AllToken — Slash Commands

Six slash-style AllToken commands — /alltoken-chat, /alltoken-image, /alltoken-video, /alltoken-search, /alltoken-models, /alltoken-cost — recognized in chat and run via stdlib Python recipes. Pair with alltoken for full project bootstrap.

Audits

Pass

ClawScanPass

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install alltoken-call

/alltoken-call — direct AllToken commands

This skill teaches the host agent to recognize six slash-style invocations in user prompts and run the corresponding recipe against the user's AllToken API key. Each command is a self-contained Python 3 stdlib script — no external SDK install required.

When to invoke this skill (vs `alltoken`)

User wants…	Use
"Generate an image of X" / "Translate this with Claude" / "Show available models"	This skill (`alltoken-call`)
"Build me an AllToken agent" / "Scaffold a chat app" / "Add AllToken to my project"	`alltoken` instead
Both	Load both — they don't conflict

Prerequisites

ALLTOKEN_API_KEY exported in the environment the agent shells out to
python3 ≥ 3.10 available on PATH
Internet access from the agent's host

The agent should refuse to invoke any command in this skill if ALLTOKEN_API_KEY is unset, and prompt the user to set it first.

Trigger recognition

Match these patterns case-insensitively anywhere in the user's message (a leading / is canonical but not required):

Trigger	Command
`/alltoken-chat`, `alltoken chat`, "ask alltoken with model X"	Command 1
`/alltoken-image`, `alltoken image`, "generate an image via alltoken", "draw with alltoken"	Command 2
`/alltoken-video`, `alltoken video`, "make a video on alltoken"	Command 3
`/alltoken-search`, `alltoken search`, "use alltoken web search", "find with alltoken"	Command 4
`/alltoken-models`, `alltoken models`, "what alltoken models are available"	Command 5
`/alltoken-cost`, `alltoken cost`, "how much did that cost" (when last response was an AllToken call)	Command 6

Production constraints baked into the recipes

These were verified live on 2026-05-12 — they're not optional:

Image results are one-shot. Persist b64_json on the first completed read; re-polling returns 410 image_already_retrieved.
enable_search: true only works on DeepSeek and Qwen. OpenAI returns 503, Claude/GLM/Kimi/Minimax silently drop it. The /alltoken-search recipe defaults to deepseek-v4-pro for this reason.
Streaming usage requires opt-in. Add stream_options: {"include_usage": True} or it'll be null.
The API key reaches /v1/* only. /api-account/user/balance, /usage, /billing return 401 with the Bearer token — they need a web session. Do not attempt them.
Errors are envelope-shaped: {"error": {"code": "<slug>", "type": "<group>", "message": "...", "param": null, "request_id": "..."}}. Always surface code and request_id to the user on failure.

1. `/alltoken-chat`

Syntax

/alltoken-chat <model> <prompt>
/alltoken-chat <prompt>                       # uses default: gpt-5.4-mini

Parameters

model (optional) — any ID from /v1/models. Common choices: gpt-5.4-mini, gpt-5.4, claude-sonnet-4-6, claude-opus-4-7, deepseek-v4-pro, gemini-3.1-pro-preview. Cheap defaults: gpt-5.4-nano, claude-haiku-4-5, gemini-3-flash-preview.
prompt (required) — free-form text.

Recipe — save as /tmp/at_chat.py, run with python3 /tmp/at_chat.py <model> <prompt...>:

import os, sys, json, urllib.request
args = sys.argv[1:]
if not args:
    print("usage: at_chat.py [model] <prompt...>"); sys.exit(2)
# Heuristic: first arg is model only if it has no spaces AND looks like an ID
first = args[0]
if len(args) >= 2 and "-" in first and " " not in first and "." in first or first.startswith(("gpt-","claude-","gemini-","deepseek-","glm-","qwen","kimi-","minimax-")):
    model, prompt = first, " ".join(args[1:])
else:
    model, prompt = "gpt-5.4-mini", " ".join(args)
body = json.dumps({
    "model": model,
    "messages": [{"role":"user","content": prompt}],
    "stream": True,
    "stream_options": {"include_usage": True},
}).encode()
req = urllib.request.Request("https://api.alltoken.ai/v1/chat/completions",
    data=body, method="POST",
    headers={"Authorization": f"Bearer {os.environ['ALLTOKEN_API_KEY']}", "Content-Type":"application/json"})
try:
    r = urllib.request.urlopen(req, timeout=120)
except urllib.error.HTTPError as e:
    err = json.loads(e.read()).get("error", {})
    print(f"\n[error {e.code}] {err.get('code')}/{err.get('type')}: {err.get('message')}  req={err.get('request_id')}")
    sys.exit(1)
usage = None
for raw in iter(r.readline, b""):
    line = raw.decode("utf-8","replace").rstrip("\n")
    if not line or line.startswith(":"): continue
    if line.startswith("data: "):
        data = line[6:]
        if data == "[DONE]": break
        obj = json.loads(data)
        if obj.get("usage"): usage = obj["usage"]
        for ch in obj.get("choices", []):
            c = ch.get("delta", {}).get("content")
            if c: sys.stdout.write(c); sys.stdout.flush()
print()
if usage:
    print(f"\n[usage] prompt={usage['prompt_tokens']} completion={usage['completion_tokens']} total={usage['total_tokens']} model={model}")

Agent presentation — after running, show the streamed text to the user and, in a separate line, surface the token usage (prompt + completion = total). If the user follows up with /alltoken-cost, that line is what gets multiplied by per-token prices.

2. `/alltoken-image`

Syntax

/alltoken-image <prompt> [--size=1024x1024] [--quality=low|medium|high] [--out=PATH]

Parameters

prompt (required)
--size — 1024x1024 (default), 1536x1024, 1024x1536, auto
--quality — low (default for speed), medium, high, auto
--out — output path (default: ./alltoken-image-<8charhex>.png in cwd)

Recipe — save as /tmp/at_image.py:

import os, sys, json, time, base64, uuid, argparse, urllib.request
ap = argparse.ArgumentParser()
ap.add_argument("prompt", nargs="+")
ap.add_argument("--size", default="1024x1024")
ap.add_argument("--quality", default="low", choices=["low","medium","high","auto"])
ap.add_argument("--out", default=None)
a = ap.parse_args()
prompt = " ".join(a.prompt)
out = a.out or f"alltoken-image-{uuid.uuid4().hex[:8]}.png"
H = {"Authorization": f"Bearer {os.environ['ALLTOKEN_API_KEY']}", "Content-Type":"application/json"}
body = json.dumps({"model":"gpt-image-2","prompt":prompt,"size":a.size,"quality":a.quality}).encode()
req = urllib.request.Request("https://api.alltoken.ai/v1/images/generations/async",
    data=body, method="POST", headers={**H, "Idempotency-Key": str(uuid.uuid4())})
try:
    created = json.loads(urllib.request.urlopen(req, timeout=60).read())
except urllib.error.HTTPError as e:
    print(f"[error] submit failed: {e.code} {e.read().decode()[:300]}"); sys.exit(1)
task_id = created["id"]; t0 = time.time()
print(f"submitted {task_id} (size={a.size} quality={a.quality})", flush=True)
while True:
    time.sleep(2)
    req = urllib.request.Request(f"https://api.alltoken.ai/v1/images/generations/{task_id}", headers=H)
    s = json.loads(urllib.request.urlopen(req, timeout=30).read())
    print(f"  [{time.time()-t0:.0f}s] {s['status']}", flush=True)
    if s["status"] == "completed":
        # ONE-SHOT: write immediately, never re-poll
        with open(out, "wb") as f: f.write(base64.b64decode(s["data"][0]["b64_json"]))
        print(f"saved {out} ({os.path.getsize(out)} bytes) in {time.time()-t0:.1f}s")
        u = s.get("usage", {}); print(f"[usage] input={u.get('input_tokens')} output={u.get('output_tokens')} total={u.get('total_tokens')}")
        break
    if s["status"] in ("failed","cancelled"):
        print(f"[error] task ended: {s.get('error')}"); sys.exit(1)

Agent presentation — confirm the file path, embed/preview the image if the host supports it, and warn the user that the result is gone from the server after retrieval. If the user asks for a variation, run a new /alltoken-image rather than re-polling the old task.

3. `/alltoken-video`

Syntax

/alltoken-video <prompt> [--model=seedance-1.5-pro] [--duration=5] [--ratio=16:9] [--resolution=480p|720p|1080p]

Parameters

prompt (required)
--model — seedance-1.5-pro (default), seedance-2.0, happyhorse-1.0-t2v, happyhorse-1.0-i2v. Check /v1/videos/models for the full list.
--duration — seconds, default 5
--ratio — 16:9 (default), 9:16, 4:3, 3:4, 21:9, 1:1, adaptive
--resolution — 480p (default), 720p, 1080p

Recipe — save as /tmp/at_video.py:

import os, sys, json, time, argparse, urllib.request
ap = argparse.ArgumentParser()
ap.add_argument("prompt", nargs="+")
ap.add_argument("--model", default="seedance-1.5-pro")
ap.add_argument("--duration", type=int, default=5)
ap.add_argument("--ratio", default="16:9")
ap.add_argument("--resolution", default="480p", choices=["480p","720p","1080p"])
a = ap.parse_args()
H = {"Authorization": f"Bearer {os.environ['ALLTOKEN_API_KEY']}", "Content-Type":"application/json"}
body = json.dumps({"model":a.model,"prompt":" ".join(a.prompt),"duration":a.duration,"ratio":a.ratio,"resolution":a.resolution}).encode()
req = urllib.request.Request("https://api.alltoken.ai/v1/videos/generations", data=body, method="POST", headers=H)
try:
    created = json.loads(urllib.request.urlopen(req, timeout=60).read())
except urllib.error.HTTPError as e:
    print(f"[error] {e.code} {e.read().decode()[:300]}"); sys.exit(1)
vid = created["id"]; t0 = time.time()
print(f"submitted {vid}", flush=True)
while True:
    time.sleep(3)
    req = urllib.request.Request(f"https://api.alltoken.ai/v1/videos/generations/{vid}", headers=H)
    s = json.loads(urllib.request.urlopen(req, timeout=30).read())
    print(f"  [{time.time()-t0:.0f}s] {s['status']}", flush=True)
    if s["status"] == "completed":
        url = s.get("video_url")
        ttl = s.get("video_url_ttl", "?")
        print(f"video_url ({ttl}s TTL): {url}")
        print(f"resolution={s.get('resolution')} ratio={s.get('ratio')} fps={s.get('fps')}")
        break
    if s["status"] in ("failed","cancelled","expired"):
        print(f"[error] task ended: {s.get('error')}"); sys.exit(1)

Agent presentation — give the video_url (presigned, expires in video_url_ttl seconds) and remind the user to download promptly. If they want to cancel mid-generation, POST /v1/videos/generations/{id}/cancel.

4. `/alltoken-search`

Syntax

/alltoken-search <query>
/alltoken-search --model=qwen3.6-flash <query>

Parameters

query (required) — natural-language question
--model — defaults to deepseek-v4-pro. Only DeepSeek and Qwen models honor enable_search:true; choose one of: deepseek-v4-pro, deepseek-v3.2, qwen3.6-flash, qwen3.6-max-preview. If you pass any other model the recipe will refuse and tell the user why.

Recipe — save as /tmp/at_search.py:

import os, sys, json, argparse, urllib.request
SEARCH_OK = {"deepseek-v4-pro","deepseek-v3.2","qwen3.6-flash","qwen3.6-max-preview","qwen3.6-plus","qwen3.6-27b"}
ap = argparse.ArgumentParser()
ap.add_argument("query", nargs="+")
ap.add_argument("--model", default="deepseek-v4-pro")
a = ap.parse_args()
if a.model not in SEARCH_OK:
    print(f"[refuse] {a.model} does NOT honor enable_search on AllToken today.")
    print(f"        Use one of: {', '.join(sorted(SEARCH_OK))}")
    sys.exit(2)
body = json.dumps({
    "model": a.model,
    "messages": [{"role":"user","content":" ".join(a.query)}],
    "enable_search": True,
    "max_tokens": 600,
}).encode()
req = urllib.request.Request("https://api.alltoken.ai/v1/chat/completions",
    data=body, method="POST",
    headers={"Authorization": f"Bearer {os.environ['ALLTOKEN_API_KEY']}", "Content-Type":"application/json"})
try:
    r = urllib.request.urlopen(req, timeout=120)
except urllib.error.HTTPError as e:
    err = json.loads(e.read()).get("error", {})
    print(f"[error {e.code}] {err.get('code')}/{err.get('type')}: {err.get('message')}  req={err.get('request_id')}"); sys.exit(1)
j = json.loads(r.read())
m = j["choices"][0]["message"]
print(m.get("content","").strip())
u = j.get("usage", {})
print(f"\n[usage] prompt={u.get('prompt_tokens')} completion={u.get('completion_tokens')} total={u.get('total_tokens')} model={a.model}")

Agent presentation — the response should look like a search-grounded answer (current dates, specific numbers). If the model says "I don't have web search" anyway, the family is honoring the flag but the underlying provider failed — re-run with a different model.

5. `/alltoken-models`

Syntax

/alltoken-models
/alltoken-models --type=chat        # chat (default), image, video
/alltoken-models --filter=claude    # substring filter on ID

Recipe — save as /tmp/at_models.py:

import os, sys, json, argparse, urllib.request
ap = argparse.ArgumentParser()
ap.add_argument("--type", default="chat", choices=["chat","image","video"])
ap.add_argument("--filter", default="")
a = ap.parse_args()
path = {"chat":"/v1/models","image":"/v1/images/models","video":"/v1/videos/models"}[a.type]
req = urllib.request.Request(f"https://api.alltoken.ai{path}",
    headers={"Authorization": f"Bearer {os.environ['ALLTOKEN_API_KEY']}"})
j = json.loads(urllib.request.urlopen(req, timeout=30).read())
ids = [m["id"] for m in j["data"]]
if a.filter: ids = [i for i in ids if a.filter.lower() in i.lower()]
print(f"{a.type}: {len(ids)} model(s)" + (f" matching '{a.filter}'" if a.filter else ""))
for i in ids: print(f"  {i}")

Agent presentation — list IDs in a code block. If the user asks "which is cheapest / fastest / best for code", cross-reference the verified-working table in alltoken/SKILL.md ## Discovering models.

6. `/alltoken-cost`

Compute the cost of a chat call from its usage block + per-million prices from the catalog. Useful right after /alltoken-chat or /alltoken-search.

Syntax

/alltoken-cost <model> <prompt_tokens> <completion_tokens>

Recipe — save as /tmp/at_cost.py:

import os, sys, json, urllib.request
if len(sys.argv) != 4:
    print("usage: at_cost.py <model> <prompt_tokens> <completion_tokens>"); sys.exit(2)
model, pt, ct = sys.argv[1], int(sys.argv[2]), int(sys.argv[3])
# Public catalog — no auth required
req = urllib.request.Request(f"https://api.alltoken.ai/api-account/models/{model}")
try:
    j = json.loads(urllib.request.urlopen(req, timeout=30).read())
except urllib.error.HTTPError as e:
    print(f"[error] catalog lookup failed: {e.code}"); sys.exit(1)
# Pricing fields vary; try common keys
data = j.get("data", j)
p_in  = float(data.get("input_price")  or data.get("prompt_price")     or 0)
p_out = float(data.get("output_price") or data.get("completion_price") or 0)
# Convention: prices are per 1M tokens
cost = (pt / 1_000_000) * p_in + (ct / 1_000_000) * p_out
print(f"model:            {model}")
print(f"prompt tokens:    {pt:>10,}")
print(f"completion tokens:{ct:>10,}")
print(f"input  $/1M:      ${p_in:.4f}")
print(f"output $/1M:      ${p_out:.4f}")
print(f"───")
print(f"total cost:       ${cost:.6f}")

Agent presentation — surface the total in $ to 6 decimal places; for streaming chat, prefer reading the exact figure from the after-[DONE] SSE comment line (which is the authoritative per-request cost from AllToken's gateway). Use this recipe as a fallback when that comment line wasn't captured.

Tip: pricing fields in the catalog response have evolved — if input_price/output_price aren't populated, fall back to inspecting data keys: curl https://api.alltoken.ai/api-account/models/<model> | jq 'keys'.

Error handling (shared across all commands)

When any recipe hits a non-2xx response, the AllToken envelope is {"error": {"code", "type", "message", "param", "request_id"}}. The agent should:

Show the user code and message.
Include request_id in any support communication.
For specific slugs, take action without prompting:
- invalid_api_key (401): tell the user the key is bad; do not retry.
- image_already_retrieved (410): tell the user to re-run; the result is gone.
- all_providers_failed (503): try a different model from the same family, then a different family.
- rate_limited / HTTP 429: read Retry-After (integer seconds), sleep, retry once.
- insufficient_balance (402): tell the user to top up in Settings → Billing.

Companion skill

If the user wants to build something instead of one-shot calls, hand off to alltoken:

"If you want me to scaffold a whole agent project around this, load skills/alltoken/SKILL.md."

Resources

AllToken docs: https://alltoken.ai/docs/apis/overview
Live model list: GET https://api.alltoken.ai/v1/models (Bearer auth)
Public catalog: GET https://api.alltoken.ai/api-account/models (no auth)
Companion bootstrap skill: ../alltoken/SKILL.md
Companion usage manual: ../alltoken/USAGE.md

AllToken — Slash Commands

Audits

Install

/alltoken-call — direct AllToken commands

When to invoke this skill (vs alltoken)

Prerequisites

Trigger recognition

Production constraints baked into the recipes

1. /alltoken-chat

2. /alltoken-image

3. /alltoken-video

4. /alltoken-search

5. /alltoken-models

6. /alltoken-cost

Error handling (shared across all commands)

Companion skill

Resources

When to invoke this skill (vs `alltoken`)

1. `/alltoken-chat`

2. `/alltoken-image`

3. `/alltoken-video`

4. `/alltoken-search`

5. `/alltoken-models`

6. `/alltoken-cost`