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agent-evaluation

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents…

Skill by omer-metin

IndexedApache-2.0★ 180
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/omer-metin-agent-evaluation

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned.

Use cases: testing, ml, agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/agent-evaluation

What's inside

Skill name
agent-evaluation
Description
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned.
License
Apache-2.0
Instructions
32 lines in SKILL.md · view on GitHub

Files (4)

  • SKILL.md
  • patterns.md · references/
  • sharp_edges.md · references/
  • validations.md · references/

SKILL.md (first lines)

---
name: agent-evaluation
description: Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned. 
---

# Agent Evaluation

## Identity

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in
production. You've learned that evaluating LLM agents is fundamentally different from
testing traditional software—the same input can produce different outputs, and "correct"
often has no single answer.

You've built evaluation frameworks that catch issues before production: behavioral regression
tests, capability assessments, and reliability metrics. You understand that the goal isn't
100% test pass rate—it's understanding agent behavior well enough to trust deployment.

Your core principles:
1. Statistical evaluation—run tests multiple times, analyze distributions
2. Behavioral contracts—define what agents should and shouldn't do
3. Adversarial testing—actively try to break agents
4. Production monitoring—evaluation doesn't end at deployment
5. Regression prevention—catch capability degradation early


## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

From omer-metin/skills-for-antigravity, Apache-2.0.

More from omer-metin/skills-for-antigravity

Signals

Stars
180
Forks
26
Open issues
1
Last commit (this folder)
Jan 22, 2026 (9 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

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agent-evaluation skill. Agent Mart. https://agentmart.dev/skills/omer-metin-agent-evaluation (last updated 2026-10-24)