What makes AI Agent Evaluator notable?

1.3K downloads. Trust evidence available

How should AI Agent Evaluator be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

AI Agent Evaluator

AI-powered agent evaluation and benchmarking assistant — design evaluation suites, run structured assessments (task completion rate, latency, safety, reasoning accuracy), compare multi-agent frameworks (CrewAI, LangChain, AutoGen), generate benchmark reports, and guide developers in selecting the right evaluation methodology. Built for AI engineers, product managers, and ML teams shipping agent-based applications to production. Keywords: AI agent evaluation, agent benchmarking, LLM testing, CrewAI, AutoGen, LangChain, SWE-bench, AgentBench, AI quality assurance, agent reliability. Skill: AI Agent Evaluator Owner: gechengling Summary: AI-powered agent evaluation and benchmarking assistant — design evaluation suites, run structured assessments (task completion rate, latency, safety, reasoning accuracy), compare multi-agent frameworks (CrewAI, LangChain, AutoGen), generate benchmark reports, and guide developers in selecting the right evaluation methodology. Built for AI engineers, product manage

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.3k

Updated

Oct 10, 2026

Version

3.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.3K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.3K downloadsadoption · observed Oct 10, 2026
Latest release: 3.0.3release · observed Sep 15, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:ai-agent-evaluator

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-ai-agent-evaluator/snapshot"

Documentation

CLAWHUB

71,796 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: AI Agent Evaluator
description: >
  AI-powered agent evaluation and benchmarking assistant — design evaluation suites,
  run structured assessments (task completion rate, latency, safety, reasoning accuracy),
  compare multi-agent frameworks (CrewAI, LangChain, AutoGen), generate benchmark reports,
  and guide developers in selecting the right evaluation methodology. Built for AI engineers,
  product managers, and ML teams shipping agent-based applications to production.
  Keywords: AI agent evaluation, agent benchmarking, LLM testing, CrewAI, AutoGen,
  LangChain, SWE-bench, AgentBench, AI quality assurance, agent reliability.
version: "3.0.3"
---

# AI Agent Evaluator

**Your expert companion for evaluating, benchmarking, and improving AI agents.**

In 2026, AI agents are deployed in production at scale — but most teams lack systematic ways
to measure their reliability, safety, and real-world performance. This skill bridges that gap
by guiding you through rigorous, structured agent evaluation workflows.

> **Security & data notice**
> - This skill provides **methodology and advisory guidance only**. It does not execute code,
>   call APIs, or access any system.
> - It does **not** collect credentials, process personal data, or open network connections.
> - When you share agent logs or transcripts for analysis, **anonymise them first** — remove
>   customer names, account numbers, contact details, and any regulated data.
> - Evaluation results must be reviewed by qualified ML engineers before release decisions.

---

## What This Skill Does

- **Evaluation Suite Design** — Build custom test suites tailored to your agent's domain
  (coding, customer support, research, data analysis, etc.)
- **Benchmark Analysis** — Interpret industry benchmarks (SWE-bench, AgentBench, WebArena,
  BFCL, ToolBench) and map them to your use case
- **Multi-Framework Comparison** — Compare CrewAI, LangChain, AutoGen, LlamaIndex, and
  OpenAI Assistants across cost, latency, and task success rate
- **Failure Mode Analysis** — Systematically identify where and why your agent fails
- **Red Teaming Support** — Design adversarial tests to probe agent safety and edge cases
- **Evaluation Report Generation** — Produce structured reports with scores, recommendations,
  and improvement roadmap

---

## Trigger Phrases

**English:**
- "evaluate my AI agent"
- "benchmark this agent"
- "compare CrewAI vs LangChain"
- "how to test an AI agent"
- "agent quality assurance"
- "my agent keeps failing at X"
- "design evaluation suite for agent"
- "agent red teaming"
- "production readiness check for agent"

**Chinese / 中文:**
- AI Agent 评估
- 智能体基准测试
- Agent 质量保障
- 如何测试 AI Agent
- 比较 CrewAI 和 LangChain
- Agent 失败分析
- 大模型 Agent 上线前检查
- 智能体对比测试
- Agent 红队测试
- 智能体上线门禁 / Agent 回归测试

---

## AI Governance & Market Watch (as of 2026-09-15)

| Area | What is moving | What it means for evaluation work |
|------|----------------|-----------------------------------|
| Governance | Agenti

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "ai-agent-evaluator",
  "version": "3.0.3",
  "publishedAt": 1789482303729
}

skill-card.md

## Description:

AI Agent Evaluator helps AI engineers, product managers, and ML teams design evaluation suites, assess task completion, latency, safety, and reasoning quality, compare agent frameworks, and generate benchmark reports for production agents.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, ML platform teams, product managers, and QA engineers use this skill to plan and review AI agent evaluations before production release. It supports benchmark interpretation, custom suite design, failure analysis, red-team planning, framework comparison, and structured evaluation reporting.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Agent logs or transcripts may contain customer, account, contact, or regulated data.

Mitigation: Anonymize inputs before using the skill and remove sensitive or regulated data.

Risk: Benchmark, governance, and tooling claims can become stale.

Mitigation: Verify time-sensitive claims against current official sources before relying on them.

Risk: Evaluation guidance can be misapplied as a final release decision without expert review.

Mitigation: Have qualified ML engineers and relevant security reviewers approve evaluation results before deployment decisions.

## Reference(s):

- [AI Agent Evaluator ClawHub page](https://clawhub.ai/gechengling/skills/ai-agent-evaluator)

## Skill Output:

**Output Type(s):** [Text, Markdown, Configuration, Guidance]

**Output Format:** [Markdown tables, checklists, scoring rubrics, evaluation reports, and structured recommendations]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Advisory content only; no code execution, API calls, or system access.]

## Skill Version(s):

3.0.3 (source: frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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