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Found 5,885 Skills
Automatically announces plans, issues, and summaries out loud using TTS. Use this skill PROACTIVELY after completing major tasks like finalizing a plan, resolving an issue, or generating a summary. Each project gets a unique voice so users can identify which project is speaking from another room. Providers fallback in order (google, openai, elevenlabs, say) on rate limits.
Local SEO Analysis and Optimization Expert. Automatically detect whether a project requires local SEO, analyze NAP (Name, Address, Phone) consistency, local keyword optimization, Google Business Profile (GBP) optimization, and local structured data generation. Provide search engine ranking optimization suggestions for local businesses, including NAP standardization, local keyword strategies, GBP completeness checks, review strategies, map embedding, and local SEO audits.
Select the most appropriate pipeline for a user goal, lock it in `PIPELINE.lock.md`, and route checkpoint questions into `DECISIONS.md`. **Trigger**: pipeline router, choose pipeline, workflow selection, PIPELINE.lock.md, 选择流程. **Use when**: 用户目标/交付物不清晰，需要在 snapshot/survey/tutorial/systematic-review/peer-review 中选一个并设置最小 HITL 问题集。 **Skip if**: pipeline 已锁定（`PIPELINE.lock.md` 存在）且所需问题已回答/签字完成。 **Network**: none. **Guardrail**: 尽量一次性提问；信息不足就写 `DECISIONS.md` 并停下等待。
Score, grade, or evaluate things using AI against a rubric. Use when grading essays, scoring code reviews, rating candidate responses, auditing support quality, evaluating compliance, building a quality rubric, running QA checks against criteria, assessing performance, rating content quality, or any task where you need numeric scores with justifications — not just categories.
Building interactive experiences that engage, challenge, and delight playersUse when "game, gamedev, game development, phaser, unity, unreal, godot, gameplay, game loop, sprites, collision, physics, player, level, tilemap, games, gamedev, interactive, gameplay, physics, engines, performance, player-experience" mentioned.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Research-aligned self-consistency for debugging. Spawns independent solver agents that each explore and debug the problem from scratch. Uses majority voting. Based on "Self-Consistency Improves Chain of Thought Reasoning" (Wang et al., 2022). Use for critical bugs, algorithms, or when other approaches have failed.
Use when conducting user research (interviews, usability tests, surveys, A/B tests) or designing research studies. Covers discovery, validation, evaluative methods, research ops, governance, and measurement for software experiences.
YouTube API access without the official API quota hassle — transcripts, search, channels, playlists, and metadata with no Google API key needed. Use when the user needs YouTube data programmatically, wants to avoid Google API quotas, or asks for "youtube api", "get video data", "youtube without api key", "no quota youtube".
Multi-instance (Multi-Agent) orchestration workflow for deep research: Split a research goal into parallel sub-goals, run child processes in the default `workspace-write` sandbox using Codex CLI (`codex exec`); prioritize installed skills for networking and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + key conclusions/recommendations summary". Applicable to: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-Agent parallel research/multi-process research".