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Found 2,397 Skills
Review existing Perses dashboards for quality: fetch via MCP or API, analyze panel layout, query efficiency, variable usage, datasource configuration. Generate improvement report. Optional --fix mode. 4-phase pipeline: FETCH, ANALYZE, REPORT, FIX. Use for "review perses dashboard", "audit dashboard", "perses dashboard quality". Do NOT use for creating new dashboards (use perses-dashboard-create).
Convert HWP files to JSON, Markdown, or HTML, extract images, and choose between @ohah/hwpjs and hwp-mcp based on OS and local Hangul availability.
Ping-pong TDD 세션의 첫 번째 단계. TrackerBoot MCP에서 스토리를 가져오거나 직접 붙여넣은 스토리 내용을 받아들이고, 프로젝트 스택과 컨벤션을 감지하며, 협력하여 태스크 분해를 계획하고, /tdd-task와 /tdd-commit이 사용할 .tdd-session.md 파일을 작성합니다.
Audit your Claude Code setup for token waste and context bloat. Use when the user says "audit my context", "check my settings", "why is Claude so slow", "token optimization", "context audit", or runs /context-audit. Starts by running /context to see real overhead, then audits MCP servers, CLAUDE.md rules, skills, settings, and file permissions. Returns a health score with specific fixes.
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
This skill should be used when the user asks to maintain an Obsidian knowledge base for a research project, import an existing research repository into Obsidian, keep project memory or daily notes synchronized, summarize project context into durable notes, or update experiments, results, papers, writing, and plans in an Obsidian vault without requiring MCP.
Operate Notion Public API through UXC with a curated OpenAPI schema for search, block traversal, page reads, content writes, and data source/database inspection. Use when tasks need recursive reads or structured writes that Notion MCP does not expose directly.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
Polish a generated CLI to pass verification and become publish-ready. Runs diagnostics (dogfood, verify, scorecard, go vet), automatically fixes all issues (verify failures, dead code, descriptions, README, MCP tool quality), reports the before/after delta, and offers to publish. Use after any /printing-press run, or on any CLI in ~/printing-press/library/. Trigger phrases: "polish", "improve the CLI", "fix verify", "make it publish-ready", "clean up the CLI", "get this ready to ship".
Buffett-style single-stock moat diagnostic — "Would Buffett buy this stock?" Five dimensions: business & moat / financial health / management & capital allocation / valuation & margin of safety / long-term visibility. Data from Longbridge CLI first, MCP fallback, WebSearch only for gaps. Runs cross-statement reconciliation (勾稽校验) BEFORE scoring; data-source appendix closes with a one-line reconciliation summary. Output: star-rated radar card, dimension detail, Buffett-voice narrative, mandatory holding-period education block. Triggers: "巴菲特", "护城河", "巴菲特会买吗", "价值投资", "好生意", "宽护城河", "定价权", "诊股", "巴菲特诊股", "巴菲特视角", "长期持有", "護城河", "巴菲特會買嗎", "價值投資", "寬護城河", "定價權", "診股", "巴菲特診股", "巴菲特視角", "長期持有", "Buffett", "Warren Buffett", "moat", "economic moat", "wide moat", "pricing power", "value investing", "owner earnings", "would Buffett buy", "Berkshire-style", "quality compounder".
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.
Generate 5–6 App Store screenshots in a given brand's aesthetic from a `brand.md`, raw product screenshots, or a public App Store listing fetched through Pika MCP. Story-driven (hook → value → features → proof → close), splashy, on-brand. Outputs 1290×2796 PNGs ready to drop into App Store Connect. Use when someone wants App Store / store listing assets — including: "make me app store screenshots", "design app store screens for [brand]", "I have a brand.md and screenshots, generate store assets", "screenshot set for app launch", "iOS store screens", "app store creative", "store listing visuals", "splashy app store screens", "app-store-screens".