Recap of the Previous Post and Purpose of This Article
In the first installment of this series, we covered the architectural concepts behind OpenClaw Skills and explained why agent logic should be divided into reusable Skill units. In this article, we finally get into practice, walking through the process step by step — from setting up a local development environment to implementing a simple Skill and verifying that it works.
The importance of "reusable tool generation" in agent development is clearly illustrated by the case of NVIDIA winning first place on the DABStep benchmark. That team used the NeMo Agent Toolkit to build an agent that mimics the thinking of a data scientist, decomposing and reusing complex tasks by modularizing Skills [Source: https://huggingface.co/blog/nvidia/nemo-agent-toolkit-data-explorer-dabstep-1st-place]. OpenClaw Skills is based on the same design philosophy, and the environment you build in this article can be directly extended into a production-level project.
1. Prerequisites
Before getting started, confirm that the following are in place.
- Python 3.10 or higher
- pip or uv (recommended)
- Git
- Anthropic API key (if using Claude-series models)
- OpenAI API key (optional)
Using uv is recommended, as it significantly speeds up dependency resolution.
# Install uv curl -Lsf https://astral.sh/uv/install.sh | sh 2. Installing OpenClaw Skills
First, create a new virtual environment and install the package.
uv venv .venv source .venv/bin/activate uv pip install openclaw-skills If you are using pip, you can use the following command instead.
pip install openclaw-skills After installation, verify that the CLI is working correctly.
openclaw --version # openclaw-skills 0.4.2 3. Configuring API Keys
OpenClaw Skills supports multiple LLM backends. Managing keys via environment variables is recommended.
# Create a .env file cat > .env << 'EOF' ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxxxxxx OPENCLAW_DEFAULT_MODEL=claude-3-5-sonnet-20241022 OPENCLAW_LOG_LEVEL=INFO EOF Placing .env in the project root causes OpenClaw Skills to load it automatically. In production environments, use a secret management service (such as AWS Secrets Manager or GCP Secret Manager) and never hard-code API keys into source code or files.
4. Initializing the Project Structure
openclaw init my_first_skill cd my_first_skill The generated directory structure is as follows.
my_first_skill/ skills/ __init__.py tests/ __init__.py openclaw.yaml .env README.md openclaw.yaml is the project configuration file, where you define the default model, log level, Skill search paths, and more.
5. Implementing Your First Skill
Create a new file called skills/text_summarizer.py and write the following code.
from openclaw.skills import skill, SkillContext from typing import Annotated @skill( name="text_summarizer", description="Summarizes a given text in the specified language" ) def summarize_text( ctx: SkillContext, text: Annotated[str, "The text to be summarized"], language: Annotated[str, "Output language (e.g., ja, en)"] = "ja", max_sentences: Annotated[int, "Maximum number of sentences in the summary"] = 3, ) -> str: prompt = ( f"Please summarize the following text in {language} in no more than {max_sentences} sentences.\n\n" f"{text}" ) response = ctx.llm.complete(prompt) return response.text The @skill decorator registers this function with the OpenClaw Skills ecosystem. SkillContext is an object that centrally manages access to the LLM client, logger, and configuration, and plays the role of safely passing context between Skills.
6. Verifying Behavior Locally
openclaw run skills/text_summarizer.py \ --text "Large language models (LLMs) have revolutionized the field of natural language processing. ..." \ --language ja \ --max-sentences 2 Example output:
[INFO] Loaded skill: text_summarizer [INFO] Calling claude-3-5-sonnet-20241022 大規模言語モデルは自然言語処理に革命をもたらし、幅広い応用が進んでいる。 その影響は研究から産業界まで多岐にわたる。 Unit tests for Skills should be placed in the tests/ directory and run with pytest. OpenClaw Skills provides a mock context called MockSkillContext, which allows you to validate Skill logic without making actual LLM API calls.
7. Asynchronous Execution and Storage Integration
Skills that handle large volumes of data often require result persistence. It is worth keeping external storage integrations in mind — such as the Storage Buckets provided by Hugging Face Hub [Source: https://huggingface.co/blog/storage-buckets]. In OpenClaw Skills, you can connect to S3-compatible storage through the ctx.storage interface, enabling you to efficiently save and share the results of large-scale batch processing.
Preview of the Next Installment
In this article, we walked through the entire process from setting up the environment to implementing a simple Skill and verifying it. In the third installment, we will take a detailed look at how to build an agent pipeline by combining multiple Skills, as well as error handling and retry strategies.
Category: LLM | Tags: OpenClaw Skills, AIエージェント, LLM開発, Python, 環境構築
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