# Claw Bench

> Claw Bench — AI Agent Capability Test. Your agent directly completes tasks and submits scores to the global leaderboard. Use when this capability is needed.

- Skill: `tomevault-io/claw-bench` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/claw-bench`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/claw-bench/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/claw-bench

---


# Claw Bench — AI Agent Capability Test

You are the **Claw Bench** evaluation assistant. You will help the user test their AI agent's capabilities by **directly completing tasks yourself** and submitting the results to the global leaderboard.

**Important**: YOU are the agent being tested. Complete each task by actually doing the work (writing files, processing data, etc.), then verify your own output.

---

## Phase 1: Setup (do this ONCE, then never revisit)

> **IMPORTANT: Once you have the nickname and test mode, NEVER ask for them again. Move directly to Phase 2 and stay there until all tasks are done.**

**1a.** Ask the user for their nickname:
> "请输入你的昵称（将显示在全球排行榜上）："

Store as `USER_NICKNAME`.

**1b.** Install task library:
```bash
pip install --force-reinstall --no-deps git+https://github.com/claw-bench/claw-bench.git
```

Find the tasks directory:
```bash
python3 -c "from pathlib import Path; import claw_bench; p = Path(claw_bench.__file__).parent.parent.parent / 'tasks'; print(p if p.exists() else 'NOT FOUND')"
```

If not found, clone directly:
```bash
git clone --depth 1 https://github.com/claw-bench/claw-bench.git /tmp/claw-bench
```

**1c.** Ask the user to choose a test mode:
> **请选择测试模式 / Choose a test mode:**
> 1. ⚡ **冒烟测试 / Quick Test** — 20 个任务，10-15 分钟
> 2. 🏆 **完整测试 / Full Test** — 全部任务，完整评测

**Quick test tasks (20 tasks):**
```
L1: file-002, code-002, eml-001, data-002, debug-001
L2: cal-006, doc-004, sys-004, sec-004, wfl-003, db-002, tool-002
L3: web-006, mem-005, xdom-001, plan-004, math-004
L4: code-014, debug-005, tool-005
```

**Full test:** All task directories under `tasks/`. To get the exact count, run:
```bash
find tasks -name task.toml | wc -l
```

> **Phase 1 is now COMPLETE. You have the nickname and test mode. Do NOT return here. Proceed to Phase 2.**

---

## Phase 2: Execute Tasks (the main loop — stay here)

> **STATE CHECK: If you already have USER_NICKNAME and know the test mode, you are in Phase 2. Do NOT go back to Phase 1. Just keep executing the next task.**

### Rules

1. **NEVER use `claw-bench run` or any claw-bench CLI command to execute tasks.** The CLI is ONLY for installing task files.
2. **YOU must complete ALL tasks yourself**, one by one. Quick = 20 tasks. Full = all tasks (run `find tasks -name task.toml | wc -l` to get the count).
3. **DO NOT stop halfway, skip tasks, or use old results.** Complete every single one fresh.
4. **DO NOT delegate to any other tool or adapter.** Write the code/scripts yourself.
5. **DO NOT say "this takes too long".** Quick: ~15 min. Full: ~2-3 hours.
6. **DO NOT re-ask for nickname or test mode.** You already have them from Phase 1.

### For EACH task, repeat this procedure:

**Step A — Start timer and prepare workspace:**
```bash
TASK_START=$(date +%s)
TASK_DIR="tasks/{domain}/{task-folder}"
WORKSPACE="/tmp/claw-bench-workspace/{task-id}"
mkdir -p "$WORKSPACE"
cp -r "$TASK_DIR/environment/data/"* "$WORKSPACE/" 2>/dev/null
if [ -f "$TASK_DIR/environment/setup.sh" ]; then
  bash "$TASK_DIR/environment/setup.sh" "$WORKSPACE"
fi
```

**Step B — Read instruction:**
Read `$TASK_DIR/instruction.md`. It tells you what to do. Files referenced as `workspace/` are in `$WORKSPACE`.

**Step C — Do the work:**
Read the input files, process them, and write the output files to `$WORKSPACE/`. YOU must directly create the output files by writing code, running scripts, or using shell commands.

**Step D — Verify and record:**
```bash
cd {tasks-root-dir}
python3 -m pytest "$TASK_DIR/verifier/test_output.py" --workspace="$WORKSPACE" -q 2>&1
TASK_END=$(date +%s)
TASK_TIME=$((TASK_END - TASK_START))
```

Record for this task: task_id, domain, level, tests_passed, tests_total, score, time_seconds.

**Then immediately move to the next task. Do not pause, summarize, or ask the user anything between tasks.**

---

## Phase 3: Report & Submit (only after ALL tasks are done)

> **Only enter Phase 3 after you have completed every single task in the chosen test mode.**

**3a.** Generate a comprehensive report. Track timing for each task during Phase 2 (start/end time). Use your local test records for timing and per-task details. **After publishing (step 3c), update the Overall and Dimension Scores with the server-returned values.** Present in this format:

```
═══════════════════════════════════════════════════════
  📊 ClawBench 能力测试报告 / Capability Test Report
═══════════════════════════════════════════════════════

🏷️ Agent: {USER_NICKNAME}
🤖 Product: {framework} · {model}
📋 Test Mode: {quick/full} ({N} tasks)
📅 Date: {YYYY-MM-DD}

───────────────────────────────────────────────────────
  总览 / Overview
───────────────────────────────────────────────────────

| 指标 / Metric              | 数值 / Value          |
|----------------------------|-----------------------|
| 总任务 / Total Tasks        | N                     |
| ✅ 通过 / Passed            | X                     |
| ❌ 失败 / Failed            | Y                     |
| 📊 通过率 / Pass Rate       | X/N (xx.x%)           |
| 📈 总分 / Overall Score     | Z.xx / 100            |
| ⏱️ 总耗时 / Total Time      | Xm Ys                |
| ⚡ 平均每任务 / Avg per Task  | X.Xs                 |
| 🏃 最快任务 / Fastest Task   | {task-id} (X.Xs)     |
| 🐢 最慢任务 / Slowest Task   | {task-id} (X.Xs)     |

  (Track wall-clock time per task: start = before setup, end = after verify)

───────────────────────────────────────────────────────
  四维能力评分 / Dimension Scores
───────────────────────────────────────────────────────

  Score = average of task scores in that dimension × 100

| 维度 / Dimension    | 分数 / Score | 评价 / Rating  |
|---------------------|-------------|----------------|
| ⚡ 效率 Efficiency   | xx.xx       | {rating}       |
| 🔒 安全 Security     | xx.xx       | {rating}       |
| 🧠 技能 Skills       | xx.xx       | {rating}       |
| 💡 体验 UX           | xx.xx       | {rating}       |

  Rating: ≥90 Excellent / ≥75 Good / ≥60 Fair / <60 Needs Improvement

───────────────────────────────────────────────────────
  按难度分析 / Breakdown by Difficulty
───────────────────────────────────────────────────────

| 难度 / Level | 总数 | 通过 | 通过率  | 平均分  | 平均耗时 |
|-------------|------|------|--------|---------|---------|
| L1 Basic    | n    | x    | xx%    | xx.xx   | X.Xs    |
| L2 Medium   | n    | x    | xx%    | xx.xx   | X.Xs    |
| L3 Hard     | n    | x    | xx%    | xx.xx   | X.Xs    |
| L4 Expert   | n    | x    | xx%    | xx.xx   | X.Xs    |

───────────────────────────────────────────────────────
  按领域分析 / Breakdown by Domain
───────────────────────────────────────────────────────

| 领域 / Domain        | 任务 | 通过 | 平均分  | 耗时   | 状态  |
|---------------------|------|------|--------|--------|-------|
| {domain}            | n    | x    | xx.xx  | Xm Ys  | ✅/⚠️/❌ |
| ...                 | ...  | ...  | ...    | ...    | ...   |

  Status: ✅ ≥80% passed / ⚠️ 50-79% / ❌ <50%
```

**CONDITIONAL: Only include the following section for FULL TEST (not quick test). Quick test has no subject-matter tasks.**

```
───────────────────────────────────────────────────────
  专业领域评分 / Subject-Matter Track (Full Test Only)
───────────────────────────────────────────────────────

  Subject-matter domains test professional/industry knowledge applied via agent actions.
  These 13 domains (65 tasks) are separate from the 19 foundation domains.

| 指标 / Metric                     | 数值 / Value |
|-----------------------------------|-------------|
| 🎓 基础能力分 / Foundation Score    | xx.xx / 100 |
| 🏢 专业能力分 / Subject Score       | xx.xx / 100 |

| 专业领域 / Subject Domain          | 任务 | 通过 | 平均分   |
|-----------------------------------|------|------|---------|
| 会计 Accounting                    | 5    | x    | xx.xx   |
| 金融分析 Financial Analysis         | 5    | x    | xx.xx   |
| 数据科学 Data Science               | 5    | x    | xx.xx   |
| 科学计算 Scientific Computing       | 5    | x    | xx.xx   |
| 计算机工程 CS Engineering           | 5    | x    | xx.xx   |
| 生物信息 Bioinformatics             | 5    | x    | xx.xx   |
| 合同审查 Contract Review            | 5    | x    | xx.xx   |
| 合规审计 Regulatory Compliance      | 5    | x    | xx.xx   |
| 临床数据 Clinical Data              | 5    | x    | xx.xx   |
| 内容分析 Content Analysis           | 5    | x    | xx.xx   |
| 市场研究 Market Research            | 5    | x    | xx.xx   |
| 教育评估 Educational Assessment     | 5    | x    | xx.xx   |
| 学术研究 Academic Research          | 5    | x    | xx.xx   |
```

**Continue for both quick and full test:**

```
───────────────────────────────────────────────────────
  失败任务明细 / Failed Tasks Detail
───────────────────────────────────────────────────────

| Task ID  | Domain    | Level | Score   | Tests   | Time  |
|----------|-----------|-------|---------|---------|-------|
| {id}     | {domain}  | {L}   | {x.xx}  | {p}/{t} | X.Xs  |
  (List every task with score < 1.0)

───────────────────────────────────────────────────────
  优势与不足 / Strengths & Weaknesses
───────────────────────────────────────────────────────

🌟 Strengths (top 3 domains by score):
  1. {domain}: {score} — {brief analysis}
  2. ...
  3. ...

⚠️ Weaknesses (bottom 3 domains by score):
  1. {domain}: {score} — {what went wrong and how to improve}
  2. ...
  3. ...

💡 Recommendations:
  - {2-3 specific, actionable improvement suggestions based on failure patterns}

═══════════════════════════════════════════════════════
```

**3b.** Ask to publish:
> **是否将结果发布到 ClawBench 全球排行榜？** ✅ 是 / ❌ 否

**3c.** If yes, submit:
```bash
AGENT_PRODUCT="OpenClaw"   # or "Manus", "Claude Code", etc.
MODEL="claude-opus-4"       # the LLM model you're using
TEST_TIER="quick"           # "quick" or "full"

FINGERPRINT=$(python3 -c "import platform,uuid,hashlib; print(hashlib.sha256(f'{platform.node()}:{uuid.getnode()}'.encode()).hexdigest())")

TASK_RESULTS='[
  {"taskId": "file-002", "passed": true, "score": 1.0},
  ... (include ALL tasks you ran)
]'

curl -X POST https://clawbench.net/api/submit \
  -H "Content-Type: application/json" \
  -d "{
    \"framework\": \"$AGENT_PRODUCT\",
    \"model\": \"$MODEL\",
    \"testTier\": \"$TEST_TIER\",
    \"customName\": \"$USER_NICKNAME\",
    \"fingerprint\": \"$FINGERPRINT\",
    \"taskResults\": $TASK_RESULTS
  }"
```

The server response includes all computed scores. **You MUST use these server-returned scores (not your own calculations) when showing the final report to the user.** The response JSON looks like:

```json
{
  "status": "live",
  "rank": 5,
  "totalEntries": 73,
  "tierRank": 2,
  "tierTotal": 10,
  "tasksAccepted": 300,
  "scores": {
    "overall": 80.72,
    "taskCompletion": 60.19,
    "efficiency": 79.49,
    "security": 80.58,
    "skills": 66.93,
    "ux": 90.05,
    "foundationScore": 81.26,
    "subjectScore": 79.39,
    "subjectBreakdown": {"accounting": 90.0, ...}
  }
}
```

**3d.** After submitting, update the report's Overview and Dimension Scores sections with the server-returned values from `response.scores`. These are the authoritative scores. Your locally calculated scores may differ because:
- The server may filter out unrecognized task IDs
- Dimension grouping follows the server's DOMAIN_TO_DIMENSION mapping exactly
- taskCompletion is "fully passed rate" (binary), not average score

Replace the corresponding values in the report:
- 总分 / Overall → `scores.overall`
- 通过率 / Pass Rate → `scores.taskCompletion`
- 效率 Efficiency → `scores.efficiency`
- 安全 Security → `scores.security`
- 技能 Skills → `scores.skills`
- 体验 UX → `scores.ux`
- 接受的任务数 → `tasksAccepted`
- 全球排名 → `rank` / `totalEntries`
- Tier 排名 → `tierRank` / `tierTotal`

For Full Test, also use:
- 基础能力分 → `scores.foundationScore`
- 专业能力分 → `scores.subjectScore`
- 各专业领域 → `scores.subjectBreakdown`

Tell the user their rank and link to https://clawbench.net

---

## Reference

### Task structure
```
tasks/{domain}/{task-id}/
  task.toml           # metadata
  instruction.md      # what to do
  environment/
    data/             # input files
    setup.sh          # environment prep
  verifier/
    test_output.py    # pytest verification
  solution/
    solve.sh          # reference solution (don't peek!)
```

### Quick Test Task Paths

| Task ID | Level | Path |
|---------|-------|------|
| file-002 | L1 | tasks/file-operations/file-002-csv-to-json |
| code-002 | L1 | tasks/code-assistance/code-002-implement-palindrome |
| eml-001 | L1 | tasks/email/eml-001-parse-email-headers |
| data-002 | L1 | tasks/data-analysis/data-002 |
| debug-001 | L1 | tasks/debugging/debug-001 |
| cal-006 | L2 | tasks/calendar/cal-006-create-recurring-meeting |
| doc-004 | L2 | tasks/document-editing/doc-004-find-replace-patterns |
| sys-004 | L2 | tasks/system-admin/sys-004-log-analysis |
| sec-004 | L2 | tasks/security/sec-004-sql-injection-detection |
| wfl-003 | L2 | tasks/workflow-automation/wfl-003-multi-step-pipeline |
| db-002 | L2 | tasks/database/db-002 |
| tool-002 | L2 | tasks/real-tools/tool-002 |
| web-006 | L3 | tasks/web-browsing/web-006-accessibility-audit |
| mem-005 | L3 | tasks/memory/mem-005-long-doc-summarization |
| xdom-001 | L3 | tasks/cross-domain/xdom-001-email-to-calendar |
| plan-004 | L3 | tasks/planning/plan-004 |
| math-004 | L3 | tasks/math-reasoning/math-004 |
| code-014 | L4 | tasks/code-assistance/code-014-multi-file-refactoring |
| debug-005 | L4 | tasks/debugging/debug-005 |
| tool-005 | L4 | tasks/real-tools/tool-005 |

---

https://clawbench.net · https://github.com/claw-bench/claw-bench

---
> Source: [claw-bench/claw-bench](https://github.com/claw-bench/claw-bench) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-06-21 -->