Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Added security documentation explaining intentional command execution design
What This Skill Does
Grago bridges OpenClaw agents and local LLMs like Ollama or llama.cpp that can't use tools natively. It runs shell scripts to pull live data from the web, APIs, or local files, then pipes the results into a local model with a focused prompt. The cloud model handles reasoning; the local machine handles the fetch-and-analyze work.
It offloads token-heavy fetch-and-analyze work to a local LLM, cutting API costs while still giving the agent access to live external data.
When to use it
- Fetching and summarizing web pages without spending cloud API tokens
- Analyzing API responses locally to keep data off third-party servers
- Monitoring RSS feeds and extracting key updates with a local model
- Running multi-source research jobs on local hardware overnight
- Piping log files through a local LLM to detect patterns or anomalies
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Fetch this API endpoint and identify any trends in the results
- 1Grago pipes the shell command `curl -s https://api.example.com/data` to fetch live data
- 2Transforms the response with `jq .results` to extract relevant fields
- 3Sends the transformed output to the local Ollama model with the prompt 'Identify trends and flag outliers'
- 4Returns the model's structured analysis back to the OpenClaw agent
A markdown summary of trends and flagged outliers from the API data, generated locally without cloud tokens
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.