القائمة
Run Fireworks-hosted open-weight model councils that compare responses and synthesize a final answer.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/canar1406/thaytrieu --skill llm-councilيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| name | llm-council |
| description | Run Fireworks-hosted open-weight model councils that compare responses and synthesize a final answer. |
| allowed-tools | Read, Write, Bash, AskUserQuestion |
| category | ai-agents |
| risk | safe |
| source | official |
| source_repo | dair-ai/dair-academy-plugins |
| source_type | official |
| date_added | 2026-06-19 |
| author | DAIR.AI |
| license | MIT |
| license_source | https://github.com/dair-ai/dair-academy-plugins/blob/main/README.md#license |
| tags | ["dair-academy","ai","workflow"] |
| tools | ["claude-code","codex-cli","cursor"] |
Use when this workflow matches the user request: Use this skill for its documented workflow.
Source: dair-ai/dair-academy-plugins (MIT).
This skill implements Karpathy's LLM Council concept where multiple open-weight LLMs deliberate on a query, powered entirely by Fireworks AI:
All inference runs through Fireworks AI using open-weight models. The speed and pricing of Fireworks makes it practical to run multi-model deliberation that would be slow or expensive on other providers.
Before running any phase, verify the Fireworks API key is set:
if [ -z "$FIREWORKS_API_KEY" ]; then
echo "ERROR: FIREWORKS_API_KEY is not set."
echo "Create a Fireworks AI account at: https://fireworks.ai/"
echo "Then export it in your shell profile (~/.zshrc or ~/.bashrc):"
echo ' export FIREWORKS_API_KEY="your_api_key_here"'
exit 1
fi
echo "FIREWORKS_API_KEY is set."
Present these options to the user via AskUserQuestion (multiselect):
| Model | Fireworks ID | Provider |
|---|---|---|
| GLM 5 | accounts/fireworks/models/glm-5 | Z.ai |
| DeepSeek V3.1 | accounts/fireworks/models/deepseek-v3p1 | DeepSeek |
| DeepSeek V3.2 | accounts/fireworks/models/deepseek-v3p2 | DeepSeek |
| MiniMax M2.1 | accounts/fireworks/models/minimax-m2p1 | MiniMax |
| Kimi K2.5 | accounts/fireworks/models/kimi-k2p5 | Moonshot |
| Qwen3 235B | accounts/fireworks/models/qwen3-235b-a22b | Alibaba |
| Llama 4 Maverick | accounts/fireworks/models/llama4-maverick-instruct-basic | Meta |
Use AskUserQuestion to get:
Note: AskUserQuestion supports max 4 options per question. Since there are 7 models, split model selection across two questions, or show the most popular 4 and let the user type "Other" for the rest. A good default is to show 4 models in the first question and note the others are available via "Other". Rotate which models are shown based on variety.
Example AskUserQuestion for model selection (show 4, mention others):
question: "Which models should participate in the LLM Council? (Also available via Other: Llama 4 Maverick, Qwen3 235B, GLM 5)"
header: "Models"
multiSelect: true
options:
- label: "DeepSeek V3.2"
description: "DeepSeek's newest and most capable model"
- label: "MiniMax M2.1"
description: "MiniMax's strong open-weight model"
- label: "Kimi K2.5"
description: "Moonshot's strong open-weight model"
- label: "DeepSeek V3.1"
description: "DeepSeek's proven reasoning model"
Example AskUserQuestion for chairman:
question: "Which model should be the Chairman (synthesizes the final answer)?"
header: "Chairman"
multiSelect: false
options:
- label: "DeepSeek V3.2 (Recommended)"
description: "Newest DeepSeek, strong at comprehensive analysis"
- label: "GLM 5"
description: "Strong reasoning for synthesis"
- label: "Kimi K2.5"
description: "Strong at structured synthesis"
- label: "MiniMax M2.1"
description: "Strong open-weight model for synthesis"
Use this mapping to convert user selections to Fireworks model IDs:
MODEL_MAP = {
"GLM 5": "accounts/fireworks/models/glm-5",
"DeepSeek V3.1": "accounts/fireworks/models/deepseek-v3p1",
"DeepSeek V3.2": "accounts/fireworks/models/deepseek-v3p2",
"MiniMax M2.1": "accounts/fireworks/models/minimax-m2p1",
"Kimi K2.5": "accounts/fireworks/models/kimi-k2p5",
"Qwen3 235B": "accounts/fireworks/models/qwen3-235b-a22b",
"Llama 4 Maverick": "accounts/fireworks/models/llama4-maverick-instruct-basic",
}
After gathering input, run this script to get responses from all selected models in parallel:
QUERY="USER_QUERY_HERE"
MODELS='["accounts/fireworks/models/glm-5", "accounts/fireworks/models/deepseek-v3p1"]'
python3 << 'PYEOF'
import os
import json
import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
FIREWORKS_API_KEY = os.environ.get("FIREWORKS_API_KEY")
API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
QUERY = os.environ.get("QUERY", "")
MODELS = json.loads(os.environ.get("MODELS", "[]"))
# Create session directory
timestamp = time.strftime("%Y%m%d-%H%M%S")
SESSION_DIR = f"/tmp/llm-council/{timestamp}"
os.makedirs(SESSION_DIR, exist_ok=True)
# Save config
config = {"query": QUERY, "models": MODELS, "timestamp": timestamp}
with open(f"{SESSION_DIR}/config.json", "w") as f:
json.dump(config, f, indent=2)
def call_model(model_id, query):
"""Call a single model via Fireworks AI"""
try:
start = time.time()
response = requests.post(
API_URL,
headers={
"Authorization": f"Bearer {FIREWORKS_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": model_id,
"messages": [
{"role": "system", "content": "You are participating in an LLM council deliberation. Provide your best, most thoughtful response to the query. Be comprehensive but focused."},
{"role": "user", "content": query}
],
"max_tokens": 4000,
"temperature": 1
},
timeout=120
)
response.raise_for_status()
elapsed = time.time() - start
data = response.json()
usage = data.get("usage", {})
return {
"success": True,
"content": data["choices"][0]["message"]["content"],
"model": model_id,
"latency_seconds": round(elapsed, 2),
"tokens": {
"prompt": usage.get("prompt_tokens", 0),
"completion": usage.get("completion_tokens", 0),
"total": usage.get("total_tokens", 0)
}
}
except Exception as e:
return {
"success": False,
"content": f"[ERROR: {str(e)}]",
"model": model_id,
"latency_seconds": 0,
"tokens": {"prompt": 0, "completion": 0, "total": 0}
}
print(f"\n{'='*60}")
print("PHASE 1: Collecting Individual Responses")
print(f"{'='*60}")
print(f"Query: {QUERY[:200]}...")
print(f"Models: {', '.join([m.split('/')[-1] for m in MODELS])}")
print(f"Session: {SESSION_DIR}")
print()
# Parallel execution
results = {}
with ThreadPoolExecutor(max_workers=len(MODELS)) as executor:
futures = {executor.submit(call_model, m, QUERY): m for m in MODELS}
for future in as_completed(futures):
model = futures[future]
result = future.result()
results[model] = result
status = "OK" if result["success"] else "FAILED"
latency = f"{result['latency_seconds']}s" if result["success"] else "N/A"
print(f" [{status}] {model.split('/')[-1]} ({latency})")
# Save raw results
with open(f"{SESSION_DIR}/phase1_responses.json", "w") as f:
json.dump(results, f, indent=2)
print(f"\nPhase 1 complete. Results saved to: {SESSION_DIR}/phase1_responses.json")
print(f"SESSION_DIR={SESSION_DIR}")
PYEOF
Each model reviews and ranks the anonymized responses from Phase 1:
SESSION_DIR="/tmp/llm-council/TIMESTAMP_HERE"
python3 << 'PYEOF'
import os
import json
import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
FIREWORKS_API_KEY = os.environ.get("FIREWORKS_API_KEY")
API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
SESSION_DIR = os.environ.get("SESSION_DIR")
# Load Phase 1 results
with open(f"{SESSION_DIR}/config.json") as f:
config = json.load(f)
with open(f"{SESSION_DIR}/phase1_responses.json") as f:
phase1_results = json.load(f)
QUERY = config["query"]
MODELS = config["models"]
# Create anonymized mapping
labels = ["A", "B", "C", "D", "E", "F", "G"][:len(MODELS)]
model_to_label = dict(zip(MODELS, labels))
label_to_model = {v: k for k, v in model_to_label.items()}
# Format anonymized responses
anonymized_responses = []
for model_id in MODELS:
label = model_to_label[model_id]
content = phase1_results[model_id]["content"]
anonymized_responses.append(f"=== Response {label} ===\n{content}")
anonymized_text = "\n\n".join(anonymized_responses)
def get_rankings(model_id, query, anonymized, own_label):
"""Get rankings from a single model"""
ranking_prompt = f"""You are evaluating responses from multiple AI models to this query:
QUERY: {query}
Here are the anonymized responses:
{anonymized}
Please rank these responses from BEST to WORST. For each ranking:
1. State the response letter (A, B, C, etc.)
2. Give a brief reason (1-2 sentences)
3. You may skip ranking your own response (labeled {own_label}) or rank it fairly
Format your response EXACTLY as:
RANKINGS:
1. [Letter] - [Brief reason]
2. [Letter] - [Brief reason]
3. [Letter] - [Brief reason]
..."""
try:
start = time.time()
response = requests.post(
API_URL,
headers={
"Authorization": f"Bearer {FIREWORKS_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": model_id,
"messages": [
{"role": "system", "content": f"You are ranking AI responses objectively. Your own response is labeled '{own_label}'."},
{"role": "user", "content": ranking_prompt}
],
"max_tokens": 1000,
"temperature": 1
},
timeout=90
)
response.raise_for_status()
elapsed = time.time() - start
return {
"success": True,
"content": response.json()["choices"][0]["message"]["content"],
"model": model_id,
"latency_seconds": round(elapsed, 2)
}
except Exception as e:
return {
"success": False,
"content": f"[ERROR: {str(e)}]",
"model": model_id,
"latency_seconds": 0
}
print(f"\n{'='*60}")
print("PHASE 2: Cross-Model Ranking")
print(f"{'='*60}")
print(f"Label mapping: {json.dumps({v: k.split('/')[-1] for k, v in model_to_label.items()})}")
print()
# Collect rankings from all models in parallel
rankings = {}
with ThreadPoolExecutor(max_workers=len(MODELS)) as executor:
futures = {
executor.submit(get_rankings, mid, QUERY, anonymized_text, model_to_label[mid]): mid
for mid in MODELS
}
for future in as_completed(futures):
model = futures[future]
result = future.result()
rankings[model] = result
status = "OK" if result["success"] else "FAILED"
latency = f"{result['latency_seconds']}s" if result["success"] else "N/A"
print(f" [{status}] {model.split('/')[-1]} ({latency})")
# Save rankings
output = {
"label_mapping": label_to_model,
"model_to_label": model_to_label,
"rankings": rankings
}
with open(f"{SESSION_DIR}/phase2_rankings.json", "w") as f:
json.dump(output, f, indent=2)
print(f"\nPhase 2 complete. Rankings saved to: {SESSION_DIR}/phase2_rankings.json")
PYEOF
The Chairman model receives all responses and rankings, then produces the final synthesis:
SESSION_DIR="/tmp/llm-council/TIMESTAMP_HERE"
CHAIRMAN_MODEL="accounts/fireworks/models/glm-5"
python3 << 'PYEOF'
import os
import json
import requests
import time
FIREWORKS_API_KEY = os.environ.get("FIREWORKS_API_KEY")
API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
SESSION_DIR = os.environ.get("SESSION_DIR")
CHAIRMAN_MODEL = os.environ.get("CHAIRMAN_MODEL")
# Load all previous results
with open(f"{SESSION_DIR}/config.json") as f:
config = json.load(f)
with open(f"{SESSION_DIR}/phase1_responses.json") as f:
phase1 = json.load(f)
with open(f"{SESSION_DIR}/phase2_rankings.json") as f:
phase2 = json.load(f)
QUERY = config["query"]
label_to_model = phase2["label_mapping"]
model_to_label = phase2["model_to_label"]
# Format responses with model names revealed
responses_text = []
for model_id, result in phase1.items():
label = model_to_label.get(model_id, "?")
model_name = model_id.split("/")[-1]
responses_text.append(f"=== {label}: {model_name} ===\n{result['content']}")
# Format rankings
rankings_text = []
for model_id, result in phase2["rankings"].items():
model_name = model_id.split("/")[-1]
rankings_text.append(f"[{model_name}'s Rankings]\n{result['content']}")
synthesis_prompt = f"""You are the Chairman of an LLM Council. Your task is to synthesize the best possible answer from multiple AI responses.
ORIGINAL QUERY:
{QUERY}
INDIVIDUAL RESPONSES:
{chr(10).join(responses_text)}
MODEL RANKINGS:
{chr(10).join(rankings_text)}
As Chairman, produce a FINAL SYNTHESIS that:
1. Incorporates the strongest elements from the best-ranked responses
2. Resolves any contradictions between responses
3. Addresses aspects that multiple models agreed on
4. Corrects any errors identified through cross-ranking
5. Provides the most complete, accurate, and helpful answer
Begin your synthesis:"""
print(f"\n{'='*60}")
print("PHASE 3: Chairman Synthesis")
print(f"{'='*60}")
print(f"Chairman: {CHAIRMAN_MODEL.split('/')[-1]}")
print()
try:
start = time.time()
response = requests.post(
API_URL,
headers={
"Authorization": f"Bearer {FIREWORKS_API_KEY}",
"Content-Type": "application/json"
},
json={
"model": CHAIRMAN_MODEL,
"messages": [
{"role": "system", "content": "You are the Chairman of an LLM Council. Synthesize multiple AI perspectives into a definitive, comprehensive response."},
{"role": "user", "content": synthesis_prompt}
],
"max_tokens": 4000,
"temperature": 1
},
timeout=180
)
response.raise_for_status()
elapsed = time.time() - start
synthesis = response.json()["choices"][0]["message"]["content"]
with open(f"{SESSION_DIR}/phase3_synthesis.txt", "w") as f:
f.write(synthesis)
print(f"Phase 3 complete ({elapsed:.2f}s). Synthesis saved to: {SESSION_DIR}/phase3_synthesis.txt")
except Exception as e:
print(f"ERROR: {e}")
synthesis = f"[ERROR: {str(e)}]"
with open(f"{SESSION_DIR}/phase3_synthesis.txt", "w") as f:
f.write(synthesis)
# Update config with chairman
config["chairman"] = CHAIRMAN_MODEL
with open(f"{SESSION_DIR}/config.json", "w") as f:
json.dump(config, f, indent=2)
PYEOF
Read all saved files and display the complete council deliberation:
SESSION_DIR="/tmp/llm-council/TIMESTAMP_HERE"
python3 << 'PYEOF'
import os
import json
SESSION_DIR = os.environ.get("SESSION_DIR")
# Load all data
with open(f"{SESSION_DIR}/config.json") as f:
config = json.load(f)
with open(f"{SESSION_DIR}/phase1_responses.json") as f:
phase1 = json.load(f)
with open(f"{SESSION_DIR}/phase2_rankings.json") as f:
phase2 = json.load(f)
with open(f"{SESSION_DIR}/phase3_synthesis.txt") as f:
synthesis = f.read()
model_to_label = phase2["model_to_label"]
label_to_model = phase2["label_mapping"]
# Build formatted output
output = []
output.append("=" * 70)
output.append(" LLM COUNCIL DELIBERATION")
output.append(" Powered by Fireworks AI")
output.append("=" * 70)
output.append("")
output.append(f"QUERY: {config['query']}")
output.append(f"COUNCIL: {', '.join([m.split('/')[-1] for m in config['models']])}")
output.append(f"CHAIRMAN: {config.get('chairman', 'N/A').split('/')[-1]}")
output.append("")
# Phase 1: Individual Responses
output.append("-" * 70)
output.append(" PHASE 1: INDIVIDUAL RESPONSES")
output.append("-" * 70)
output.append("")
for model_id, result in phase1.items():
model_name = model_id.split("/")[-1]
label = model_to_label.get(model_id, "?")
latency = result.get("latency_seconds", "N/A")
tokens = result.get("tokens", {})
output.append(f"[{label}] {model_name} (latency: {latency}s, tokens: {tokens.get('total', 'N/A')})")
output.append("-" * 40)
output.append(result["content"])
output.append("")
# Phase 2: Cross-Model Rankings
output.append("-" * 70)
output.append(" PHASE 2: CROSS-MODEL RANKINGS")
output.append("-" * 70)
output.append("")
output.append(f"Label mapping: {json.dumps({v: k.split('/')[-1] for k, v in model_to_label.items()}, indent=2)}")
output.append("")
for model_id, result in phase2["rankings"].items():
model_name = model_id.split("/")[-1]
output.append(f"[{model_name}'s Rankings]")
output.append(result["content"])
output.append("")
# Phase 3: Chairman Synthesis
output.append("-" * 70)
output.append(" PHASE 3: CHAIRMAN'S SYNTHESIS")
output.append("-" * 70)
output.append("")
chairman_name = config.get("chairman", "Chairman").split("/")[-1]
output.append(f"[{chairman_name} - Chairman]")
output.append("")
output.append(synthesis)
output.append("")
output.append("=" * 70)
output.append(f"Session files: {SESSION_DIR}/")
# Save formatted output
final_output = "\n".join(output)
with open(f"{SESSION_DIR}/final_output.md", "w") as f:
f.write(final_output)
print(final_output)
print(f"\nFull output saved to: {SESSION_DIR}/final_output.md")
PYEOF
/tmp/llm-council/{timestamp}/export FIREWORKS_API_KEY="your_api_key_here"
source ~/.zshrcاستنادا إلى تصنيف SOC المهني
