agent-evaluation
Use when evaluating agent outputs, designing benchmarks, or building self-evaluation rubrics.
Skill by HarleyBartles
Install
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/harleybartles-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
Use cases: agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
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
- Use when evaluating agent outputs, designing benchmarks, or building self-evaluation rubrics.
- License
- MIT
- Metadata
- owner: Harley Bartles · status: active · source-id: agent-evaluation · source-path: skills/agent-evaluation/SKILL.md · provenance-name: Agent Evaluation first-party skill · source-category: first_party
- Instructions
- 27 lines in SKILL.md · view on GitHub
Files (6)
- SKILL.md
- openai.yaml · agents/
- CITATIONS.md · assets/authority/
- authority.yaml · assets/authority/
- source-map.yaml · assets/authority/
- operational-guidance.md · references/
SKILL.md (first lines)
# Agent Evaluation Use this skill when evaluating agent outputs, designing benchmarks, or building self-evaluation rubrics. ## When to Use - Evaluating an agent's output against a rubric. - Designing or running a benchmark. - Building a self-evaluation workflow. ## Core Pattern 1. Define the evaluation dimension (correctness, completeness, safety, concision) and scoring anchors before scoring. 2. Build or select benchmark tasks that reflect real deployment conditions and hold out a final test set. 3. Score outputs against rubrics or reference solutions, not just model self-ratings. 4. Report per-task and per-dimension results with failure modes and confidence intervals. 5. Keep the evaluation harness separate from the evaluated agent to avoid leakage. ## Common Mistakes - Reporting a single aggregate score without a breakdown. → Report per-dimension and per-task results. - Reusing the test set as a development target. → Hold out a final evaluation set. - Evaluating on politeness or verbosity instead of outcomes. → Score end results against criteria. Load `references/operational-guidance.md` for deeper coverage of benchmark design, scoring, and rubrics.
From HarleyBartles/agent-asset-marketplace, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 28, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Use when aligning repository operating-model standards, assessing adoption, or deciding which focused standard owns a requested change.
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Cite this page
agent-evaluation skill. Agent Mart. https://agentmart.dev/skills/harleybartles-agent-evaluation (last updated 2026-10-24)