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Found 2,034 Skills
Configure and customize skills for your workspace by asking questions and updating skill files with your preferences.
Invoke parallel document-specialist agents for external web searches and documentation lookup
Spawn 10 independent parallel agents to analyze source material from distinct perspectives, synthesize findings, and apply improvements to a target agent or skill. Use when source material is complex and multi-angle extraction justifies 3-5x token cost over inline analysis. Use for "parallel analysis", "multi-perspective", or "deep extraction". Do NOT use for routine improvements, simple source material, or when token budget is limited.
Skill Tester
Socratic drilling — it asks, you answer, it pushes back. Does NOT give you the answer until you've earned it. Use when the user says "drill me on", "quiz me", "socratic", "test me on [subject]", or wants to study actively.
Build or review an element chart — a patent claim chart (infringement, invalidity, or review) or a civil element chart for any cause of action or defense — with every cell pin-cited and gap detection as the priority output. Use when the user asks for a claim chart, element chart, proof chart, infringement or invalidity contention, element-by-element mapping, or asks "what are we missing to prove [claim]".
This skill should be used when the user asks to "improve a skill", "make this skill better", "add features to a skill", "this skill is missing something", "upgrade my skill", "what's missing from this skill", "the skill doesn't do X", "make this more useful", or wants to improve skill effectiveness rather than structural correctness. Not for structural fixes — use repair-skill. Not for agents.
Use when setting up a GitHub project board, labels, and Actions workflows for a new or existing repo.
Comprehensive guide for skill development based on Anthropic's official best practices - use for complex skills requiring detailed structure
Initialize a new Ruflo project with MCP tools, hooks, and agent configuration
Route tasks to optimal agents using learned patterns, model recommendations, and confidence scoring
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.