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Found 9,853 Skills
Analyzes current conversation context to recommend the best skills and subagents for the task at hand. Use proactively when unsure which tool, skill, or agent to use.
What's blocking close — maintain the closing checklist with status, critical path, and days to close. Self-updating: ingests new items from diligence findings and schedule builds, tracks status, surfaces what's blocking. Use when user says "closing checklist", "what's left to close", "checklist status", "add to the checklist", or on a scheduled status pull.
Case status summary by audience — client-facing (plain language), internal (for the professor), or court-ready (formal caption format per local rules). Same facts, different framing and depth. Use when a student needs to update the client, brief the professor, or prepare a court status report.
Persist learnings to memory or maintain existing memories. Triggers on "extract learnings", "save this for next time", "remember this pattern", "consolidate memories", "dream", "clean up memories".
This skill should be used when the user asks to "create an agent", "make an agent", "write an agent", "build a subagent", "add an agent to a plugin", "design an autonomous agent", "generate an agent file", "write a system prompt for an agent", "what frontmatter does an agent need", "create a specialized agent". Not for skills or commands — use create-skill.
This skill should be used when the user asks to "repair an agent", "audit an agent", "fix my agent", "review agent quality", "check if my agent is well-written", "diagnose agent problems", "what's wrong with this agent", "improve this agent", or "what's wrong with this agent file". Not for skills — use repair-skill.
Automated factory that converts GitHub repositories into standardized AI Skills. This tool is used when users provide a GitHub URL and want to "package", "wrap", or "create a Skill". It supports automatic retrieval of repository metadata, generation of standard directory structures, and injection of extended metadata required for lifecycle management.
Meta-skill for improving and optimizing prompts using Anthropic's prompt engineering best practices. Provides the 4-step improvement workflow (example identification, initial draft, chain of thought refinement, example enhancement), keyword registries for documentation lookup, and decision trees for improvement strategies. Use when improving prompts, optimizing for accuracy, adding chain of thought reasoning, structuring with XML tags, enhancing examples, or iterating on prompt quality. Delegates to docs-management skill for official prompt engineering documentation.
Cursor PMでアイデアを検証し、Plans.mdを更新してバトンタッチ。2-Agentワークフロー対応。
Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations.
Merge extract-rules output from multiple projects into a unified portable rule set. Promotes .local.md patterns shared across projects to Principles format.
Generate interactive AI transformation context-builder prompts for consulting clients. Use when creating structured discovery session prompts that guide a company through context gathering about their business, pain points, tech stack, and AI opportunities. Produces a resumable, multi-section prompt with Express/Deep Dive modes.