Total 55,731 skills, AI & Machine Learning has 9268 skills
Showing 12 of 9268 skills
Modo de explicação em camadas. Aplica SOMENTE na resposta imediatamente após a invocação — depois volta ao normal automaticamente. A resposta deve ser em manchete (1 frase, no máximo 2), no nível exato da granularidade da pergunta. Não listar itens individuais quando a pergunta foi sobre o conjunto. Não oferecer drill-down nem perguntar se quer detalhar — esperar o usuário pedir. Use SOMENTE quando o usuário invocar explicitamente com "/peel-talk", "peel-talk", "explica no peel-talk", "peel talk", "modo peel", ou variações. NÃO invocar automaticamente em outras tarefas.
Lettria integration. Manage data, records, and automate workflows. Use when the user wants to interact with Lettria data.
Chinese OpenClaw/AI agent use case reference with 50+ real-world scenarios for automation, content creation, DevOps, and productivity
使用 agency-agents-zh 中文 AI 智能体角色库，为 AI 编程工具提供 215 个即插即用的专家角色
Real-time crypto news aggregation with AI ratings and trading signals from 84+ sources across news, listings, on-chain, meme, market, and prediction engines
Self-evolving autonomous agent framework with skill tree growth, browser/desktop/mobile control, and hierarchical memory system
Terminal AI coding assistant optimized for DeepSeek v4 with deep thinking, reasoning control, Agent Skills, and MCP integration
Control Ableton Live with AI agents via MCP - create MIDI clips, insert audio, add tracks/devices, analyze signals, automate mixing
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Triggers whenever a user uploads a syllabus, course outline, or curriculum document and wants supplementary readings. Also triggers on: 'syllabus reading list', 'find papers for my course', 'create a reading list from this syllabus', 'recent research for my class', 'supplementary readings', 'find journal articles for these topics', 'what recent papers cover this material', 'any new research on these course topics', 'update my syllabus with recent papers'. Even casual mentions when a syllabus is attached should trigger this skill.
agent-team: Cancel a non-terminal task with a reason.
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.
Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally