All Agent Skills
41,263 results
- Mcp Server Patternsaffaan-mBuild MCP servers with Node/TypeScript SDK â tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.
- MCP Server Patternsaffaan-mBuild MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.
- MCP Server Patternsaffaan-m使用Node/TypeScript SDK构建MCP服务器——工具、资源、提示、Zod验证、stdio与可流式HTTP对比。使用Context7或官方MCP文档获取最新API信息。
- Mcp Server Patternsaffaan-m使用Node/TypeScript SDK构建MCP服务器——工具、资源、提示、Zod验证、stdio与可流式HTTP对比。使用Context7或官方MCP文档获取最新API信息。
- Mcp Server Patternsaffaan-mBuild MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API. Use when building or debugging an MCP server — tools, resources, prompts, validation, or transport choice.
- Mcp Server Patternsaffaan-mBuild MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API. Use when building or debugging an MCP server — tools, resources, prompts, validation, or transport choice.
- Messages Opsaffaan-mEvidence-first live messaging workflow for ECC. Use when the user wants to read texts or DMs, recover a recent one-time code, inspect a thread before replying, or prove which message source was actually checked.
- Messages Opsaffaan-m面向ECC的以证据为先的实时消息工作流。当用户想要阅读短信或私信、恢复最近的一次性验证码、在回复前检查对话线程，或证明实际检查了哪个消息来源时使用。
- Ml Adoption Playbookaffaan-mEnd-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.
- Ml Adoption Playbookaffaan-mEnd-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.
- Mle Workflowaffaan-mProduction machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
- Mle Workflowaffaan-mProduction machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
- Mle Workflowaffaan-mProduction machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
- Mle Workflowaffaan-mProduction machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
- Module Regressionaffaan-mGovern cross-module regression in large repositories with a REGRESSION.md ledger that records each module's downstream consumers and executable acceptance command. After a change, run the changed module and every affected downstream command, and use observed exit codes as regression evidence under the project's delivery policy.
- Motion Advancedaffaan-mAdvanced motion patterns for React / Next.js — drag & drop, gestures, text animations, SVG path drawing, custom hooks, imperative sequences (useAnimate), loaders, and the full API decision tree. Requires motion-foundations. Use when building drag and drop, gestures, text or SVG animation, or imperative animation sequences in React or Next.js.
- Motion Foundationsaffaan-mMotion tokens, spring presets, performance rules, device adaptation, accessibility enforcement, and SSR safety for React / Next.js using motion/react. Foundation layer — all other motion skills depend on this. Use when setting up motion tokens, spring presets, reduced-motion handling, or SSR-safe animation in React or Next.js.
- Motion Patternsaffaan-mProduction-ready animation patterns for React / Next.js — button, modal, toast, stagger, page transitions, exit animations, scroll, and layout — built on motion-foundations tokens and springs. Use when animating a specific UI element in React or Next.js — button, modal, toast, stagger, page transition, or scroll.
- Motion UIaffaan-mProduction-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.
- Mybatis Patternsaffaan-mMyBatis and MyBatis-Spring patterns for mapper design, XML and annotation SQL, result mapping, dynamic SQL safety, transactions, batching, pagination, and query performance. Use when building or reviewing Java persistence code with MyBatis, Spring Boot, MyBatis-Spring, or MyBatis-based legacy applications.
- Mysql Patternsaffaan-mMySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends. Use when designing MySQL or MariaDB schemas and indexes, or when a query, transaction, or replica lags.
- Nanoclaw Replaffaan-mOperate and extend NanoClaw, ECC's zero-dependency session-aware REPL, with persistent markdown-backed sessions and slash commands for model switching, skill loading, session branching, cross-session search, history compaction, and export. Use when running or extending scripts/claw.js, or when resuming, branching, compacting, searching, or exporting a NanoClaw session.
- Nanoclaw Replaffaan-m操作并扩展NanoClaw v2，这是ECC基于claude -p构建的零依赖会话感知REPL。
- Nasiko Control Planeaffaan-mManage the experimental Nasiko CLI lifecycle through ECC — read-only status checks, consent-gated install of the pinned qualified version with dry-run preview, and ownership-checked uninstall, under explicit telemetry and secrets boundaries. Use when the user asks to install, inspect, or remove the Nasiko CLI or check whether it is present.