Agent Skills Hub

freeride

Manages free AI models from OpenRouter for OpenClaw. Ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json automatically.

subagent-driven-development

Execute implementation plans using isolated subagents for each task, featuring a rigorous two-stage review process for spec compliance and code quality.

agentic-workflows

Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.

nelson

Orchestrates complex multi-agent software development using a structured Royal Navy squadron metaphor, featuring mission planning, parallel task coordination, and rigorous audit logs.

langgraph

Expert LangGraph architect skill for designing stateful, multi-actor AI agent workflows with robust persistence, conditional branching, and ReAct patterns.

stable-baselines3

Production-ready reinforcement learning using Stable Baselines3. Train agents, design custom environments, implement training callbacks, and optimize workflows with a scikit-learn-style API.

sparc-methodology

SPARC methodology for multi-agent development: systematic Specification, Pseudocode, Architecture, Refinement, and Completion workflows via Claude Flow orchestration.

glyphbox

A framework for an LLM-based NetHack agent that dynamically synthesizes Python code in a secure sandbox to perform complex dungeon exploration and gameplay actions via a high-level API.

ai-collaboration-standards

Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.

dev-cycle

Interactive development workflow manager. Coordinates discovery, planning, review, and build phases using a specialized team of AI agents (Scout, Bob, Garry, Arlo) for consistent project delivery.

prompt-engineering-patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production applications.

context-driven-testing

Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.