bambu-studio-ai
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install exe215/agentbenchExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Benchmark your OpenClaw agent's general capabilities across 40 real-world tasks spanning 7 domains.
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-sideFlags are combinable: /benchmark --fast --suite research
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.
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}
For each task:
/tmp/agentbench-task-{task-id}/ as workspacetasks/{suite}/{task}/inputs/ to the workspace (if inputs/ exists)setup.sh: run `bash tasks/{suite}/{task}/setup.sh {wor...Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
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.
Place this skill in your OpenClaw skills directory, or clone directly:
git clone https://github.com/agentbench/agentbench-openclaw.git ~/.openclaw/skills/agentbench
/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
| 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
Each t...
# 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...# 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...
Bambu Lab 3D printer control and automation. Activate when user mentions: printer status, 3D printing, slice, analyze model, generate 3D, AMS filament, print monitor, Bambu Lab, or any 3D printing task. Full pi...
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Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.