Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
18
Categories
9
Compatible tools
6
Contributors
2
Showing 1–18 of 18 skills
Helps process a busy inbox quickly. Categorizes messages by urgency and type, extracts action items and deadlines, drafts short reply options, and proposes an order to handle them. Summarizes long threads into the decision needed and flags anything sensitive or requiring escalation, so you can clear the inbox with fewer decisions.
Turns a messy list of tasks into a ranked, actionable plan. Applies a chosen framework (Eisenhower urgent/important, RICE, MoSCoW, or value/effort) consistently, surfaces dependencies and quick wins, groups work into a realistic today/this-week plan, and explains the ranking so the user can adjust the weighting.
Helps make and document structured decisions. Elicits the options and the criteria that matter, assigns weights, scores each option, computes a weighted result, and runs a quick sensitivity check to show how robust the recommendation is. Produces a clear matrix plus a short written rationale suitable for an ADR or decision log.
Drafts clear product requirements documents from rough ideas or stakeholder notes. Structures the problem statement, goals and non-goals, user stories, acceptance criteria, success metrics, and open questions, and surfaces ambiguities and edge cases that need decisions before engineering starts.
Turns strategy into well-formed Objectives and Key Results. Coaches on ambitious yet measurable objectives, outcome-based (not output-based) key results, leading vs lagging indicators, alignment across teams, and quarterly cadence with check-ins and scoring. Flags common anti-patterns like task lists disguised as OKRs.
Builds focused, time-boxed meeting agendas from a goal and a list of topics. Defines clear objectives and desired outcomes, allocates time per item, assigns owners, suggests pre-reads, and prepares decision points and discussion prompts so meetings stay purposeful and produce action items.
Drafts accurate, empathetic customer support replies grounded in a knowledge base or help docs. Classifies intent and urgency, matches the right tone, proposes solutions with clear steps, suggests escalation when needed, and outputs canned-response and macro templates for common ticket categories.
Transforms feature ideas and product requirements into well-formed agile user stories with clear acceptance criteria. Follows the "As a / I want / so that" format, adds Gherkin-style Given/When/Then criteria, estimates relative complexity, and breaks epics into right-sized stories ready for sprint planning.
Turns raw meeting transcripts or rough notes into clear, structured summaries. Extracts key decisions, action items with owners and due dates, open questions, and discussion highlights. Produces a concise recap suitable for sharing, plus an optional follow-up email draft.
Discovers, evaluates, and prioritizes App Store keywords for mobile apps. Expands seed keywords using autocomplete suggestions, competitor rankings, and category analysis, then scores each keyword by volume, difficulty, and relevance to build a prioritized keyword strategy with primary, secondary, long-tail, and aspirational buckets.
Crafts App Store metadata — title, subtitle, keyword field, and description — that maximizes both search visibility and conversion rate. Applies platform-specific character limits and indexing rules for iOS and Android, provides multiple copy variants per field, and outputs a keyword coverage matrix with before/after comparison.
Performs a full App Store Optimization health check across 10 weighted dimensions: title, subtitle, keyword field, description, screenshots, preview video, ratings and reviews, icon, keyword rankings, and conversion signals. Produces a scored ASO report card (0–100) with quick wins, high-impact changes, and strategic recommendations prioritized by effort and expected impact.
Fetches current weather conditions and forecasts for any location using free public APIs. Provides temperature, humidity, wind, precipitation probability, and multi-day forecasts. Useful for agents that need environmental context for travel planning, event scheduling, or outdoor activity recommendations.
Generates professional PowerPoint presentations from text descriptions, outlines, or research notes. Creates structured slide decks with appropriate layouts, bullet points, speaker notes, and visual hierarchy. Outputs valid PPTX files or structured JSON for presentation frameworks.
Records learnings, mistakes, and corrections to enable continuous improvement of AI agent behavior. Maintains a structured memory of failures, user corrections, outdated knowledge, and discovered better approaches. Reviews past learnings before executing important tasks to avoid repeating mistakes.
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.
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.
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.
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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