SuperSkill
Related Servers
Alternatives to SuperSkill
No user-submitted related servers found.
Related Servers
- AlicenseAqualityBmaintenanceEnables AI agents to manage, search, plan, and execute reusable skills while automatically enforcing always-on coding and reasoning guidelines. It provides workflow orchestration, permission-controlled execution, dynamic skill registration, and feedback-based ranking.911 npm1MIT
- AlicenseNot gradedqualityCmaintenanceProvides context-aware skill selection for AI agents, reducing token usage by 85-98% and improving accuracy through semantic retrieval, session memory, and feedback learning.1MIT
- AlicenseNot gradedqualityFmaintenanceProvides dynamic, context-aware code assistant skills through hybrid RAG (vector + knowledge graph), enabling runtime skill discovery, automatic toolchain-based recommendations, and on-demand loading from multiple git repositories.69 PyPI20MIT
- AlicenseNot gradedqualityFmaintenanceEnables AI agents to intelligently match tasks to skills through semantic embeddings, track skill effectiveness, detect skill gaps, and discover new skills from external sources.Apache 2.0
- AlicenseAqualityDmaintenanceRoutes SKILL.md libraries to any MCP client, enabling task matching and skill loading with embedding-based scoring, keyword fallback, and context-window discipline.59 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to search and retrieve over 89K skills on-demand at runtime, eliminating the need to manually install skills upfront.134MIT
TDQS
Scored across 52 tools
Many tools overlap in purpose: capture, extract, learn, decide, task, and brainstorm all store different knowledge artifacts, while status, stats, doctor, telemetry, hygiene_report, watchdog, resume, and session all report on project/session state. Descriptions help somewhat, but an agent faces multiple plausible choices for common actions like recalling context, recording knowledge, or checking health.
Naming mixes single-word nouns and verbs (spec, tickets, gate, read, write) with prefixed snake_case families (worktree_*, skill_*, graph_*, knowledge_*). The groupings are readable, but there is no single consistent verb_noun pattern across all 52 tools.
52 tools is far beyond the typical concise MCP surface and exceeds the rubric's 50+ extreme mismatch threshold. Even with broad knowledge, skill, worktree, and hygiene domains, this many tools creates a high selection burden and likely includes low-frequency operations better exposed as actions within fewer multiplexed tools.
The surface is extensive and covers most domains it names: vault read/write/search/link/capture/extract, skill install/list/remove/validate, worktree lifecycle, spec/ticket/evidence/gate workflows, graph traversal, and session coordination. Only minor gaps exist, such as explicit edit/delete operations for individual vault notes beyond deprecate/prune, but agents can work around them.