Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council
Route any prompt to the best-performing LLM using peer-reviewed council rankings from LLM Council
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
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.
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.
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.
https://clawbot.llmcouncil.ai
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.
Find the best-performing model for a query.
{ "query": "Explain quantum entanglement simply", "k": 20 } FieldTypeRequiredDescriptionquerystringYesThe prompt or question to routekintegerNoNumber of past evaluations to consider (default: 20)
{ "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 (0β1)How closely matched evaluations relate to your query. Above 0.75 is strong.confidencefloat (0β1)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
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"])
TierDaily LimitAttributionFree100 requests/dayRequiredPro10,000 requests/dayNone
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
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.