Data analysis, machine learning, LLM apps, and prompt design (9.1K skills)
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.
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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
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Use when adding interactive 3D scenes from Spline.design to web projects, including React embedding and runtime control API.
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Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact.
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Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
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Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.
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Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
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Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, or working with webhook data in workflows.
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You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur
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Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building distributed systems.
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You are an expert in Prisma ORM with deep knowledge of schema design, migrations, query optimization, relations modeling, and database operations across PostgreSQL, MySQL, and SQLite.
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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
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Production-ready patterns for building LLM applications, inspired by [Dify](https://github.com/langgenius/dify) and industry best practices.
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Use when a task should repeat on a schedule or until a condition holds (CI until green, follow a PR to merge, review rounds until no blockers, daily session summary). Ships ready prompt templates with run and duration limits, stop conditions and go-gate safety rules, and per-harness loop invocation.
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Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
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Interactive 9-step prompt crafting and delegation protocol for autonomous multi-agent teams. Enforces objective verification, integrity modes, and acceptance criteria across Antigravity, Claude Code, Cursor, OpenCode, Codex, and Roo Code.
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Linear multi-agent build pipeline with file hand-offs in production_artifacts/, run after /bdbrainstorm or /grill-me. This is the linear variant — no state.json, no repair loop, no dispatcher graph — distinct from startcycle-graph, which adds durable state and automated escalation. Use when an approved plan should be built, when the user types /startcycle or "run a startcycle", or when you pick a startcycle variant from the situation yourself (no user prompt needed).
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阿里云 AnalyticDB (ADB) for MySQL 只读数据分析。 多维分析（计数/聚合/时间序列）、交叉验证、Schema 文档生成、多 Profile。
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LLM Agent engineering: pattern selection (12-mode matrix), data simulation, convergence iteration. Covers intent routing, function calling, ReAct, MCP, prompt chaining, guardrails, evaluation.
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Elasticsearch / SLS 只读数据分析：索引探索、mapping、聚合统计、日志搜索、时间序列、多 Profile。
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ScalarDBの制約を考慮したデータモデル設計を行います。Partition Key・Clustering Key・Secondary Index の設計、ホットスポット評価、メタデータオーバーヘッド見積もり、バックエンドDB選定を含みます。ワークフローStep 04（データモデル設計）で使用します。
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backlog（atoms/pipeline/outputs）と skill ヘルス（usage/duration/unused）を統合分析し 1 レポートで提示する。kawai 氏型 analytics ループの起点。「次に何やる？」「atom 整理」「放置案件」「stale」「振り返り」「retro」「skill 使用統計」「unused skill」「atom-suggest」で発動。
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