Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
AgentBench
Benchmark your OpenClaw agent across 40 real-world tasks. Tests file creation, research, data analysis, multi-step workflows, memory, error handling, and too...
skill openclawclawhub Free
Benchmark your OpenClaw agent across 40 real-world tasks. Tests file creation, research, data analysis, multi-step workflows, memory, error handling, and too...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, lib/metrics.sh, skill.json, tasks/data-analysis/cross-reference/inputs/inventory.csv, tasks/data-analysis/cross-reference/inputs/orders.csv
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Exe215
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 โ Run the full benchmark suite (all 40 tasks) /benchmark --fast โ Run only easy+medium tasks (19 tasks) /benchmark --suite <name> โ Run one domain only /benchmark --task <id> โ Run a single task /benchmark --strict โ Tag results as externally verified scoring /benchmark-list โ List all tasks grouped by domain /benchmark-results โ Show results from previous runs /benchmark-compare โ Compare two runs side-by-side Flags are combinable: /benchmark --fast --suite research
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: 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 {workspace-path} For file-unchanged validators: compute checksums of specified files after setup, before task execution Announce: Running: {task.name} [{task.suite}] (difficulty: {task.difficulty}) Record start time (milliseconds): date +%s%3N Execute the task yourself directly: Read the task's user_message and execute it as if a real user sent you the request Work ONLY within the workspace directory If input files are listed, read them from the workspace Execute naturally โ use the appropriate tools (read, write, edit, exec, web_search, web_fetch, etc.) Create any output files in the workspace directory When done, write a brief execution-trace.md to the workspace: What you understood the task to be What approach you took What files you created or modified Any difficulties or decisions you made Record end time and compute duration Collect metrics: total_time_ms: end - start tool_calls_total: count how many tool calls you made during this task errors: count any tool call failures planning_ratio: estimate the fraction of time spent reading/thinking vs producing output (approximate is fine) Layer 0 โ Automated Structural Checks (compute directly): After task execution, check the workspace. For each entry in expected_outputs: file-exists: Check if file exists. 30 points if found, 0 if not. content-contains: Read file, check each required section keyword (case-insensitive). Points proportional to matches found. Pool: 40 points. word-count-range: Count words. In range = 30 points. Within 2x range = 15 points. Outside = 0. git-log-contains: Check git log for expected strings. 30 points if all found, proportional otherwise. directory-structure: Check all paths exist. 30 points if all present, proportional for partial. command-output-contains: Run command, check output contains all strings. 30 points if match, 0 if not. file-unchanged: Compare checksum against pre-execution checksum. 30 points if unchanged, 0 if modified. link-consistency: Scan files for link syntax consistency. 30 points if consistent, 15 if mostly consistent (>70% one style), 0 if mixed. Normalize total to 0-100. Layer 1 โ Metrics Analysis (compute directly): If task has expected_metrics: Tool calls within expected range: 40 points Tool calls within 2x range: 20 points Outside 2x range: 0 points Planning ratio within expected range: 30 points Planning ratio outside but within 2x: 15 points Way off: 0 points Zero errors: 30 points 1-2 errors: 15 points 3+ errors: 0 points Normalize to 0-100. If no metrics available, score as 50. Token estimate is tracked for reporting but NOT scored. Layer 2 โ Behavioral Analysis (self-evaluate honestly, 0-100): Score based on HOW you executed: Instruction Adherence (30 points): 30: Followed all instructions precisely 20: Mostly followed, minor deviations 10: Significant deviations 0: Ignored or misunderstood Tool Appropriateness (25 points) โ rule-based first: Penalty: -10 for each use of exec cat instead of read to read files Penalty: -10 for each use of exec echo/printf instead of write to create files Penalty: -5 for each use of exec sed/awk instead of edit for file edits Start at 25, apply penalties, floor at 0 Approach Quality (25 points) โ check read-before-write: 25: Read all inputs before producing output 15: Read most inputs, minor gaps 5: Started producing output without reading context 0: No clear approach Error Recovery (20 points): 20: Clean recovery or no errors occurred 10: Partial recovery 0: Failed to recover Layer 3 โ Output Quality (self-evaluate honestly, 0-100): Score the deliverable: Completeness (25): All requirements met? Gaps? Accuracy (25): Content correct? Calculations right? Formatting (25): Well-structured? Correct file format? Polish (25): Would a user be satisfied? Compute composite score: score = (L0 ร 0.20) + (L1 ร 0.35) + (L2 ร 0.20) + (L3 ร 0.25) Use weights from task.yaml if specified, otherwise these defaults. Save task result to agentbench-results/{run-id}/{task-id}/: scores.json: All layer scores, composite, breakdown, notes metrics.json: Timing, tool calls, errors, planning ratio Copy output files Display: {task.name}: {composite}/100 (L0:{l0} L1:{l1} L2:{l2} L3:{l3})
Step 4: Generate Report
After all tasks: Compute domain averages (group by suite, average composite scores) Compute overall score (average of domain scores โ equal domain weighting) Compute aggregate metrics Generate three files in agentbench-results/{run-id}/: results.json โ Machine-readable with this structure: { "run_id": "20260222-143022", "timestamp": "2026-02-22T14:30:22Z", "platform": "openclaw", "mode": "sandboxed", "profile": "full", "suite_version": "1.0.0", "scoring_method": "self-scored", "overall_score": 74, "duration_ms": 754000, "task_count": 40, "metrics": { "total_tool_calls": 187, "total_errors": 3, "avg_planning_ratio": 0.28, "est_tokens": 245000 }, "domain_scores": {}, "tasks": [] } If --strict was used, set scoring_method to "externally-verified". Integrity signature: After building results.json (without signature field), compute: SIG=$(echo -n "$CONTENT" | openssl dgst -sha256 -hmac "agentbench-v1-{run_id}-{suite_version}-integrity" | awk '{print $2}') Add as "signature" field to results.json. report.md โ Markdown summary: Overall Score, Metrics, Domain Breakdown, Task Details, Top Failures, Recommendations. report.html โ Self-contained HTML dashboard (inline CSS/JS, no external deps): Score display with color (green 80+, yellow 60-79, red <60) Domain cards with score bars Task detail table (sortable, expandable) Top failures section Dark mode via prefers-color-scheme Footer: "Generated by AgentBench v1.0.0 (OpenClaw) | Suite v{suite_version} | Profile: {profile}"
Step 5: Present Results
Display overall score Show domain breakdown Tell user where results are saved Mention they can submit to https://www.agentbench.app/submit
Step 6: Clean Up
Run teardown.sh if present. Remove temp workspace directories unless --keep-workspace was specified.
Listing Tasks (/benchmark-list)
Read all task.yaml files, group by suite, display as: ## file-creation (9 tasks) - project-scaffold [easy] - project-proposal [medium] ...
Viewing Results (/benchmark-results)
List all directories in agentbench-results/, show run ID, date, overall score, profile, and task count for each.
Comparing Runs (/benchmark-compare)
Show two runs side-by-side: overall scores, domain scores, and per-task deltas. Warn if profiles differ.
Key Differences from Claude Code Version
No hooks โ metrics are self-tracked (timing, tool call counting) No subagents โ you execute tasks directly in sequence Same tasks, same scoring, same output format โ results are cross-platform comparable Same integrity signature โ submissions work on the same leaderboard
Important Notes
Be honest in self-evaluation (L2/L3). Inflated scores are obvious on the leaderboard. The objective layers (L0 + L1) carry 55% of the weight โ they can't be faked. Token estimates are informational only, not scored. Any link syntax is accepted in skill graph tasks โ consistency is what's scored.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Files1 Scripts1 Config
- SKILL.md
- README.md
- lib/metrics.sh
- skill.json
- tasks/data-analysis/cross-reference/inputs/inventory.csv
- tasks/data-analysis/cross-reference/inputs/orders.csv