Skills

The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.

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Skills

17

Categories

9

Compatible tools

5

Contributors

1

Showing 117 of 17 skills

Resume Optimizerbeginner

Reviews and rewrites resumes and CVs to be clear, achievement-focused, and ATS-friendly. Rewrites bullet points using strong action verbs and quantified impact, aligns wording to a target job description, flags gaps and red flags, and checks formatting for applicant tracking system compatibility.

4 models
Color Palette Generatorbeginner

Creates accessible color palettes for brands and UIs from a brief or a seed color. Produces primary, secondary, and neutral scales with hex values, suggests semantic tokens (success, warning, error, info), and checks foreground/background pairings against WCAG contrast ratios. Outputs ready-to-use CSS variables or design tokens.

4 models
SQL Query Explainerbeginner

Explains what a SQL query does in plain language and how it executes. Breaks down joins, subqueries, CTEs, and window functions step by step, describes the result set, reads EXPLAIN/EXPLAIN ANALYZE output to identify slow scans and missing indexes, and flags correctness pitfalls. Helps developers understand, review, and trust unfamiliar SQL.

4 models
Blog Post Writerbeginner

Drafts long-form blog posts from a topic, outline, or set of notes. Handles title and hook generation, logical section structure, SEO-aware headings, tone matching, internal linking suggestions, and a clear call to action. Produces publish-ready Markdown with meta description and suggested tags.

4 models
Code Comment Generatorbeginner

Adds clear, accurate inline comments and API doc blocks to existing code without changing behavior. Generates docstrings and structured comments (JSDoc, Google/NumPy style, Javadoc, Rustdoc) that explain intent, parameters, return values, side effects, and edge cases, while avoiding noisy comments that merely restate the code.

4 models
Chart Generatorbeginner

Turns raw data and natural-language requests into clear, well-labeled charts and the code to render them. Recommends the right chart type for the data and message, handles aggregation and formatting, and outputs production-ready visualizations using libraries like Matplotlib, Plotly, Vega-Lite, or Chart.js with accessible color palettes.

3 models
Brainstorm Facilitatorbeginner

Facilitates structured brainstorming sessions using proven ideation frameworks — SCAMPER, Six Thinking Hats, How Might We, Crazy Eights, and more. Generates diverse ideas, challenges assumptions, and helps converge on the strongest concepts.

3 models
Code Explainerbeginner

Explains complex code in plain language at the requested level of detail. Breaks down algorithms, design patterns, and architecture decisions. Adapts explanation depth from high-level overview to line-by-line walkthrough based on audience.

4 models
Changelog Generatorbeginner

Generates structured changelogs from git history, commit messages, or PR descriptions. Follows Keep a Changelog format, groups changes by type (Added, Changed, Fixed, Removed), and highlights breaking changes. Supports semantic versioning recommendations.

3 models
Professional Email Writerbeginner

Drafts professional emails for various business contexts — follow-ups, introductions, requests, escalations, and announcements. Adapts tone from formal to friendly based on audience and relationship. Keeps messages concise and action-oriented.

3 models
Test Data Generatorbeginner

Generates realistic test data, fixtures, and seed files for databases and APIs. Creates data that respects constraints (foreign keys, unique fields, valid formats) and covers edge cases. Supports JSON, SQL, CSV, and factory patterns.

4 models
Environment Config Generatorbeginner

Generates .env files, configuration schemas, and environment variable documentation from application requirements. Includes validation rules, default values, required vs optional flags, and example values. Supports multiple environments (dev/staging/prod).

3 models
Error Message Improverbeginner

Rewrites vague or technical error messages into clear, actionable user-facing messages. Considers the audience (end-user vs developer), suggests error codes, and provides guidance on what the user can do to resolve the issue.

3 models
PR Description Generatorbeginner

Generates clear, structured pull request descriptions from code diffs. Includes summary of changes, motivation, testing notes, and reviewer guidance. Follows team conventions and links related issues automatically.

3 models
Cron Expression Builderbeginner

Builds and explains cron expressions from natural language schedules. Supports standard cron (5-field), extended cron (6-field with seconds), and cloud-specific formats (AWS EventBridge, Google Cloud Scheduler). Validates expressions and shows next run times.

3 models
Commit Message Writerbeginner

Generates clear, conventional commit messages from code diffs. Follows Conventional Commits specification with appropriate type prefixes, scopes, and descriptions. Handles breaking changes, multi-file changes, and produces both concise subjects and detailed bodies.

4 models
Regex Builderbeginner

Builds, explains, and tests regular expressions from natural language descriptions. Supports multiple regex flavors (PCRE, JavaScript, Python, Go). Provides step-by-step breakdowns, test cases, and performance considerations for complex patterns.

4 models

Skills vs MCP servers

what's the difference?

Skillsthe “what to do”

A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.

MCP serversthe “how to connect”

An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.

Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.

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