Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
AgentBench runs 40 real-world tasks across 7 domains to measure how well an OpenClaw agent performs. Tasks cover file creation, research, data analysis, multi-step workflows, memory, and error handling. Results are scored across four weighted layers and can be submitted to a public leaderboard.
Measures actual agent behavior across diverse real-world tasks rather than synthetic or code-only benchmarks, and the objective scoring layers (55% of total weight) can't be self-inflated.
When to use it
- Checking if a new agent config handles multi-step tasks reliably
- Comparing agent performance before and after changing tool settings
- Running a fast subset of tests to catch regressions after updates
- Identifying which task domains an agent consistently underperforms in
- Submitting scored results to the AgentBench public leaderboard
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: /benchmark --fast
- 1Reads task.yaml files from the tasks/ directory, filtering to easy and medium difficulty tasks only
- 2Creates a timestamped run directory under agentbench-results/
- 3Executes each task in an isolated workspace, tracking tool calls, errors, and timing
- 4Scores each task across four layers: structural file checks, metrics analysis, behavioral self-evaluation, and output quality
- 5Generates results.json with integrity signature, report.md, and report.html in the run directory
Overall score with per-domain breakdown, task detail table, and saved report files ready for review or submission