ZooEval is a self-evolving evaluation engine that blind-tests AI agent skills by running each task twice -- with and without the skill -- and grading outputs against deterministic assertions and LLM-as-judge rubrics. It maintains two closed feedback loops: one that sharpens its own evaluation methodology, and one that automatically rewrites underperforming skills. We evaluated 123 skills from the ClawHub marketplace -- 76.4% measurably improve model output, and the engine discovered that removing 60-80% of a skill's content outperforms adding to it.

Quick Start • Methodology • Leaderboard • Security Scan • Paper • Knowledge Base

• 76.4% of skills measurably improve model output over the baseline (no skill)
• Only 4% are actively harmful -- causing worse results than the model alone
• The #1 improvement strategy is Remove > Add -- deleting 60-80% of a skill's content outperforms adding to it
• The evaluation engine itself evolved from 7 phases (v0.1.0) to 12 phases (v0.4.0), catching failure modes each prior version missed
• 8 failure modes discovered, including phantom tooling (43% prevalence), reference manual bloat (21%), and template compliance drift

Clone the repo, then tell OpenClaw or any other AI agent what to evaluate. ZooEval is not a software tool -- it's a methodology that any AI agent can follow.

Just copy and paste the following prompts into your OpenClaw:

Clone https://github.com/SerendipityOneInc/ZooEval.git and read methodology/SKILL-EVAL.md,
then evaluate the ClawHub skill "explain-code".
Download it from ClawHub first, then run the full evaluation.

Replace the skill path below with your actual skill location before copying.

Clone https://github.com/SerendipityOneInc/ZooEval.git and read methodology/SKILL-EVAL.md,
then evaluate the skill at ~/.agents/skills/my-skill/SKILL.md

Replace the directory path below with your actual skills folder before copying.

Clone https://github.com/SerendipityOneInc/ZooEval.git and read methodology/SKILL-EVAL.md,
then evaluate all skills under ~/.agents/skills/
Process them one by one, generate a score card for each, and build a leaderboard.

That's it. The agent reads the methodology, generates test cases, runs blind A/B tests, grades the outputs, and produces a score card.

Browse the Leaderboard or read individual Skill Cards. Each card includes score, strengths, weaknesses, and a recommendation.

Read the Improvement Patterns -- proven strategies that raised scores by 1.5-2.0 points. Also check the Failure Modes to avoid common anti-patterns.

The core insight: Skills should be behavioral contracts, not textbooks. Use MUST/ALWAYS/NEVER mandates. Delete reference content the model already knows.

The most counterintuitive finding: removing content from underperforming skills works better than adding content.

Skills that scored poorly were typically 200+ lines of educational content the model already knows. The proven improvement formula:

1. Delete 60-80% of reference/tutorial content
2. Add 5-10 behavioral mandates (MUST/ALWAYS/NEVER rules)
3. Keep output format specs and trigger conditions
4. Target < 100 lines

This strategy successfully improved 3/3 tested skills by +1.5 to +2.0 points. See Improvement Patterns for the full playbook.

ZooEval uses blind A/B testing: every task runs twice on the same model -- once with the skill, once without. Outputs are graded against deterministic assertions (file checks, keyword matching) and LLM-as-judge rubrics.

The engine has two self-evolving loops:

• Evaluation loop: evaluate → learn → improve methodology → better evaluations
• Improvement loop: improve skill → re-evaluate → learn what worked → better improvements

See SKILL-EVAL.md for the full 12-phase specification.

ZooEval/
├── README.md                        # This file
├── zooeval/                       # Core Python package
│   └── security/                    # Security evaluation module (docs/security.md)
├── paper/
│   ├── ZooEval.pdf               # Research paper
│   └── ZooEval.tex               # LaTeX source
├── methodology/
│   ├── SKILL-EVAL.md                # Self-evolving eval engine spec
│   └── VERSION                      # Engine version
├── docs/
│   └── security.md                  # Security scan usage guide
├── knowledge/                       # Accumulated eval wisdom
│   ├── lessons.md                   # What worked, what didn't
│   ├── eval-patterns.md             # Reusable assertion templates
│   ├── failures.md                  # Failure mode catalog
│   └── improve/                     # Skill improvement engine knowledge
│       ├── patterns.md              # Proven improvement strategies
│       ├── lessons.md               # Improvement-specific lessons
│       └── failures.md              # What NOT to try
├── results/
│   ├── leaderboard/index.html       # Interactive HTML leaderboard
│   └── skill-cards/                 # 415 individual eval reports
├── tests/                           # Unit & integration tests
└── scripts/                         # Card & leaderboard generators
    ├── generate_skill_card.py
    ├── generate_leaderboard.py
    └── llm_judge_prompt.md

@article{wang2026zooeval,
  title={ZooEval: A Self-Evolving Framework for Evaluating and Improving AI Agent Skills},
  author={Wang, Ji and Li, Xiaopu and Xu, Wenhao and Hu, Ning},
  year={2026},
  institution={Serendipity One Inc.}
}

Apache License 2.0 -- see LICENSE for details.

Built with ZooEval v0.4.0 | Evaluated on Claude Opus 4 | March 2026