skillcraft

SkillCraft - LLM Agent Tool Composition Benchmark

Description

Evaluate and analyze LLM agents' ability to form, abstract, and reuse higher-level tool compositions (Skills). Use this skill when researching agent skill discovery, tool composition patterns, or evaluating skill caching efficiency.

Trigger words: SkillCraft, skill discovery, tool composition, agent skills, skill caching, LLM benchmark, 技能发现, 工具组合

Core Concepts

Problem Statement

• Traditional benchmarks test "can the agent call the right tool?"
• SkillCraft tests "can the agent abstract and reuse tool combinations?"
• This is the difference between tool usage and tool mastery

Dual Difficulty Dimensions

1. Quantitative Scaling - Increase number of entities/items to process
2. Structural Scaling - Compose subtasks into longer, more complex tool chains

Key Findings

• Up to 80% token reduction through skill caching
• Success rate correlates with tool composition ability
• Real-world stress test - recurring patterns in long-horizon workflows

Architecture Paradigm Shift

Traditional: Tool → LLM → Result

SkillCraft:  Tool → LLM → Skill Abstract → Skill Cache → Reuse

Practical Applications

Code Agent Skills

Pattern: read → analyze → edit → test → commit
Skill: One-click workflow execution

Data Analysis Skills

Pattern: load → clean → analyze → visualize → report
Skill: Data type-specific analysis templates

Research Skills

Pattern: search → filter → summarize → compare
Skill: Literature review automation

Reproduction

Prerequisites

• Linux (recommended)
• Python 3.10+
• uv package manager
• Node.js 22+ with npx
• OpenRouter/Toolathlon API endpoint

Install & Run

# Clone
git clone https://github.com/shiqichen17/SkillCraft
cd SkillCraft

# Install
uv sync

# Configure .env
TOOLATHLON_OPENAI_API_KEY=YOUR_KEY
TOOLATHLON_OPENAI_BASE_URL=https://openrouter.ai/api/v1
TOOLATHLON_MODEL=deepseek-v3.2-exp
TOOLATHLON_PROVIDER=openrouter

# Run complete evaluation
uv run python test_all_tasks.py \
  --scaled-tasks \
  --mode base,skill \
  --model deepseek-v3.2-exp \
  --provider openrouter

Single Task Test

# Base mode
bash run.sh scaled_tasks/cat-facts-collector/e1 base --model deepseek-v3.2-exp --provider openrouter

# Skill mode
bash run.sh scaled_tasks/cat-facts-collector/e1 skill --model deepseek-v3.2-exp --provider openrouter

Benchmark Tasks

Scaled tasks include:

• gitlab-deep-analysis
• countries-encyclopedia
• tvmaze-series-analyzer
• pokeapi-pokedex
• cat-facts-collector
• And more...

Output Structure

Each run produces:

test_runs/run_YYYYMMDD_HHMMSS/
├── run_info.json
├── test_results_<provider>_<model>.json
├── summary_<provider>_<model>.json
├── dumps_base_test/
└── dumps_skill_test/

Cognitive Level Mapping

Level	Type	SkillCraft Test
1	Knowledge	❌ Not tested
2	Understanding	❌ Not tested
3	Application	⚠️ Prerequisite
4	Analysis	⚠️ Prerequisite
5	Synthesis	✅ Core test
6	Evaluation	⚠️ Implicit

Core test: Can the agent synthesize new skills from tool combinations?

Implications for Agent Development

Current Agent Skills Limitations

• Depend on human pre-definition (SKILL.md)
• Cannot auto-discover skill patterns
• Lack cross-task reuse mechanism

Future Evolution

skill:
  type: human-defined      # Current: human-written
  type: auto-discovered    # Future: pattern mining from trajectories
  
  source:
    - pattern_mining       # Discover from success trajectories
    - composition          # Abstract tool combinations
    - optimization         # Auto-optimize existing skills
    
  metrics:
    - token_savings        # Efficiency gain
    - success_rate         # Task completion
    - transferability      # Cross-domain applicability

Actionable Insights for pi Skills

1. Trajectory Analysis - Mine pi logs for high-frequency tool combinations
2. Skill Extraction - Auto-generate SKILL.md candidates
3. Effect Validation - Use SkillCraft methodology for quality assessment

Resources

• Paper: SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?
• Code: github.com/shiqichen17/SkillCraft
• Project: skillcraft-website.github.io/page

Citation

@misc{chen2026skillcraftllmagentslearn,
      title={SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?},
      author={Shiqi Chen and Jingze Gai and Ruochen Zhou and Jinghan Zhang and Tongyao Zhu and Junlong Li and Kangrui Wang and Zihan Wang and Zhengyu Chen and Klara Kaleb and Ning Miao and Siyang Gao and Cong Lu and Manling Li and Junxian He and Yee Whye Teh},
      year={2026},
      eprint={2603.00718},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.00718},
}

Key Insight

SkillCraft tests whether agents can evolve from "tool users" to "skill creators" - a qualitative leap from execution to learning.

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Last updated: 2026-03-21