consult-cloudflare

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_...'   # Cloudflare API token with Workers AI scope
export CF_ACCOUNT_ID='3c1f...'   # 32-char hex account id (from dash.cloudflare.com right sidebar)

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"  # any slug from `list`
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,
    # "stream": True,  # SSE; see `stream` section below
}
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)
    # Workers AI streams "{response: '...token...'}" per chunk (NOT OpenAI-shape)
    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).

# tools/swarm/cf_fanout.py
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: [e-mail]).
• 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

• CF Workers AI catalog (live): https://api.cloudflare.com/client/v4/accounts/{acct}/ai/models/search?per_page=200
• CF Workers AI REST docs: https://developers.cloudflare.com/workers-ai/get-started/rest-api/
• Companion skills: /consult-nvidia-deepseek, /consult-ring, /consult-grok, /swarm-run (multi-engine fan-out)