Openclaw-skills-Compilations

Use when managing the Claude CLI Proxy (ban protector) — start, stop, restart, status, logs, backup, test, setup, or register the proxy in OpenClaw. Triggers on: 'proxy status', 'ban protector', 'start proxy', 'stop proxy', 'proxy health', 'antiban', 'cli proxy'.

Run stateful multi-agent graph workflows using LangGraph (linear, supervisor, parallel, conditional). Use when tasks need dynamic routing, branching logic, or a supervisor agent delegating work. All LLM calls use OpenClaw's existing provider config — no API keys needed. Choose over autogen-collab (debate) and crewai-collab (fixed pipelines) when you need conditional edges, a supervisor making routing decisions, or parallel fan-out with synthesis.

Run multi-agent AutoGen-style debates when tasks need consensus across specialists. Uses OpenClaw's existing provider configuration — no API keys or extra setup needed. Use when: architecture decisions, design reviews, root cause analysis, or complex research where multiple expert perspectives improve the answer. Triggered automatically by Cooper for high-complexity tasks or explicitly via [autogen] tag or "debate this" / "get consensus" instructions.

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Run structured multi-agent workflows using CrewAI (sequential, hierarchical, consensus). Each agent has a defined role, goal, and backstory. Tasks have explicit expected outputs. Use when: structured pipelines (research → design → implement), manager-supervised workflows, or consensus-building with clear acceptance criteria. All LLM calls use OpenClaw's existing provider config — no API keys needed.

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

This skill should be used when the user asks to 'demonstrate skills', 'show skill format', 'create a skill template', or discusses skill development patterns. Provides a reference template for creating OpenClaw skills.

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.

Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.

This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.

This skill should be used when the user asks to 'add MCP server', 'integrate MCP', 'configure MCP', 'use .mcp.json', 'set up Model Context Protocol', 'connect external service via MCP', or discusses MCP server types (SSE, stdio, HTTP, WebSocket). Provides comprehensive guidance for integrating Model Context Protocol servers into OpenClaw for external tool and service integration.

This skill should be used when the user asks to 'create an agent', 'add an agent', 'write a new agent', 'configure agent SOUL.md', 'agent IDENTITY.md', 'agent workspace setup', 'agent fallback chains', 'agent model assignment', or needs guidance on OpenClaw agent structure, SOUL.md/IDENTITY.md patterns, agent configuration in openclaw.json, or agent development best practices.

Analyze a codebase or workspace and recommend OpenClaw automations (cron jobs, skills, agent routing, processes, MCP servers, workspace structure). Use when user asks for automation recommendations, wants to optimize their OpenClaw setup, mentions improving OpenClaw workflows, asks how to best configure OpenClaw for a project, or wants to know what OpenClaw features they should use.

This skill should be used when the user asks to 'create a slash command', 'add a command', 'write a custom command', 'define command arguments', 'skill slash-commands', 'organize commands', or needs guidance on OpenClaw skill-based slash commands, YAML frontmatter, dynamic arguments, or command development best practices.

This skill should be used when the user asks to 'create a cron job', 'add a scheduled task', 'write a process definition', 'configure automation', 'set up event-driven automation', 'define a process in PROCESSES.json', or needs guidance on OpenClaw cron jobs (`openclaw cron`), process definitions, scheduled automation, or workflow automation best practices.

Audit and improve CLAUDE.md and SOUL.md files across the OpenClaw workspace, plus openclaw.json config. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files, SOUL.md agent personality files, or openclaw.json configuration. Also use when the user mentions 'config maintenance', 'project memory optimization', 'agent personality audit', or 'workspace health check'.

This skill should be used when the user asks about 'OpenClaw settings', 'configure openclaw.json', 'skill configuration', 'per-project settings', 'agent configuration', 'model routing', 'provider settings', 'skills.entries config', or wants to make OpenClaw behavior configurable through openclaw.json.

This skill should be used when the user wants to 'create a skill', 'add a skill', 'write a new skill', 'improve skill description', 'organize skill content', or needs guidance on OpenClaw SKILL.md format, progressive disclosure, metadata.openclaw block, or skill development best practices.

This skill should be used when the user asks to 'understand workspace structure', 'organize workspace', 'set up OpenClaw workspace', 'where do files go', 'workspace layout', 'agent workspace paths', or needs guidance on OpenClaw directory layout, file organization, component locations, or workspace architecture best practices.

Creates interactive HTML playgrounds — self-contained single-file explorers that let users configure something visually through controls, see a live preview, and copy out a prompt. Use when the user asks to make a playground, explorer, or interactive tool for a topic.

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Best practices for building Stripe integrations. Use when implementing payment processing, checkout flows, subscriptions, webhooks, Connect platforms, or any Stripe API integration.

Use when executing implementation plans with independent tasks in the current session

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when implementing any feature or bugfix, before writing implementation code

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

This skill should be used when the user asks to 'create an automation rule', 'write a process rule', 'configure automation', 'add an automation rule', or needs guidance on OpenClaw process/cron automation patterns, rule syntax, and automated enforcement.