Skills
开放的 AI 智能体 Skills 注册表,收录结构化提示词和工作流,并提供推荐模型、示例提示词与兼容工具。
部分描述为试点机器翻译内容,尚未经过人工审核。
Skills
100
类别
9
兼容工具
7
贡献者
2
Converts a loosely defined opportunity into testable product requirements, user journeys, scope boundaries, assumptions, risks, success metrics, and discovery questions.
Designs reliable JSON and typed outputs for LLM features, including schemas, validation, recovery paths, versioning, examples, and contracts for downstream consumers.
Designs trustworthy product analytics events, properties, identity rules, validation, privacy boundaries, dashboards, and governance for product and growth decisions.
Produces clear, reviewable architecture decision records that capture context, alternatives, trade-offs, consequences, rollout steps, and reversal criteria.
Produces blameless, actionable incident postmortems with a precise timeline, contributing factors, impact, decisions, corrective actions, owners, and measurable follow-through.
按照实用的无障碍要求审查产品界面,覆盖语义结构、键盘操作、焦点行为、颜色对比度、屏幕阅读器和表单。
Defines versioned data contracts between producers and consumers, with ownership, schemas, quality expectations, compatibility rules, and operational change management.
将陌生的代码仓库整理成简明的工程指南,涵盖架构、约定、本地设置、关键执行路径和低风险的首次贡献。
Turns merged work, pull requests, issues, and user-visible behavior changes into accurate, audience-specific release notes with breaking-change and upgrade guidance.
Synthesizes interviews, support tickets, reviews, and survey responses into evidence-based themes, opportunities, representative quotes, confidence levels, and product actions.
Builds regression suites for prompts and agent instructions so teams can detect quality, safety, format, and tool-selection regressions before deploying changes.
Designs reliable AI-assisted business and engineering automations with triggers, approvals, idempotency, observability, exception handling, and human handoff points.
Designs and implements rate limiting for APIs and services. Recommends an algorithm (token bucket, leaky bucket, fixed or sliding window) for the use case, defines per-key and per-endpoint limits, plans distributed enforcement with Redis, and specifies response headers and 429 handling with retry-after. Produces a design plus reference implementation.
Reviews claims in a document for accuracy and verifiability. Extracts discrete factual statements, rates each as supported, unsupported, or needs-verification, flags logical inconsistencies and unsourced numbers, and suggests what evidence would confirm or refute each claim. Designed to reduce hallucinated or outdated facts before publishing.
Sets up and maintains Git hooks for a repository. Recommends a manager (Husky, Lefthook, or pre-commit), wires up pre-commit and commit-msg hooks for linting, formatting, type checks, secret scanning, and conventional-commit validation, and keeps hooks fast with staged-file filtering. Produces config plus a short contributor guide.
Produces production-ready nginx configuration for common scenarios: reverse proxy, load balancing, TLS termination, static file serving, HTTP/2, gzip/brotli, caching, rate limiting, and security headers. Explains each directive, warns about risky defaults, and includes a validation step so the config can be tested before reload.
Decodes and reviews JSON Web Tokens and their surrounding auth flow for correctness and security. Explains header and claims, checks algorithm and key handling, validates expiration and audience/issuer claims, and flags common pitfalls such as the alg:none attack, weak secrets, missing validation, and over-long token lifetimes. Never treats token contents as trusted secrets to echo back.
Translates requirements and user stories into behavior-driven development scenarios in Gherkin. Writes clear Given/When/Then steps, covers happy paths, edge cases, and negative cases, uses scenario outlines with examples for data-driven tests, and keeps steps declarative and reusable. Optionally scaffolds step definitions for Cucumber or Behave.
Designs surveys that produce reliable, unbiased data. Turns research goals into clear questions, chooses appropriate scales and response types, avoids leading and double-barreled wording, orders questions to reduce bias, and plans screening and branching logic. Outputs a ready-to-field questionnaire with an analysis plan for each question.
Helps turn a game concept into a structured game design document. Captures the core loop, mechanics, progression, economy, controls, level structure, art and audio direction, and target platform and audience. Keeps scope realistic, flags dependencies and risks, and produces a living GDD that a small team can build from.
Writes idempotent Ansible playbooks and roles from a described target state. Structures tasks with proper handlers, variables, and templates; favors modules over shell commands; applies role-based layout and inventory grouping; and adds check-mode safety and tags. Produces playbooks that are re-runnable without unintended side effects.
Skills 与 MCP 服务器
有什么区别?Skills定义“做什么”
Skill 将说明、示例提示词和推荐模型组织在一起,让智能体稳定地完成任务。Skills 增加的是知识,而不是新的连接。
MCP 服务器定义“如何连接”
MCP 服务器通过连接数据库、API 和文件等真实系统,为智能体增加新能力。MCP 提供连接和操作,而不是任务说明。
简单来说:当模型需要把某项任务做好时选择 Skill;当模型需要访问工具或系统时选择 MCP 服务器。两者可以组合使用,Skill 可以依赖 MCP 服务器提供的工具。
创建了实用的 Skill?
提交 SKILL.md,加入由社区维护的开源注册表。