MCP server exposing superpowers skills to any LLM client
MCP server exposing superpowers skills to any LLM client. The superpowers mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 12 defined tools rather than through you.
Superpowers is a skills library for Claude Code that enforces disciplined workflows -- TDD, systematic debugging, brainstorming, planning, and more. This server exposes those skills to any MCP-compatible client: Cursor, Windsurf, Gemini, Kilo Code, Claude Desktop, or your own app.
Everything the assistant can do here goes through one of these:
list_skills — List all available skills with descriptions and file listsuse_skill — Load a skill by name -- returns the full skill content as instructions to follow (optional guardrail enforcement)get_skill_file — Load a supporting file from a skill (reference docs, prompt templates, scripts)recommend_skills — Recommend top skills for a task using semantic ranking + workflow policy boostscompose_workflow — Build an ordered multi-skill workflow for a goalvalidate_workflow — Validate selected skills against required workflow guardrailssemantic_search_skills — Semantic search across SKILL.md and supporting filesTools — The Tools tool exposed by this serverPrompts — Each skill is registered as an MCP prompt named superpowers:{skill-name}. Clients that support prompt selection will show these in their UIResources — Each skill's SKILL.md and supporting files are registered as resources:Architecture — The Architecture tool exposed by this serverTests — The Tests tool exposed by this serverInstallation goes through your MCP client rather than a global install: point it at npm on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Superpowers MCP's toolset — list_skills, use_skill, get_skill_file and 9 more — is a fair guide to whether it matches your workflow. It is maintained by erophames; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| list_skills | List all available skills with descriptions and file lists |
| use_skill | Load a skill by name -- returns the full skill content as instructions to follow (optional guardrail enforcement) |
| get_skill_file | Load a supporting file from a skill (reference docs, prompt templates, scripts) |
| recommend_skills | Recommend top skills for a task using semantic ranking + workflow policy boosts |
| compose_workflow | Build an ordered multi-skill workflow for a goal |
| validate_workflow | Validate selected skills against required workflow guardrails |
| semantic_search_skills | Semantic search across SKILL.md and supporting files |
| Tools | The Tools tool exposed by this server. |
| Prompts | Each skill is registered as an MCP prompt named superpowers:{skill-name}. Clients that support prompt selection will show these in their UI. Selecting a prompt injects the full skill content into the conversation. |
| Resources | Each skill's SKILL.md and supporting files are registered as resources: |
| Architecture | The Architecture tool exposed by this server. |
| Tests | The Tests tool exposed by this server. |
{
"mcpServers": {
"superpowers": {
"command": "node",
"args": ["/absolute/path/to/superpowers-mcp/build/index.js"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.