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Found 1,266 Skills
Orchestrate the full skill development lifecycle from idea to publication. Guides through ideation, requirements definition, skill implementation, and marketplace registration. Use when: "create a new skill end-to-end", "skill development workflow", "build and publish a skill", "スキル開発ワークフロー", "スキルを作って公開", "新しいスキルを開発".
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we discuss", "remember when", "find previous conversation", "check history", or before starting work to recall prior decisions.
Comprehensive knowledge of Claude Agent SDK architecture, tools, hooks, skills, and production patterns. Auto-activates for agent building, SDK integration, tool design, and MCP server tasks.
Create technical design document with architecture decisions and approach. Trigger: When the orchestrator launches you to write or update the technical design for a change.
Autonomous workflow execution pipeline with CSV wave engine. Session discovery → plan validation → IMPL-*.json → CSV conversion → wave execution via spawn_agents_on_csv → results sync. Task JSONs remain the rich data source; CSV is brief + execution state.
Frontend debugging team using Chrome DevTools MCP. Dual-mode -- feature-list testing or bug-report debugging. Covers reproduction, root cause analysis, code fixes, and verification. CSV wave pipeline with conditional skip and iteration loops.
Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
Implement approved OpenSpec proposal using DAG-scheduled multi-agent parallel execution
Forge implementation from JIRA tickets using dynamic skill generation. Gathers context into a lazy-loaded skill, then invokes it for execution. Use when given a JIRA ticket URL (format: https://[domain].atlassian.net/browse/[TICKET-ID]) to process end-to-end.
Skill for using Fabro, the open source AI coding workflow orchestrator that lets you define agent pipelines as Graphviz DOT graphs with human gates, multi-model routing, and cloud sandboxes.
2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection
Resolve all pending CLI todos using parallel processing, compound on lessons learned, then clean up completed todos.