    • Eliminate Guesses: No more "I've fixed the bug" followed by a broken build. You get the receipts.
    • CI/CD Readiness: Since it exits with non-zero codes on failure, it acts as a local pre-flight check before you even push to a branch.
    • Reproducibility: Every fix includes the exact reproduction command and the passing test run in a single output block.
    • Professional Handoffs: Ideal for developers who want their AI agents to deliver work that is already verified against ruff, pytest, or curl-based smoke tests.

    Use Cases

    • Enforce proof-of-work by requiring command logs for every task completion.
    • Generate instant "receipts" for bug fixes including reproduction and resolution.
    • Gate code submissions on linting, testing, and manual smoke-test results.
    • Standardize how agents report success across different frameworks and tools.

    Reviews

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Network Access

    Tags

    testing
    ci-cd
    automation
    python
    verification
    quality-assurance

    Requires Python 3.8+. No external dependencies (standard library only). Works with any SKILL.md-compatible agent (Claude Code, Cursor, Codex CLI, Gemini CLI).

    Creator

    Ifásola

    Frequently Asked Questions

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