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LLM Council Router

Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council

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High Signal

Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council

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Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 9 sections Open source page

LLM Council Router

Route any prompt to the best-performing LLM. The API finds the top model for a given query based on thousands of peer-reviewed council deliberations β€” then you call that model directly.

Base URL

https://clawbot.llmcouncil.ai

Authentication

Use the X-API-Key header with your LLM Council API key. X-API-Key: clwb_YOUR_KEY_HERE Get a free key at llmcouncil.ai/developers.

Endpoint: POST /v1/route

Find the best-performing model for a query.

Request

{ "query": "Explain quantum entanglement simply", "k": 20 } FieldTypeRequiredDescriptionquerystringYesThe prompt or question to routekintegerNoNumber of past evaluations to consider (default: 20)

Response

{ "query": "Explain quantum entanglement simply", "nearest_councils": 20, "model": "anthropic/claude-sonnet-4", "relevance": 0.8234, "confidence": 0.65, "model_rankings": [ { "rank": 1, "model": "anthropic/claude-sonnet-4", "nearby_wins": 13, "nearby_appearances": 20 }, { "rank": 2, "model": "openai/gpt-4.1", "nearby_wins": 5, "nearby_appearances": 18 } ] } FieldTypeDescriptionmodelstringTop recommended model ID (matches OpenRouter catalogue)relevancefloat (0–1)How closely matched evaluations relate to your query. Above 0.75 is strong.confidencefloat (0–1)How decisively the top model outperforms alternatives. Higher = clearer winner.nearest_councilsintegerNumber of relevant past evaluations usedmodel_rankingsarrayAll models ranked by performance across matched evaluations

How to use this skill

When the user asks you to find the best model for a task, or when you need to decide which LLM to use: Call the routing API with the user's query: curl -X POST https://clawbot.llmcouncil.ai/v1/route \ -H "Content-Type: application/json" \ -H "X-API-Key: $LLMCOUNCIL_API_KEY" \ -d '{"query": "USER_QUERY_HERE"}' Read the response β€” the model field is the best-performing model for that query type. Chain with OpenRouter β€” model IDs match the OpenRouter catalogue directly, no mapping needed: import requests, os # Step 1: Get the best model from LLM Council route = requests.post( "https://clawbot.llmcouncil.ai/v1/route", headers={"X-API-Key": os.environ["LLMCOUNCIL_API_KEY"]}, json={"query": "Write a Python web scraper"}, ).json() best_model = route["model"] # e.g. "anthropic/claude-sonnet-4" confidence = route["confidence"] # e.g. 0.85 # Step 2: Call that model via OpenRouter answer = requests.post( "https://openrouter.ai/api/v1/chat/completions", headers={"Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}"}, json={ "model": best_model, "messages": [{"role": "user", "content": "Write a Python web scraper"}], }, ).json() print(answer["choices"][0]["message"]["content"])

Rate Limits

TierDaily LimitAttributionFree100 requests/dayRequiredPro10,000 requests/dayNone

When to use this

User asks "which model is best for X?" You need to pick the optimal model for a specific task type You want data-driven model selection instead of guessing You want to chain model routing with OpenRouter for automatic best-model dispatch

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
1 Docs
  • SKILL.md Primary doc