skillsmp-mcp 0.2.0

MCP server for SkillsMP: search, read-only GitHub fetch, full Cisco AI Skill Scanner, scan-gated bulk install, and bulk uninstall.

skillsmp-mcp

Python MCP server for SkillsMP

search the catalogue, read a skill's SKILL.md straight from GitHub, run the full Cisco AI Skill Scanner + LLM scanner, and install skills only after they pass the scan.

Tools

Tool	What it does
search_skills(query, limit, sort_by)	Keyword search (sort_by: stars/recent).
read_skill(repo, skill_name)	Fetch SKILL.md from a GitHub owner/repo, read-only. Never scans, never spawns a subprocess, never installs.
scan_skill(repo, skill_name)	Full Cisco scan; reports findings only.
install_skills(skills, force=False)	Scan-gated bulk install. skills is a list of owner/repo:skill_name specs (may span multiple repos). Each is resolved, scanned, and gated independently; refuses HIGH/CRITICAL unless force=True. Reports per-skill results without aborting the batch on one failure.
uninstall_skills(skills)	Remove installed skills (bulk). skills is a list of owner/repo:skill_name specs (may span multiple repos). No scan/network; reports per-skill results (removed / not installed / error).

Requirements

• Python 3.10+
• A SkillsMP API key (sk_live_...) from skillsmp.com — the only hard requirement.
• For scan_skill / install_skills: either uv installed (the server runs the scanner via uvx on demand) or the cisco-ai-skill-scanner package installed directly. Without either, scans return SKIPPED and installs are blocked.
• Recommended: an Anthropic API key (sk-ant-...) to enable the LLM semantic analyzer.

Install

The package is on PyPI. The simplest path is to let uvx fetch and run it on demand — nothing to install or keep updated:

uvx skillsmp-mcp          # runs the server (reads keys from the environment)

Or install a persistent command:

pipx install skillsmp-mcp     # isolated global command (recommended)
# or
pip install skillsmp-mcp      # into the current environment

Either install gives you two ways to launch it — the skillsmp-mcp console script and python -m skillsmp_mcp (handy when the script isn't on PATH).

For scan_skill / install_skills you also need the Cisco scanner reachable: have uv installed (the server runs it via uvx on demand) or pipx install cisco-ai-skill-scanner. Without it, scans return SKIPPED and installs are blocked.

Quick smoke test (no keys needed — completes the MCP handshake, lists the 5 tools, then exits):

printf '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"t","version":"0"}}}\n' \
  | SKILLSMP_API_KEY=dummy uvx skillsmp-mcp

Configure & register with an MCP client

Keys are supplied through the MCP server's env block — the code reads them from the process environment, which the client injects at launch. There is no .env file to manage. Most clients share the same command / args / env JSON schema (Claude Desktop, Claude Code, Cursor); Codex uses TOML. Pick your client below.

Claude Desktop

Add to claude_desktop_config.json (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json). Merge into mcpServers — don't replace the block.

"skillsmp": {
  "command": "/Users/you/.local/bin/uvx",
  "args": ["skillsmp-mcp"],
  "env": {
    "SKILLSMP_API_KEY": "sk_live_...",
    "SKILL_SCANNER_LLM_API_KEY": "sk-ant-...",
    "SKILL_SCANNER_LLM_MODEL": "anthropic/claude-sonnet-5"
  }
}

SKILLSMP_API_KEY is the only hard requirement. Set SKILL_SCANNER_LLM_API_KEY too so the semantic analyzer runs — without it, scans fall back to static + behavioral only and can miss prose-based prompt injection. All other variables (see the table below) are optional and go in the same env block.

Use absolute paths for command. GUI apps like Claude Desktop don't inherit your shell PATH, so a bare uvx (or skillsmp-mcp) often won't resolve. Run which uvx to get the full path and use that (as shown above). If you installed with pipx instead, point command straight at the installed script — find it with which skillsmp-mcp — and drop the args line.

For the same PATH reason, set SKILLSMP_SCANNER_CMD to the absolute uvx path so the scanner is found when the server runs under the client:

"SKILLSMP_SCANNER_CMD": "/Users/you/.local/bin/uvx --from cisco-ai-skill-scanner skill-scanner"

Fully quit and relaunch Claude Desktop after editing.

Claude Code

Claude Code is separate from Claude Desktop — it does not read claude_desktop_config.json. Register it with the CLI:

claude mcp add skillsmp \
  -e SKILLSMP_API_KEY=sk_live_... \
  -e SKILL_SCANNER_LLM_API_KEY=sk-ant-... \
  -- uvx skillsmp-mcp

By default the server is scoped to the current project; add -s user to make it available everywhere. Alternatively, commit a project-level .mcp.json at the repo root using the same command/args/env schema as the Claude Desktop block above. A project .mcp.json is also the place to set a repo-local install root — e.g. "SKILLSMP_INSTALL_DIR": ".claude/skills" — so install/uninstall write into the repo instead of ~/.claude/skills.

Cursor

Cursor uses the same JSON schema as Claude Desktop, in ~/.cursor/mcp.json (global) or .cursor/mcp.json (project). Merge into mcpServers:

{
  "mcpServers": {
    "skillsmp": {
      "command": "/Users/you/.local/bin/uvx",
      "args": ["skillsmp-mcp"],
      "env": {
        "SKILLSMP_API_KEY": "sk_live_...",
        "SKILL_SCANNER_LLM_API_KEY": "sk-ant-..."
      }
    }
  }
}

Like Claude Desktop, Cursor is a GUI app that won't inherit your shell PATH — use absolute paths for command and SKILLSMP_SCANNER_CMD (see the note above).

Codex

Codex uses TOML at ~/.codex/config.toml. Add an mcp_servers table:

[mcp_servers.skillsmp]
command = "uvx"
args = ["skillsmp-mcp"]
env = { SKILLSMP_API_KEY = "sk_live_...", SKILL_SCANNER_LLM_API_KEY = "sk-ant-..." }

Environment variables

Var	Required	Default	Purpose
SKILLSMP_API_KEY	yes	—	SkillsMP REST auth (sent only to skillsmp.com).
SKILL_SCANNER_LLM_API_KEY	recommended	—	Enables the LLM semantic analyzer.
SKILL_SCANNER_LLM_MODEL	no	anthropic/claude-sonnet-5	LiteLLM model string.
GITHUB_TOKEN	no	—	Read-only PAT; raises GitHub rate limit.
SKILLSMP_INSTALL_DIR	no	~/.claude/skills	Install/uninstall root. For repo-local skills, set it to e.g. .claude/skills in a project-scoped .mcp.json; both install_skills and uninstall_skills then operate on that root.
SKILLSMP_BLOCK_SEVERITIES	no	HIGH,CRITICAL	Severities that block install.
SKILLSMP_SCANNER_CMD	no	auto-detect	Override the scanner command.
SKILLSMP_SCANNER_POLICY	no	—	Cisco policy preset (e.g. strict).
SKILLSMP_SCANNER_TIMEOUT	no	300	Scan timeout (seconds).
SKILLSMP_MAX_FILES / SKILLSMP_MAX_SINGLE_FILE_BYTES / SKILLSMP_MAX_TOTAL_BYTES	no	100 / 512000 / 5242880	Fetch/scan size caps.
VIRUSTOTAL_API_KEY / AI_DEFENSE_API_KEY	no	—	Optional cloud engines.

For standalone runs outside a Claude client (e.g. terminal testing), just export the variables in your shell first — the server reads the same environment either way.

Security model

• Your SkillsMP key is sent only to skillsmp.com. GitHub fetches are unauthenticated (or use a read-only GITHUB_TOKEN); the two never mix.
• Skill files are fetched read-only, written to a private temp dir for scanning, and that dir is deleted afterward. Cisco's analysis is static/AST — nothing is executed.
• install_skills scans and gates each skill independently: it refuses HIGH/CRITICAL findings unless forced, refuses to install unscanned (scanner missing) unless forced, and never silently overwrites — re-installing an existing skill folder also requires force=True.
• Installs are namespaced as <owner>-<repo>__<skill-dir> so skills from different sources can't collide and a crafted name can't escape the install root.
• A clean scan is not a safety guarantee — it means no known patterns matched. Read the SKILL.md yourself; read_skill labels it untrusted for that reason.

License

MIT — see LICENSE.