OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LeoYeAI/openclaw-master-skills --skill ai-model-routergit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leoyeai/openclaw-master-skills/ai-model-router)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/ai-model-router"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/ai-model-router/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/ai-model-router"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/ai-model-router.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00794 |
| Opus 5 | $0.00000 | $0.00397 |
| Sonnet 5 | $0.00000 | $0.00159 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
ai-model-router scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-model-router — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: ai-model-router description: Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones). Detects local models (Ollama, LM Studio) automatically, routes based on task complexity and privacy. Use when users ask to "switch model", "use local/cloud model", or mention API keys/passwords (triggers privacy mode). Trigger on: sensitive data detection (forces local), complex tasks like "design architecture" (uses cloud), or configuration requests. version: 1.0.0
AI Model Router
Compact, intelligent model routing that just works.
Quick Start
# Install
npx clawhub@latest install ai-model-router
# First run - auto-detects your models
python3 skill/core/router.py "What is Python?"
# List available models
python3 skill/core/router.py --list
How It Works
Your Request → Analyze → Select Model
↓
Simple? → Primary (fast/cheap)
Complex? → Secondary (capable)
Private? → Primary (forced)
Scoring (from model-router-premium)
| Pattern | Points |
|---|---|
| Microservices, architecture | +10 |
| Design, implement, optimize | +5 |
| Explain, analyze, compare | +3 |
| Syntax, example, "what is" | -3 |
Threshold: 5 (simple vs complex)
Features
| Feature | Status |
|---|---|
| Auto-detect local models | ✓ (Ollama, LM Studio) |
| Cloud model registry | ✓ (7 built-in) |
| Privacy detection | ✓ (API keys, passwords) |
| Context tracking | ✓ (conversations) |
| JSON config | ✓ (optional) |
| CLI interface | ✓ |
| Core code size | ~200 lines |
CLI
# Route a task
python3 skill/core/router.py "Design a system"
python3 skill/core/router.py "What is a for loop?"
# Options
--json # JSON output
--force primary # Force primary model
--list # List all models
--status # Show status
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 120 lines · 0 tokens per session scan A 1c03995ca5ae
ai-model-router is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,141 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 794 tokens. A static security scan graded it A with 0 findings. It is 100% identical to ai-model-router, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
add-ollama-tool
Add Ollama MCP server so the container agent can call local models and optionally manage the Ollama model library.
graphify
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
nlp-alignment
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
distributed-training
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
data-loading
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.
map
Build and commit a Cortex function knowledge graph — maps structural dependencies and domain intent relationships across all AI functions in the project. Supports --reduce (default on) for transitive reduction of the dependency graph.