Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Grago
Delegate web and API data fetching to local LLMs for research tasks, saving tokens and keeping data private while using your local machine for analysis.
skill openclawclawhub Free
Delegate web and API data fetching to local LLMs for research tasks, saving tokens and keeping data private while using your local machine for analysis.
โฌ 0 downloads โ
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, SECURITY.md, SKILL.md, grago.sh, install.sh
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
- 1.0.1
Provenance
- Publisher
- solsuk
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Grago
Delegate research and data-fetch tasks to a free local LLM. Save tokens. Use your machine. Grago bridges the gap between your OpenClaw agent and local LLMs (Ollama, llama.cpp, etc.) that can't use tools natively. It runs shell scripts to fetch live data from the web, APIs, and local files โ then pipes the results into your local model with a focused prompt. Your cloud model stays sharp. Your local machine does the grunt work. Your token bill drops.
โ ๏ธ Security Model
Grago executes shell commands. This is intentional โ it's the only way to give tool-less local LLMs access to external data. Safe for: Trusted, single-user environments (your own Mac Mini, VPS, workstation) NOT safe for: Multi-tenant systems, public APIs, untrusted agents If your OpenClaw agent is compromised via prompt injection, Grago can execute arbitrary commands. This is the trade-off for free local compute. Read SECURITY.md in the repo for full details.
When to Use This Skill
Use Grago when: You need live data fetched (web pages, APIs, RSS feeds, logs) The task is research-heavy and doesn't need your primary model You want to keep data on your own machine (privacy) You want to save tokens by offloading analysis to a local LLM
How It Works
Fetch โ Shell scripts pull live data (curl, jq, grep, etc.) Analyze โ Results are piped to your local Ollama model with a prompt Return โ Structured analysis comes back to your OpenClaw agent
Usage
# Fetch a URL and analyze locally grago fetch "https://example.com" \ --analyze "Summarize the key points" \ --model gemma2 # Multi-source research from a YAML config grago research \ --sources sources.yaml \ --prompt "What are the main themes across these sources?" # Pipe any shell command into your local model grago pipe \ --fetch "curl -s https://api.example.com/data" \ --transform "jq .results" \ --analyze "Identify trends and flag outliers"
Configuration
Config file: ~/.grago/config.yaml default_model: gemma2 # Your preferred Ollama model timeout: 30 # Seconds per fetch max_input_chars: 16000 # Input truncation limit output_format: markdown # markdown | json | text
Requirements
Ollama installed and running locally (install.sh handles this) At least one model pulled in Ollama (gemma2, mistral, llama3, etc.) bash, curl, jq
Installation
git clone https://github.com/solsuk/grago.git cd grago && ./install.sh
Notes for the Agent
Prefer pipe mode over fetch --analyze for reliability (avoids Ollama TTY spinner issues) Default model is whatever is set in ~/.grago/config.yaml; override per-call with --model Input is truncated to max_input_chars before being sent to the local model Local model responses can be slow (5โ30s depending on hardware and model size) โ this is expected Grago is for research and fetch delegation โ not for tasks requiring your primary model's reasoning
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
3 Docs2 Scripts
- SKILL.md
- README.md
- SECURITY.md
- grago.sh
- install.sh