agentleFS
28672032962867203296/hermes-agent/skills/software-development/skills-router/SKILL.md

skills-router

Skill0 starsChanged 3 months agoMITView on GitHub

1|--- 2|name: skills-router 3|description: "Auto-load matching skills based on user intent keywords. Inspired by gstack's routing pattern." 4|version: 1.0.0 5|triggers: 6| - router 7| - "which skill" 8| - "auto skill" 9| - "enable auto skills" 10| - "disable auto skills" 11|allowed-tools: 12| - Bash 13| - Read 14| - skill_view 15| - skills_list 16|--- 17| 18|# Skills Router 19| 20|Auto-detects user intent from messages and loads matching skills. Based on gstack's routing pattern: "When in doubt, invoke the skill…

  • Commits and pushes

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

1|--- 2|name: skills-router 3|description: "Auto-load matching skills based on user intent keywords. Inspired by gstack's routing pattern." 4|version: 1.0.0 5|triggers: 6| - router 7| - "which skill" 8| - "auto skill" 9| - "enable auto skills" 10| - "disable auto skills" 11|allowed-tools: 12| - Bash 13| - Read 14| - skill_view 15| - skills_list 16|--- 17| 18|# Skills Router 19| 20|Auto-detects user intent from messages and loads matching skills. Based on gstack's routing pattern: "When in doubt, invoke the skill — a false positive is cheaper than a false negative." 21| 22|## How It Works 23| 24|1. Scan user's message for trigger keywords 25|2. Match against registered skill triggers 26|3. Auto-load top matching skill via skill_view() 27|4. Reply with loaded skill context 28| 29|## Routing Rules 30| 31|When you see these patterns, load the corresponding skill via skill_view(): 32| 33|### Agent & Infrastructure 34|- "agent rules", "agent behavior", "doctrine" → agent-doctrine 35|36|- "webhook", "subscribe", "event trigger" → webhook-subscriptions 37|- "cognee", "memory backend", "vector memory" → hermes-cognee 38|- "godmode", "jailbreak", "red team", "越狱" → godmode 39|- "python debug", "debugpy", "VSCode debug", "attach debugger" → python-debugpy 40|- "learn yourself", "自主学", "新工具不熟", "查官方文档", "自学技能" → self-learning 41| 42|### Gaming 43|- "minecraft", "MC server", "modpack" → minecraft-modpack-server 44|- "pokemon", "宝可梦", "pixelmon" → pokemon-player 45| 46|### Code & Development 47|- "lint", "format", "style check" → hooks-system (auto-format hooks) 48|- "hook", "pre-commit", "post-save" → hooks-system 49|- "review code", "code review", "PR review" → requesting-code-review 50|- "test", "TDD", "unit test" → test-driven-development 51|- "refactor", "clean up code" → simplify-code 52|- "debug", "bug", "why is this broken" → systematic-debugging 53|- "plan", "architecture", "design doc" → plan 54|- "repo map", "codebase map", "项目地图" → repomap 55|- "token save", "省 token", "简洁" → concise-output 56|- "agent team", "multi agent", "parallel agents", "orchestrate", "多 Agent", "kanban", "merge gate" → agent-orchestration (统一编排) 57|- "auto memory", "auto learn", "continuous learning", "session summary" → auto-memory 58|- "ECC", "everything claude code" → (research: ECC knowledge base node + GitHub API) 59|- "MCP gateway", "meta MCP", "MCP 网关", "MCP 编排", "MCP 401", "MCP auth", "add mcp tool", "REST to MCP" → mcp-gateway (统一 MCP 全栈) 60|- "build", "develop", "implement", "feature", "做功能", "开发", "全流程", "auto develop" → superpowers-workflow (全自动流水线) 🆕 61|- "codebase", "index", "codegraph", "代码索引", "project structure", "find in project" → codegraph-index (增量代码索引) 🆕 62| 63|### ECC Imports 64|- "instinct", "continuous learn", "confidence score", "本能学习" → continuous-learning-v2 65|- "iterative retrieval", "subagent context", "context problem" → iterative-retrieval 66|- "token budget", "response depth", "brief answer", "detailed" → token-budget-advisor 67|- "context budget", "context limit", "上下文预算" → context-budget 68|- "compact", "compress context", "压缩上下文" → strategic-compact 69|- "prompt optimize", "prompt engineering", "提示优化" → prompt-optimizer 70|71|- "verification", "eval loop", "pass@k", "验证循环" → verification-loop 72|- "agent eval", "agent benchmark", "评估" → agent-eval 73|- "security scan", "security audit", "安全扫描", "safety guard", "安全护栏", "guarail", "destructive" → security (统一安全) 74|75|- "knowledge ops", "知识运营", "knowledge sync", "ingest knowledge" → knowledge-base (吸收 knowledge-ops) 76|77| 78|### Architecture & Research 79|- "Claude Code", "agent framework", "agent 架构" → load hermes-agent then research via API 80|- "GitHub research", "挖 GitHub", "search repos" → skills-router (see references/github-api-research.md) 81| 82|### Project Management 83|- "GitHub", "github", "issue", "PR", "pull request", "merge", "clone", "fork", "code review", "gh", "git push", "branch", "release", "workflow", "actions", "CI", "token", "auth setup" → github-workflow (统一 GitHub 入口) 84|- "codebase stats", "LOC", "language breakdown", "pygount" → codebase-inspection 85|- "cron", "schedule", "定时" → (use cronjob tool, not a skill) 86| 87|### Infrastructure 88|- "deploy", "CI/CD", "pipeline" → github-workflow 89|- "config", "setup Hermes" → hermes-agent 90|- "memory", "remember this" → (use memory tool) 91|- "knowledge base", "笔记", "Obsidian", "知识库", "vault", "知识图谱", "加到图谱", "更新图谱", "全量更新" → knowledge-base (优先) 或 obsidian 92| 93|### Creative 94|- "ASCII", "figlet", "banner" → ascii-art 95|- "diagram", "架构图", "流程图" → architecture-diagram 96|- "UI", "design", "mockup" → popular-web-designs 97|- "heartmula", "Suno-like", "song generate", "AI 写歌" → heartmula 98| 99|### Social & Apple 100|- "xurl", "twitter share", "X post", "发推" → xurl 101|- "imessage", "iMessage", "Apple message", "苹果短信" → imessage 102|- "findmy", "Find My", "查找设备", "定位 iPhone" → findmy 103| 104|### Data & Research 105|- "paper", "arXiv", "论文" → arxiv 106|- "research paper", "paper writing", "学术写作", "论文写作" → research-paper-writing 107|- "YouTube", "transcript", "视频总结" → youtube-content 108|- "blog", "RSS", "监控" → blogwatcher 109|- "notebook", "Jupyter", "数据分析" → jupyter-live-kernel 110|- "audiocraft", "music gen", "audio generation", "音乐生成" → audiocraft 111|- "vllm", "LLM serving", "模型部署", "inference engine" → vllm 112|- "eval harness", "benchmark", "模型评测", "lm eval" → lm-evaluation-harness 113| 114|## Proactive Mode 115| 116|By default, the router is proactive: it suggests and loads relevant skills without being asked. The user can disable with "stop suggesting skills" or enable with "be proactive about skills". 117| 118|When proactive is ON: 119|1. Before answering any user request, scan for matching skill triggers 120|2. If a skill matches, load it via skill_view() FIRST 121|3. Then proceed with the task using the skill's context 122| 123|When proactive is OFF: 124|1. Only load skills when the user explicitly asks ("/skill name" or "use the X skill") 125|2. Answer directly without scanning 126| 127|## Usage 128| 129|Load this skill at session start: 130| 131|hermes -s skills-router 132| 133| 134|Or in-session: 135| 136|/skill skills-router 137| 138| 139|Then just talk normally — the router will auto-load relevant skills for each request. 140| 141|## Configuration 142| 143|Custom routing rules can be added to ~/.hermes/skills/router-rules.json: 144| 145|json 146|{ 147| "keyword": "skill-name", 148| "deploy k8s": "kubernetes-deploy" 149|} 150| 151| 152|## References 153| 154|- references/github-api-research.md — GitHub API + raw file fetch pattern for restricted networks (no git clone needed) 155|- references/github-access-china.md — GitHub access from China: status matrix, reliable patterns, anti-patterns 156|

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.