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Skills are somebody’s method, written down. MCP is how your agent reaches systems it otherwise cannot touch. 325,949 listed, and nobody here has run most of them.

Stacks

One person’s picks, with their name on them

  • Write the thing that does not exist yet

    For the point where the answer stops being "install something" and starts being "build it". Two Anthropic meta-skills, read together rather than in sequence, because the skill-or-server decision is what the pair of them makes legible and neither one makes on its own.

    1. 1Skill creator
    2. 2Mcp builder

    agentscliIn house · 2 steps

  • Write against the API that exists today

    The worst failure in third-party integration work does not look like a failure. The call is idiomatic, the types line up, and the method was removed two majors ago. Two sources of truth on two different clocks, and the case that matters is not when they disagree but when one of them is silent.

    • Context7
    • GitHub

    agentscliIn house · 2 items

  • Read a codebase you did not write

    Three servers that answer the three different questions an agent gets wrong on a repository it has never seen: where is anything, why is it like that, and what version am I calling. Curated stacks in the wild carry all three.

    • Serena MCP: the IDE for your agent
    • GitHub
    • Context7

    agentscliIn house · 3 items

  • Find the cause before writing the fix

    A stack trace tells you where the program died, not where it went wrong. Two servers that answer the two questions the trace cannot: what actually happened out there, and why the code is shaped like that. This is the pairing people doing incident work are documented as running.

    1. 1Sentry mcp
    2. 2GitHub

    agentscliIn house · 2 steps

  • Build the design, then go and look

    An agent implementing a design generates pixels and reasons in text. It will tell you the header is blue and centred while it renders red and left. Read the design as data, then render it in a real browser and compare. This loop is the one the write-ups actually describe.

    1. 1Figma Context MCP
    2. 2Playwright mcp

    agentscliIn house · 2 steps

  • A review that is not a rubber stamp

    Three systems that can each contradict the pull request description in a different way. This is the reach layer, it is the part of the problem the ecosystem has actually solved, and on its own it is not yet a review. The page says which half is missing.

    1. 1GitHub
    2. 2Context7
    3. 3Sentry mcp

    agentscliIn house · 3 steps

Turning up in stacks

The only ranking signal here that is not a popularity count

  • GitHub

    github/github-mcp-server/io.github.github/github-mcp-server MCP server

    in 4 stacks32,051★ repo

    Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

  • Context7

    upstash/context7/io.github.upstash/context7 MCP server

    in 3 stacks60,787★ repo

    Up-to-date code docs for any prompt

  • Sentry mcp

    getsentry/sentry-mcp/io.github.getsentry/sentry-mcp MCP server

    in 2 stacksno license808★ repo

    MCP server for Sentry - error monitoring, issue tracking, and debugging for AI assistants

  • Mcp builder

    anthropics/skills/skills/mcp-builder Skill

    in 1 stackno license168,934★ repo

    Public repository for Agent Skills

  • Playwright mcp

    microsoft/playwright-mcp/io.github.microsoft/playwright-mcp MCP server

    in 1 stack36,126★ repo

    Playwright Tools for MCP

  • Serena MCP: the IDE for your agent

    oraios/serena/io.github.oraios/serena MCP server

    in 1 stack27,951★ repo

    A powerful toolkit for coding, providing semantic retrieval and editing capabilities.

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