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bench-agent

Skill by Haofei

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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/haofei-bench-agent

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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Use cases: agent-building

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What's inside

Skill name
bench-agent
Description
None
License
MIT
Instructions
56 lines in SKILL.md · view on GitHub

Agent Skills spec issues

  • missing YAML frontmatter
  • name: missing
  • description: missing

Files (4)

  • SKILL.md
  • air-skill.yaml
  • bench.air-profile.yaml
  • tools.json

SKILL.md (first lines)

# AIR Bench Agent

Use this executor for controlled comparisons between AIR agent/tool/profile variants, such as Semble versus grep/read/LSP, core code-agent versus subagent-enabled code-agent, or one prompt/profile shape versus another.

## Operating Model

Run experiments in isolated temporary workdirs, usually under `/tmp/air-bench-agent-*`, and leave the source workspace unchanged. Prefer using existing AIR benchmark commands and trace artifacts. When a candidate needs temporary profiles or tool configs, create them inside the temporary clone/workdir and include their paths in the report.

## Benchmark Method

For each comparison:

1. Define the task and the compared variants.
2. Run a baseline first.
3. Run each candidate with the same task, model config, and comparable budget.
4. Capture trace paths and output/artifact paths.
5. Extract metrics from trace JSONL:
   - `model_calls`
   - `tool_calls`
   - per-tool counts
   - `max_provider_request_bytes`
   - `sum_provider_request_bytes`
   - `max_input_bytes`
   - `sum_input_bytes`
   - child subagent trace metrics when present
6. Check whether each run completed the task correctly and whether verification passed.
7. Explain the first meaningful behavioral divergence.
8. Recommend whether the candidate should be default, optional, rejected, or tested further.

## Report Format

Return a concise structured report:

```text
Task
- ...

Variants
- baseline: ...
- candidate: ...

Metrics
| variant | success | model calls | tool calls | request bytes | notes |

Trace Findings
- ...

Recommendation
- default / optional / reject / needs more benchmark
- rationale
```

## Decision Rules

A candidate is not worth default integration unless it improves success rate or materially reduces model calls/request bytes without increasing failure modes. A candidate may still be worth optional integration if it helps a narrow class of tasks and stays isolated from the default code-agent path.

From Haofei/air, MIT.

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Last commit (this folder)
May 19, 2026 (5 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Cite this page

bench-agent skill. Agent Mart. https://agentmart.dev/skills/haofei-bench-agent (last updated 2026-10-24)