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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/haofei-bench-agent
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About
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.
More from Haofei/air
Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- May 19, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Haofei/air
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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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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Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
bench-agent skill. Agent Mart. https://agentmart.dev/skills/haofei-bench-agent (last updated 2026-10-24)