| name | consult-cloudflare |
| description | Consult any text-generation model on Cloudflare Workers AI (37+ models from Meta, OpenAI, Qwen, Mistral, Google, NVIDIA, Z-AI, IBM, DeepSeek, Moonshot). Use when the user says "/consult-cloudflare", "/consult-cf", "ask cloudflare", "cf workers ai", or names a CF-hosted model (llama-3.3-70b, gpt-oss-120b, nemotron-3-120b, qwen3-30b, glm-4.7-flash, etc.). Subcommands - list, run, models, search. |
consult-cloudflare
Query any Cloudflare Workers AI text-generation model via the REST API. Includes auto-discovery (list / search) so you never have to memorize model slugs.
When to use
- User says
/consult-cloudflare, /consult-cf, "ask cloudflare", "cf workers ai"
- User names a CF-hosted model:
llama-3.3-70b, gpt-oss-120b, nemotron-3-120b, qwen3-30b, glm-4.7-flash, mistral-small-24b, gemma-4-26b, etc.
- User wants a multi-model consensus call across CF models (use the
fan-out subcommand)
- User wants to discover what's on CF before picking a model (use
list or search)
Prerequisites
Two env vars (do NOT hardcode in commits):
export CF_API_TOKEN='cfut_...'
export CF_ACCOUNT_ID='3c1f...'
Verify the token works:
curl -s "https://api.cloudflare.com/client/v4/user/tokens/verify" -H "Authorization: Bearer $CF_API_TOKEN"
{"success":true,"result":{"status":"active"}} means good. If the token is scope-restricted (typical for cfut_ user tokens), /user, /accounts, /memberships will return 401 — that's expected, only /user/tokens/verify and /accounts/{id}/ai/* are accessible.
Subcommands
list — show all text-generation models
curl -s "https://api.cloudflare.com/client/v4/accounts/$CF_ACCOUNT_ID/ai/models/search?per_page=200" \
-H "Authorization: Bearer $CF_API_TOKEN" \
| python3 -c "
import sys, json
d = json.load(sys.stdin)
text_gen = [m for m in d['result'] if (m.get('task') or {}).get('name') == 'Text Generation']
for m in sorted(text_gen, key=lambda x: x['name']):
print(f\"{m['name']:60s} {m.get('description','')[:80]}\")
"
Current catalog (as of 2026-05-25 — 37 text-generation models):
| Family | Slug |
|---|
| Meta — Llama dense | @cf/meta/llama-3.3-70b-instruct-fp8-fast |
| Meta — Llama MoE | @cf/meta/llama-4-scout-17b-16e-instruct |
| Meta — smaller | @cf/meta/llama-3.1-8b-instruct-fp8, @cf/meta/llama-3.2-3b-instruct, @cf/meta/llama-3.2-1b-instruct, @cf/meta/llama-3-8b-instruct, @cf/meta/llama-3.2-11b-vision-instruct, @cf/meta/llama-guard-3-8b |
| NVIDIA | @cf/nvidia/nemotron-3-120b-a12b |
| OpenAI open weight | @cf/openai/gpt-oss-120b, @cf/openai/gpt-oss-20b |
| Qwen | @cf/qwen/qwen3-30b-a3b-fp8, @cf/qwen/qwq-32b, @cf/qwen/qwen2.5-coder-32b-instruct |
| DeepSeek | @cf/deepseek-ai/deepseek-r1-distill-qwen-32b |
| Moonshot | @cf/moonshotai/kimi-k2.6, @cf/moonshotai/kimi-k2.5 |
| Z-AI | @cf/zai-org/glm-4.7-flash |
| Google Gemma | @cf/google/gemma-3-12b-it, @cf/google/gemma-4-26b-a4b-it, @cf/google/gemma-2b-it-lora, @cf/google/gemma-7b-it-lora, @hf/google/gemma-7b-it |
| AISG | @cf/aisingapore/gemma-sea-lion-v4-27b-it |
| Mistral | @cf/mistralai/mistral-small-3.1-24b-instruct, @cf/mistral/mistral-7b-instruct-v0.1, @cf/mistral/mistral-7b-instruct-v0.2-lora, @hf/mistral/mistral-7b-instruct-v0.2 |
| IBM | @cf/ibm-granite/granite-4.0-h-micro |
| Microsoft | @cf/microsoft/phi-2 |
| Defog (SQL) | @cf/defog/sqlcoder-7b-2 |
| Nous | @hf/nousresearch/hermes-2-pro-mistral-7b |
| Meta-Llama LoRA | @cf/meta-llama/llama-2-7b-chat-hf-lora, @cf/meta/llama-2-7b-chat-fp16, @cf/meta/llama-2-7b-chat-int8, @cf/meta/llama-3.1-8b-instruct-awq, @cf/meta/llama-3-8b-instruct-awq |
Always re-run the list command to get the live catalog — the table above goes stale.
search <pattern> — filter the catalog
curl -s "https://api.cloudflare.com/client/v4/accounts/$CF_ACCOUNT_ID/ai/models/search?search=<pattern>" \
-H "Authorization: Bearer $CF_API_TOKEN" \
| python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['result']]"
Example: search=llama → all 13 Llama variants. search=120b → the two largest (gpt-oss-120b, nemotron-3-120b).
run <model> <prompt> — single call (non-streaming)
MODEL='@cf/meta/llama-3.3-70b-instruct-fp8-fast'
PROMPT='Your prompt here'
curl -s "https://api.cloudflare.com/client/v4/accounts/$CF_ACCOUNT_ID/ai/run/$MODEL" \
-H "Authorization: Bearer $CF_API_TOKEN" \
-H "Content-Type: application/json" \
-d "$(python3 -c "import json,sys; print(json.dumps({'messages':[{'role':'user','content':sys.argv[1]}],'max_tokens':4096,'temperature':0.4}))" "$PROMPT")" \
| python3 -c "import sys,json; r=json.load(sys.stdin); print(r['result']['response'] if r.get('success') else r['errors'])"
Python equivalent (cleaner for multi-turn or non-trivial payloads):
import os, json, requests
acct, tok = os.environ["CF_ACCOUNT_ID"], os.environ["CF_API_TOKEN"]
model = "@cf/openai/gpt-oss-120b"
url = f"https://api.cloudflare.com/client/v4/accounts/{acct}/ai/run/{model}"
payload = {
"messages": [{"role": "user", "content": "Your prompt"}],
"max_tokens": 4096,
"temperature": 0.4,
}
r = requests.post(url, headers={"Authorization": f"Bearer {tok}"}, json=payload, timeout=180)
data = r.json()
print(data["result"]["response"] if data.get("success") else data["errors"])
run --stream — SSE streaming
Workers AI returns Server-Sent Events when stream: true:
r = requests.post(url, headers={"Authorization": f"Bearer {tok}"},
json={**payload, "stream": True}, stream=True, timeout=180)
for line in r.iter_lines():
if not line: continue
ln = line.decode("utf-8")
if not ln.startswith("data: "): continue
data = ln[6:].strip()
if data == "[DONE]": break
obj = json.loads(data)
piece = obj.get("response", "")
if piece: print(piece, end="", flush=True)
Important shape gotcha: Workers AI streaming chunks are {"response": "<token>"}, NOT the OpenAI {"choices":[{"delta":{"content":"..."}}]} shape. Don't reuse OpenAI parser code without adjusting.
fan-out <prompt> [--models slug1,slug2,...] — multi-model consensus
For getting a cross-model second opinion (e.g. on a quant scoring formula). Default panel: llama-3.3-70b, gpt-oss-120b, nemotron-3-120b, qwen3-30b, glm-4.7-flash, mistral-small-24b. Run them sequentially or in a thread-pool; collect all responses; write to a single markdown report.
Reference implementation: tools/swarm/cf_fanout.py (create with this skill — see template below).
import os, json, sys, time, requests
ACCT, TOK = os.environ["CF_ACCOUNT_ID"], os.environ["CF_API_TOKEN"]
DEFAULT_PANEL = [
"@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"@cf/meta/llama-4-scout-17b-16e-instruct",
"@cf/nvidia/nemotron-3-120b-a12b",
"@cf/openai/gpt-oss-120b",
"@cf/qwen/qwen3-30b-a3b-fp8",
"@cf/zai-org/glm-4.7-flash",
"@cf/mistralai/mistral-small-3.1-24b-instruct",
]
def call(model, prompt, history=None):
url = f"https://api.cloudflare.com/client/v4/accounts/{ACCT}/ai/run/{model}"
messages = (history or []) + [{"role":"user","content":prompt}]
r = requests.post(url, headers={"Authorization": f"Bearer {TOK}"},
json={"messages":messages, "max_tokens":4096, "temperature":0.4},
timeout=240)
data = r.json()
return data["result"]["response"] if data.get("success") else f"[ERROR {data.get('errors')}]"
if __name__ == "__main__":
prompt = sys.stdin.read()
models = sys.argv[1].split(",") if len(sys.argv) > 1 else DEFAULT_PANEL
for m in models:
t0 = time.time()
out = call(m, prompt)
print(f"\n=== {m} ({time.time()-t0:.1f}s, {len(out)} chars) ===\n{out}\n")
Usage:
echo "Give me a stock-prediction-algorithm scoring formula in 200 words" | \
python3 tools/swarm/cf_fanout.py \
'@cf/meta/llama-3.3-70b-instruct-fp8-fast,@cf/openai/gpt-oss-120b,@cf/qwen/qwen3-30b-a3b-fp8'
Common pitfalls
| Symptom | Cause | Fix |
|---|
{"errors":[{"code":10000,"message":"Authentication error"}]} on /accounts or /user | cfut_ token is scope-restricted to Workers AI only | Don't try to introspect — use the token directly against /accounts/{id}/ai/run/{model} |
403 Forbidden on a specific model | model requires "AI Models — Run" permission AND has billing implications | Verify the token includes "Workers AI Read+Run" scope at dash.cloudflare.com/profile/api-tokens |
| Streaming returns nothing | parser expects OpenAI shape | Workers AI SSE chunks are {"response": "..."} — see streaming section above |
model not found | typo in slug — qwen3-max is NOT on CF (closest = qwen3-30b-a3b-fp8 + qwq-32b) | Run list first to confirm available slugs |
| Response truncated mid-sentence | max_tokens floor too low | Bump to 4096-8192 (CF default is 256!) |
Notes
- Workers AI is OpenAI-compatible-ish at the message-shape level (
{role, content}) but NOT at the response-shape level: the JSON envelope is {success: true, result: {response: "..."}} not {choices: [...]}.
- All slugs start with
@cf/<vendor>/<model> (Cloudflare-managed) or @hf/<vendor>/<model> (HuggingFace-mirrored).
- Token in this repo: see
/home/eaguiar2015/.bashrc or ask user for CF_API_TOKEN. Account ID 3c1f2867a7120334ef745ceb9345d9e6 (host: zerounderscore@gmail.com).
- For non-CF NVIDIA models (DeepSeek-R1-distill, Kimi-k2.6, MiniMax-m2.7) use
/consult-nvidia-deepseek — different endpoint, different model catalog.
- 🔴 Grounding: For asset-class / edge / pick-performance prompts, include the leakage-context block from
consult-cloudflare-models/SKILL.md or consult-nvidia-models/SKILL.md. The worker_runner.py now auto-injects hypothesis_registry.json rejected entries into all swarm prompts, but manual consults still need the block prepended. See reports/2026-05-25_multi_ai_panel_meta_review.md for why this matters.
Reference
