Skills MCP Server
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- AlicenseAqualityCmaintenanceServes 484 Claude Code skills and 17 global rules as MCP tools, enabling skill and rule retrieval, keyword search, and project-level rule discovery for any Claude client.118 npm2MIT
- AlicenseAqualityDmaintenanceEnables Claude to create, edit, run, and manage reusable skills stored locally, including executing scripts with automatic dependency management and environment variables. Works across all MCP-compatible clients like Cursor, Claude Desktop, and claude.ai.531MIT
- AlicenseNot gradedqualityBmaintenanceExposes specialized agents as MCP tools so users can manage and triage Jira tasks, analyze repositories for health and conventions, generate commit/PR/DevOps patterns, assemble reports, produce slides and infographics, and run semantic search over a knowledge base. Everything is invokable directly from inside a coding session in Claude Code, Cursor, or Claude Desktop.1MIT
- AlicenseNot gradedqualityDmaintenanceTurns Claude-style skill folders into MCP tools, resources, and prompts for discovering and using skills.5 npm1MIT
- AlicenseNot gradedqualityDmaintenanceTurns Claude-style skills (SKILL.md files with resources) into callable MCP tools for any agent. Discovers skills from a directory, exposes their instructions and resources, and can execute bundled helper scripts.224 PyPI409MIT
- AlicenseNot gradedqualityBmaintenanceEnables dynamic discovery, search, and on-demand retrieval of local Claude Skills, making them accessible as a composable skill registry across MCP-compatible hosts without manual configuration.1MIT
TDQS
Scored across 43 tools
Most tools have distinct purposes, but several clusters genuinely overlap: a11y_aria_snapshot, a11y_observe, a11y_announce and a11y_compare_browsers all surface the accessibility tree, and a11y_tab_order, a11y_reach and a11y_probe(kind=tab_walk) all report keyboard focus order. The very detailed descriptions mitigate this, but an agent still has to carefully pick between tree-reading and focus-walking tools.
Every tool uses a consistent a11y_ prefix and snake_case, which is highly predictable. However verb style is mixed: many are verb_noun (get_reference, read_file, list_content) while others are bare nouns (a11y_contrast, a11y_dossier, a11y_page_map, a11y_screenshot), a minor deviation from a strict convention.
At 43 tools this is a heavy surface, well beyond the 15-tool sweet spot, spanning browser sessions, audits, reporting, file editing, learning and a content base. It is a genuinely broad scope, but the sheer count makes selection and cognitive load a real burden for an agent.
The lifecycle is remarkably thorough: discovery and reference content, browser session control with approval gating, behavioral probes, fact-gathering, finding/report/export (JSON/EARL/SARIF), run diffing, cross-browser comparison, crawling, NVDA speech, and safe file editing with undo. Almost no obvious dead ends for the stated accessibility-testing purpose.