critique gate

    1

    Enforce quality gates using single-critic, multi-lens, adversarial, or ensemble review patterns via loop-ledger.

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    critique gate

    Example session with this skill installed

    /critique-gate:critique-multi review this python auth logic for security, style, and performance independently. don't ship until they all clear a 0.9 score.

    • Read your context and instructions
    • Compiled the critique gate

    I have initiated a multi-critic gate for the authentication logic. I will act as three independent reviewers and rewrite the code until the weakest score clears 0.9.

    Multi-Critic Gate: Auth Logic

    Round 1 Results:

    • Security: 0.6 (Missing bcrypt salt, potential timing attack on comparison)
    • Style: 0.9 (Clear naming, follows PEP8)
    • Performance: 0.8 (Redundant DB lookup in loop)
    • Current Progress (Min Score): 0.6
    • Status: should_continue: true

    Rewriting to address Security and Performance gaps...

    Round 2 Results:

    • Security: 0.95 (Implemented constant-time comparison and salted hashing)
    • Style: 0.9 (Maintained)
    • Performance: 0.92 (Cached DB lookup results)
    • Current Progress (Min Score): 0.9
    • Status: target_reached
    import bcrypt
import hmac

def verify_user(username, provided_password, user_db):
    # Optimized: Single lookup
    user_record = user_db.get(username)
    if not user_record:
        return False
    
    # Security: Constant-time comparison to prevent timing attacks
    return bcrypt.checkpw(
        provided_password.encode('utf-8'), 
        user_record['password_hash']
    )

    Next steps

    1. Review the implemented constant-time comparison.
    2. Verify the user_db.get integration with your specific database driver.
    3. Close the loop ledger entry to finalize the session.

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

    About this skill

    LLMs often call a draft "done" even when sections are vague or unsupported. This skill separates the generator from the critic, ensuring outputs clear a specific quality bar before they ship. It implements four distinct review patterns to prevent the model from grading its own homework.

    What it does

    • Score-retry gates outputs behind a single critic scoring against a rubric.
    • Multi-critic reviews the same draft through independent lenses like security, style, and correctness, gating on the weakest score.
    • Adversarial subjects the draft to active red-teaming, requiring all objections to be rebutted or patched.
    • Judge-ensemble aggregates opinions from multiple independent judges to surface disagreement and variance.
    • Enforced Retries blocks the session from closing until the quality target is met or the budget is exhausted.

    How it works

    1. The user invokes a specific mode like /critique-gate:critique-score or /critique-gate:critique-adversarial.
    2. The skill initiates a loop via loop-ledger, setting a target_value (default 0.8) and max_iterations.
    3. A critic role analyzes the draft, reporting scores as progress and specific issues as state.
    4. If the gate isn't cleared, the generator rewrites the draft based on the critic's feedback and ticks the loop again.

    Frameworks & tools

    This skill requires the loop-ledger MCP plugin. It uses loop_start, loop_tick, and loop_end tools to manage state and enforcement.

    Why this beats prompting it yourself

    Prompting a model to "check its work" usually results in sycophancy where the model confirms its own brilliance. This skill enforces a structural separation of concerns, uses scalar progress tracking to detect stalls, and allows for multi-modal critiques that a single prompt cannot reliably coordinate.

    Use cases

    • Security Audits: Run adversarial red-teaming on design docs to catch exploits before implementation.
    • PR Reviews: Validate code against independent correctness, style, and safety lenses.
    • Policy Proposals: Use a judge ensemble to ensure consensus across multiple independent viewpoints.
    • Content Quality: Gate technical summaries behind a strict 0.8 rubric score to ensure metrics aren't missing.

    Known limitations

    This skill has no internal logic or server; it is a prose-driven orchestration of the loop-ledger plugin. It cannot gate on variance in ensemble mode as a native exit condition.

    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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