Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
6
Categories
9
Compatible tools
4
Contributors
1
Showing 1–6 of 6 skills
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 Designed to reduce hallucinated or outdated facts before publishing.
Builds structured competitive analyses from public information. Maps competitors across positioning, pricing, features, target segments, and go-to-market motion; produces feature-comparison matrices and SWOT summaries; and highlights differentiation gaps and opportunities. Emphasizes citing sources and separating verified facts from inference.
Builds discovery and usability interview guides that surface real insight. Translates research questions into open, non-leading prompts, sequences warm-up to deep-dive topics, adds follow-up probes, and applies techniques like the "five whys" and past-behavior questions. Outputs a timed guide with a consent intro and a synthesis template for notes.
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.
Conducts structured market and competitor research and turns it into a decision-ready brief. Covers market sizing (TAM/SAM/SOM), competitor feature and pricing matrices, positioning and differentiation, customer segments, and SWOT, with clearly stated assumptions and sources so conclusions can be traced and challenged.
Summarizes research papers, technical articles, and documentation into structured briefs. Extracts key findings, methodology, limitations, and practical implications. Adapts output format from executive summary to detailed technical breakdown.
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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