Skill Bundles

    Read the source. Install what you trust.

    Each skill bundle packages a reusable agent behavior: a prompt, supporting files, and evaluation criteria. Browse the public catalog, review the full source, then install a private copy you can edit and experiment with.

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    109 published bundles ready to inspect and install

    Skill bundlev1.0.0

    Eval Versioning And Regression

    Track eval suite changes over time, detect regressions when evals are updated

    0 installs
    70/100 quality
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    Skill bundlev1.0.0

    Domain Specific Eval Design

    Build evals for specialized verticals (legal, medical, finance, engineering)

    0 installs
    70/100 quality
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    Skill bundlev1.0.0

    Eval Contamination Prevention

    Ensure training data and eval data don't overlap

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    70/100 quality
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    Skill bundlev1.0.0

    Adversarial Eval Generation

    Create evals specifically designed to find failure modes and edge cases

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    70/100 quality
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    Skill bundlev1.0.0

    Eval Saturation Detection

    Identify when a model has maxed out an eval and needs harder/different benchmarks

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    70/100 quality
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    Skill bundlev1.0.0

    Eval Coverage Analysis

    Measure whether your eval suite covers the actual distribution of production tasks

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    70/100 quality
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    Skill bundlev1.0.0

    Build Fuzzy Eval

    Design evals for tasks with multiple valid solutions (writing, design, open-ended code)

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    70/100 quality
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    Skill bundlev1.0.0

    Outcome VS Process Reward Tradeoff

    When to reward final results vs. intermediate steps, and how to blend both

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    70/100 quality
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    Skill bundlev1.0.0

    Reward Calibration

    Ensure reward functions produce consistent, well-scaled signals across different task types and difficulties

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    70/100 quality
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    Skill bundlev1.0.0

    Human Feedback Collection

    Design interfaces and protocols for collecting human preference judgments at scale

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    70/100 quality
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    Skill bundlev1.0.0

    Reward Hacking Detection

    Identify when agents exploit reward function loopholes to get high scores without doing the task correctly

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    70/100 quality
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    Skill bundlev1.0.0

    Reward Shaping

    Add intermediate reward signals that guide learning without changing the optimal policy

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    70/100 quality
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