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agentbench

Benchmark your OpenClaw agent across 40 real-world tasks. Tests file creation, research, data analysis, multi-step workflows, memory, error handling, and tool efficiency. Not a coding benchmark — measures your agent setup and config.

exe2150 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install exe215/agentbench

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

AgentBench for OpenClaw

Benchmark your OpenClaw agent's general capabilities across 40 real-world tasks spanning 7 domains.

Commands

When the user says any of these, follow the corresponding instructions:

  • /benchmark — Run the full benchmark suite (all 40 tasks)
  • /benchmark --fast — Run only easy+medium tasks (19 tasks)
  • /benchmark --suite <name> — Run one domain only
  • /benchmark --task <id> — Run a single task
  • /benchmark --strict — Tag results as externally verified scoring
  • /benchmark-list — List all tasks grouped by domain
  • /benchmark-results — Show results from previous runs
  • /benchmark-compare — Compare two runs side-by-side

Flags are combinable: /benchmark --fast --suite research

Running a Benchmark

Step 1: Discover Tasks

Read task.yaml files from the tasks/ directory in this skill:

tasks/{suite-name}/{task-name}/task.yaml

Each task.yaml contains: name, id, suite, difficulty, mode, user_message, input_files, expected_outputs, expected_metrics, scoring weights.

Filter by --suite or --task if specified. If --fast is set and --task is not, filter to only tasks where difficulty is "easy" or "medium".

Profile is "fast" if --fast was specified, otherwise "full".

List discovered tasks with count and suites.

Step 2: Set Up Run Directory

Generate a run ID from the current timestamp: YYYYMMDD-HHmmss

Read suite_version from skill.json in this skill directory.

Create the results directory:

agentbench-results/{run-id}/

Announce: Starting AgentBench run {run-id} | Profile: {profile} | Suite version: {suite_version} | Tasks: {count}

Step 3: Execute Each Task

For each task:

  1. Set up workspace:
    • Create /tmp/agentbench-task-{task-id}/ as workspace
    • Copy input files from tasks/{suite}/{task}/inputs/ to the workspace (if inputs/ exists)
    • If the task directory contains a setup.sh: run `bash tasks/{suite}/{task}/setup.sh {wor...

Source information

Source
github
Repository
openclaw/skills
Path
skills/exe215/agentbench
Version
—
Commit
ca3b265a98f7fca906fe5eed361720a63a51ef5f
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

README

AgentBench for OpenClaw

Benchmark your OpenClaw agent's general capabilities across 40 real-world tasks spanning 7 domains.

Not a coding benchmark — tests file creation, research, data analysis, multi-step workflows, memory, error handling, and tool efficiency.

Same tasks and scoring as the Claude Code version. Results are cross-platform comparable and submit to the same leaderboard.

Install

Place this skill in your OpenClaw skills directory, or clone directly:

git clone https://github.com/agentbench/agentbench-openclaw.git ~/.openclaw/skills/agentbench

Quick Start

/benchmark                              # Run all 40 tasks (full profile)
/benchmark --fast                       # Run 19 easy+medium tasks (fast profile)
/benchmark --suite research             # Run one domain
/benchmark --suite research --fast      # Run easy+medium in one domain
/benchmark --task research-summarize-doc # Run one task
/benchmark --strict                     # Tag as externally verified

Domains

Domain Tasks Difficulty What It Tests
File Creation 9 2E, 3M, 4H Documents, spreadsheets, project scaffolding, config migration, skill graphs
Research 5 3M, 2H Summarize, compare, multi-source synthesis, git archaeology
Data Analysis 5 1E, 1M, 1H, 1X Anomalies, statistics, multi-format reconciliation, log pattern detection
Multi-Step 5 1M, 2H, 2X Data pipelines, log analysis, repo refactoring, release preparation
Memory 5 2M, 1H, 1X Recall, constraints, context switching, progressive accumulation
Error Handling 6 1E, 2M, 3H Corrupted input, cascading failures, misleading errors, partial recovery
Tool Efficiency 5 3E, 2H Minimal reads, right tool choice, codebase navigation, targeted fixes

E=Easy, M=Medium, H=Hard, X=Expert

Scoring

Each t...

Extracted content

SKILL.md
# AgentBench for OpenClaw

Benchmark your OpenClaw agent's general capabilities across 40 real-world tasks spanning 7 domains.

## Commands

When the user says any of these, follow the corresponding instructions:

- **`/benchmark`** — Run the full benchmark suite (all 40 tasks)
- **`/benchmark --fast`** — Run only easy+medium tasks (19 tasks)
- **`/benchmark --suite <name>`** — Run one domain only
- **`/benchmark --task <id>`** — Run a single task
- **`/benchmark --strict`** — Tag results as externally verified scoring
- **`/benchmark-list`** — List all tasks grouped by domain
- **`/benchmark-results`** — Show results from previous runs
- **`/benchmark-compare`** — Compare two runs side-by-side

Flags are combinable: `/benchmark --fast --suite research`

## Running a Benchmark

### Step 1: Discover Tasks

Read task.yaml files from the `tasks/` directory in this skill:

```
tasks/{suite-name}/{task-name}/task.yaml
```

Each task.yaml contains: name, id, suite, difficulty, mode, user_message, input_files, expected_outputs, expected_metrics, scoring weights.

Filter by `--suite` or `--task` if specified. If `--fast` is set and `--task` is not, filter to only tasks where difficulty is "easy" or "medium".

Profile is "fast" if `--fast` was specified, otherwise "full".

List discovered tasks with count and suites.

### Step 2: Set Up Run Directory

Generate a run ID from the current timestamp: `YYYYMMDD-HHmmss`

Read `suite_version` from `skill.json` in this skill directory.

Create the results directory:
```
agentbench-results/{run-id}/
```

Announce: `Starting AgentBench run {run-id} | Profile: {profile} | Suite version: {suite_version} | Tasks: {count}`

### Step 3: Execute Each Task

For each task:

1. **Set up workspace**:
   - Create `/tmp/agentbench-task-{task-id}/` as workspace
   - Copy input files from `tasks/{suite}/{task}/inputs/` to the workspace (if inputs/ exists)
   - If the task directory contains a `setup.sh`: run `bash tasks/{suite}/{task}/setup.sh {wor...
README
# AgentBench for OpenClaw

Benchmark your OpenClaw agent's general capabilities across 40 real-world tasks spanning 7 domains.

Not a coding benchmark — tests file creation, research, data analysis, multi-step workflows, memory, error handling, and tool efficiency.

Same tasks and scoring as the [Claude Code version](https://github.com/agentbench/agentbench). Results are cross-platform comparable and submit to the same [leaderboard](https://www.agentbench.app/leaderboard).

## Install

Place this skill in your OpenClaw skills directory, or clone directly:

```bash
git clone https://github.com/agentbench/agentbench-openclaw.git ~/.openclaw/skills/agentbench
```

## Quick Start

```
/benchmark                              # Run all 40 tasks (full profile)
/benchmark --fast                       # Run 19 easy+medium tasks (fast profile)
/benchmark --suite research             # Run one domain
/benchmark --suite research --fast      # Run easy+medium in one domain
/benchmark --task research-summarize-doc # Run one task
/benchmark --strict                     # Tag as externally verified
```

## Domains

| Domain | Tasks | Difficulty | What It Tests |
|--------|-------|------------|---------------|
| File Creation | 9 | 2E, 3M, 4H | Documents, spreadsheets, project scaffolding, config migration, skill graphs |
| Research | 5 | 3M, 2H | Summarize, compare, multi-source synthesis, git archaeology |
| Data Analysis | 5 | 1E, 1M, 1H, 1X | Anomalies, statistics, multi-format reconciliation, log pattern detection |
| Multi-Step | 5 | 1M, 2H, 2X | Data pipelines, log analysis, repo refactoring, release preparation |
| Memory | 5 | 2M, 1H, 1X | Recall, constraints, context switching, progressive accumulation |
| Error Handling | 6 | 1E, 2M, 3H | Corrupted input, cascading failures, misleading errors, partial recovery |
| Tool Efficiency | 5 | 3E, 2H | Minimal reads, right tool choice, codebase navigation, targeted fixes |

*E=Easy, M=Medium, H=Hard, X=Expert*

## Scoring

Each t...

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