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route

Skill123 starsChanged 31 days agoMITView on GitHub

Route a task to the best LLM based on task type and complexity

17 other projects use a version of this.

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Projects with it also use learn and prototype.

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  • genomewalkerSkill

    learn

    Learn from web sources, generate SKILL.md, and store key patterns in soul memory. Trigger keywords: learn, skill-gen, web-learn, documentation, tutorial.

    Also in 3 projects that use this

  • GoogleSkill

    prototype

    Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 16 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Every 5th routed task, the system shows estimated savings: Claude API costs avoided and rate limit capacity preserved.

    line 57

“Common” is measured: kept by at least 6 in 10 of the 16 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/route — Smart LLM Task Router

Route any task to the optimal LLM automatically.

Usage

/route <task description>

Auto-Classification

Most prompts are classified automatically by the UserPromptSubmit hook — no /route needed. The hook uses a multi-layer classification chain:

  1. Heuristic scoring (instant, free) — Three signal layers accumulate evidence:
    • Intent patterns (+3) — action verbs and task markers
    • Topic patterns (+2) — domain-specific nouns
    • Format patterns (+1) — structural and temporal cues
    • High-confidence match (score >= 4) routes immediately
  2. Ollama local LLM (~1s, free) — When heuristics are uncertain, qwen3.5 classifies locally via the chat API with thinking disabled
  3. Cheap API model (~$0.0001) — If Ollama is unavailable, Gemini Flash or GPT-4o-mini classifies
  4. Weak heuristic / auto fallback — Last resort: low-confidence heuristic match or llm_route (full LLM classifier)

Task Categories

The consolidated llm(prompt=..., task=..., tier=...) tool is the front door for all of these — task= picks the specialization, tier= (fast/balanced/best) picks the cost tier:

CategoryCallSignals
Researchllm(task="research")Current events, news, funding, trends, market data, rankings
Generatellm(task="generate")Writing, drafting, brainstorming, emails, articles, translations
Analyzellm(task="analyze")Evaluation, debugging, comparison, trade-offs, code review
Codellm(task="code")Implementation, refactoring, building, bug fixes
Queryllm(task="query") (or task="auto")Simple questions, definitions, explanations
Imagellm_imageVisual generation, design, artwork

Complexity & Profiles

ComplexityProfileModel Tier
SimplebudgetGemini Flash, GPT-4o-mini
ModeratebalancedGPT-4o, Gemini 2.5 Pro
Complexpremiumo3, Gemini 2.5 Pro

Savings Awareness

Every 5th routed task, the system shows estimated savings: Claude API costs avoided and rate limit capacity preserved. Run llm_router_status(view="usage") for a detailed breakdown.

Examples

What are the top 3 AI startups that raised funding?
→ research (heuristic, score=8) → llm(task="research") → Perplexity Sonar

Write me a blog post about productivity tips
→ generate (heuristic, score=5) → llm(task="generate", tier="balanced") → Gemini 2.5 Pro

Compare React vs Vue for our new project
→ analyze (ollama, qwen3.5) → llm(task="analyze", tier="balanced") → GPT-4o

Implement a rate limiter in Python using sliding window
→ code (heuristic, score=4) → llm(task="code", tier="balanced") → GPT-4o

What is a monad?
→ query (ollama, qwen3.5) → llm(task="query", tier="fast") → Gemini Flash

Configuration

Environment variables:

  • LLM_ROUTER_OLLAMA_MODEL — Ollama model (default: qwen3.5:latest)
  • LLM_ROUTER_OLLAMA_URL — Ollama server (default: http://localhost:11434)
  • LLM_ROUTER_OLLAMA_TIMEOUT — Timeout in seconds (default: 5)
  • LLM_ROUTER_CONFIDENCE_THRESHOLD — Heuristic score cutoff (default: 4)

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