Borrowing it
Nothing to install: this file belongs to houarnu166/skillful-agent-system-builder. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/houarnu166/skillful-agent-system-builder/main/.agents/skills/agent-evaluation/SKILL.mdgit clone --depth 1 https://github.com/houarnu166/skillful-agent-system-builderWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/houarnu166/skillful-agent-system-builder/agent-evaluation)<a href="https://agentmods.dev/skills/houarnu166/skillful-agent-system-builder/agent-evaluation"><img src="https://agentmods.dev/badge/skills/houarnu166/skillful-agent-system-builder/agent-evaluation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/houarnu166/skillful-agent-system-builder/agent-evaluation"><img src="https://agentmods.dev/badge/skills/houarnu166/skillful-agent-system-builder/agent-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.00416 |
| Opus 5.5 | $0.00022 | $0.00166 |
| Sonnet 5.5 | $0.00011 | $0.00083 |
| Haiku 4.5 | $0.00006 | $0.00042 |
Grade A, and why
agent-evaluation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-evaluation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Agent Evaluation
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
Capabilities
- agent-testing
- benchmark-design
- capability-assessment
- reliability-metrics
- regression-testing
Requirements
- testing-fundamentals
- llm-fundamentals
Patterns
Statistical Test Evaluation
Run tests multiple times and analyze result distributions
Behavioral Contract Testing
Define and test agent behavioral invariants
Adversarial Testing
Actively try to break agent behavior
Anti-Patterns
❌ Single-Run Testing
❌ Only Happy Path Tests
❌ Output String Matching
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |
| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |
| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |
| Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |
Related Skills
Works well with: multi-agent-orchestration, agent-communication, autonomous-agents
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 65 lines · 56 tokens per session scan A ed17c3241688
agent-evaluation is a skill published in the GitHub repository houarnu166/skillful-agent-system-builder (23 stars, last pushed 8mo ago), licensed MIT. It adds 56 tokens to every session and 416 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-evaluation, differing in 0 lines, and is treated as a copy.
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