VMake

Process image or video watermark removal and quality restoration with Vmake AI. Always discover the remote catalog before naming or selecting a task: V2 uses the returned server task metadata; legacy aliases are allowed only when the legacy config explicitly returns them. Video tasks use spawn-run-t

Vmake Skill

When to Use This Skill

Activate when the user wants any of the following:

• Watermark removal on images or video (remove watermark, eraser watermark, etc.)
• Image quality restoration (restore, upscale, enhance, super-resolution)
• Video quality restoration (video restore, upscale, hdvideo-style enhancement)

Billing and user-facing claims (MANDATORY)

• Fact: Each successful run-task (including inside a sessions_spawn worker) goes through server-side quota / credit consumption for the MT_AK tenant. This is a paid, metered commercial API, not free compute bundled with the skill or the host.
• Forbidden: Do not state or imply that the service is free, costs nothing, uses no quota, has unlimited trial, or similar. Do not invent prices, plan names, promotions, or trial rules.
• Allowed: Neutral wording — e.g. processing uses the Vmake account quota tied to the configured keys; billing and plans are per your console or administrator. If the user asks about cost, point them to admin / official billing docs / console; do not guess. When the API returns quota or membership errors, follow Step 3 — MANDATORY (quota / consume failures) using server detail and pricing_url when present.
• On success too: Success summaries must stay factual (task completed, delivery). Do not add “free” or zero-cost implications.

Execution authorization and attachment reuse (MANDATORY)

• A media attachment already present in the conversation is the current input. Do not ask the user to upload it again, restate its media type, or provide its file location when that information is already available.
• Before the first upload to Vmake, quota consumption, or task submission, ask for one concise authorization that covers both uploading the current media and using the configured Vmake account quota.
• If the user's current request already explicitly authorizes both the upload and quota use, treat that as authorization and do not ask again.
• After the user authorizes the operation, immediately reuse the current attachment and continue through input resolution, upload, quota consumption, task submission, polling, and delivery. Do not ask for another confirmation merely because execution continues in another turn or worker session.
• Refer to chat media as “received” before authorization. Do not say that the media “has not been uploaded” without clarifying that it has not yet been sent to Vmake.
• Ask for the media again only when the attachment is genuinely unavailable to the execution environment.
• Authorization applies to the current media and selected single task. For a multi-stage pipeline, disclose the number of quota-consuming stages and request authorization for the complete pipeline.

Agent metadata (v2)

For catalog, run-task, and spawn-run-task, make a best effort to provide --agent-name, --agent-version, and --agent-model from information you actually know about your own runtime. The name is the host (e.g. codex, claude-code, openclaw); version is the host software version, not the skill version. Use only explicit self/runtime information: never guess, fabricate, derive a host from its model, or probe for unavailable details. If a value is not known with confidence, omit it.

Explicit arguments override SKILL_AGENT_NAME, SKILL_AGENT_VERSION, SKILL_AGENT_MODEL; an explicit empty value suppresses that field. Metadata is optional and sent only to WAPI as X-Agent-Name, X-Agent-Version, X-Agent-Model before signing. The server stores it for effective v2 consume records. spawn-run-task preserves the caller's metadata in its worker command; the model field describes the initiating caller, not a discovered worker model.

Capabilities and remote task discovery

The local skill defines capabilities, not authoritative executable IDs:

Capability	Input
Watermark / subtitle removal	Image or video
Quality restoration / enhancement	Image or video

Before naming a task to the user, asking for execution authorization, uploading media, or building a worker, run the read-only catalog command:

bash

python3 {baseDir}/scripts/vmake_ai.py install-deps
python3 {baseDir}/scripts/vmake_ai.py catalog [--media-type image|video]

catalog only calls /skill/config.json; it does not upload media, call /skill/consume.json, or submit an algorithm task.

Dual-stack selection (MANDATORY)

1. V2 first: When config_mode is v2, use the returned description, name, and media_type to select the task, then pass that entry's exact returned material_id as the CLI --task value. Never invent or substitute a local/legacy algorithm name. A unique display name remains accepted only for backward compatibility; use material_id for new calls so duplicate names remain unambiguous.
2. Legacy compatibility: When config_mode is legacy, an alias such as videoscreenclear may be used only if that exact alias appears in the command's returned tasks. Absence means unavailable; local documentation or memory is not evidence.
3. Failure stops the flow: If catalog exits non-zero, returns config_unavailable, config_catalog_empty, or has no matching tasks, tell the user that no matching Vmake task is currently available, then stop. Include any server notice messages in that reply. If those notices provide no next step, advise the user to contact Vmake official support without inventing a support URL or contact method. Do not infer a local task, request media for execution, upload, consume quota, or submit an algorithm job.
4. Execution revalidates: run-task and spawn-run-task fetch config again and reject tasks not advertised by that response before upload or consume.

For a V2 task selection, present the returned display fields localized to the conversation language. For example:

text

Task: Subtitle
- Capability: Video subtitle removal
- Input type: Video

Video tasks — default execution

For a selected V2 entry with media_type: "video", or a server-confirmed legacy video alias: spawn-run-task → pass sessions_spawn_args to sessions_spawn (main session does not block on run-task). Command shape, runTimeoutSeconds (default 3600), polling and recovery: §3b and docs/errors-and-polling.md.

---

Multi-stage pipelines (chaining tasks)

When the user asks for more than one Vmake step on the same media (e.g. remove watermark then restore quality), treat each step as a separate job:

Typical chain	Stages
Image	catalog-selected watermark task name → catalog-selected restoration task name
Video	catalog-selected watermark task name → catalog-selected restoration task name

Rules:

1. After stage A completes with skill_status: "completed", use primary_result_url or output_urls[0] as --input for stage B with a new --task. That is a new job, not a retry of stage A. For video, stage B means a new spawn-run-task + sessions_spawn (each spawn embeds a single run-task), not a second run-task inside the same embed.
2. “Do not re-run run-task” in this skill means: do not submit run-task again for the same task_id / the same submitted job (use query-task to resume polling instead). It does not forbid the next pipeline stage with a different catalog-selected task name (or confirmed legacy alias) and the previous result URL as input.
3. Step 4 (delivery): Prefer final-stage native delivery when the user wanted the full pipeline; intermediate stages may still run embedded Step 4 per worker (one spawn per video stage) — tune the user-facing copy if they only care about the last asset.
4. Video chains (medeo-style): One sessions_spawn = one embedded run-task. Do not put two run-task calls in one spawn. Chain = multiple spawns: after stage A, read primary_result_url from stdout or last-task / history, then spawn-run-task for stage B with that URL as --input. No video run-task in the main session. Optional one-line user update before the second spawn.

See also Step 3 success bullets and agent_instruction in the JSON.

---

API submission path (MANDATORY)

• Discovery: Fetch task choices only via python3 {baseDir}/scripts/vmake_ai.py catalog …. Production WAPI is wapi-skill.vmake.ai. Do not hand-craft config HTTP.
• New jobs: Submit only via python3 {baseDir}/scripts/vmake_ai.py run-task … (§3a / §3b), or the same run-task command embedded in spawn-run-task → sessions_spawn. Do not hand-craft HTTP to WAPI or AIGC / invoke endpoints to replace that flow — that skips POST /skill/consume.json (quota and permission) and breaks the supported pipeline.
• Exception: query-task --task-id is only for resuming status polling on an existing full task_id (no upload, no second consume). Do not use it instead of run-task for a new submission.
• No curl replay: This skill does not emit debug curl for API calls. Do not hand-craft HTTP to wapi / AIGC to mimic requests — always use the CLI above so /skill/consume.json runs before algorithm submit.

---

0. Pre-Flight Check (MANDATORY — run before anything else)

Verify AK/SK are configured (only run this command; do not read other Python sources first):

bash

python3 {baseDir}/scripts/vmake_ai.py preflight

• Output ok → continue to Step 1
• Output missing → stop and send the user the configuration message below

Feishu — send an interactive card via the Feishu API (do not use the message tool for this):

python

import json, urllib.request
cfg = json.loads(open("/home/ec2-user/.openclaw/openclaw.json").read())
feishu = cfg["channels"]["feishu"]["accounts"]["default"]
token = json.loads(urllib.request.urlopen(urllib.request.Request(
    "https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal",
    data=json.dumps({"app_id": feishu["appId"], "app_secret": feishu["appSecret"]}).encode(),
    headers={"Content-Type": "application/json"}
)).read())["tenant_access_token"]
card = {
    "config": {"wide_screen_mode": True},
    "header": {"title": {"tag": "plain_text", "content": "🖼️ Vmake — credentials required"}, "template": "blue"},
    "elements": [{"tag": "div", "text": {"tag": "lark_md", "content": "1. Apply for **Access Key** and **Secret Key** at [Vmake Developers — API Key](https://vmake.ai/developers#api-key).\n2. Set **MT_AK** and **MT_SK** in `scripts/.env` (see `scripts/.env.example`), then reload env:\n```\nsource scripts/.env\n```\nIf keys are issued by your organization, ask your administrator."}}],
}
urllib.request.urlopen(urllib.request.Request(
    "https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id",
    data=json.dumps({"receive_id": "<USER_OPEN_ID>", "msg_type": "interactive", "content": json.dumps(card)}).encode(),
    headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
))

Telegram / Discord / other channels — use the message tool with plain text:

text

🖼️ Vmake — credentials required

1. Get Access Key and Secret Key (apply here if needed):
   https://vmake.ai/developers#api-key

2. Set MT_AK and MT_SK in scripts/.env (see scripts/.env.example), then run:
   source scripts/.env

If keys are issued by your organization, ask your administrator.

---

Step 1 — Discover task and resolve the current input

1. Run install-deps, then catalog. If the user or attachment already establishes the modality, filter with --media-type image or --media-type video; otherwise fetch all entries and ask one short image-or-video question.
2. In V2 mode, match the request against the server's description, name, and media_type, then pass the selected entry's exact returned material_id as --task. In user-visible replies, show the task name, capability description, and localized input type; do not show material_id unless debugging requires it. Do not invent or supplement missing catalog descriptions locally.
3. In legacy mode, map the request to a legacy alias only after confirming that exact alias appears in catalog output. Never report or execute a local legacy name before this confirmation.
4. Resolve the current input from the existing attachment, path, or URL. Infer the media type from attachment metadata or the file extension when available. Do not ask the user to repeat either one. Video extensions include .mp4, .mov, .webm, .mkv, .m4v; common static image extensions include .jpg, .jpeg, .png, .webp, .gif, .bmp.
5. Video plus preview/cover: when one message includes a video and a sibling still, process only the video for a clip-targeted request. The cover is a delivery helper, not a second Vmake job, unless the user explicitly asks to process it.
6. Unsupported media URL: if run-task returns error: "input_unavailable" from consume, ask the user to upload the image or video file directly and do not retry the same URL. Suggested reply: This media URL is currently unavailable. Please upload the image or video file directly.

Getting media from IM messages (full detail: docs/im-attachments.md):

Platform	How to obtain
Feishu	Message resource URL / image_key + message_id → optional resolve-input
Telegram	file_id → resolve-input --telegram-file-id (needs TELEGRAM_BOT_TOKEN)
Discord	attachments[0].url — often usable directly as --input
Generic	URL or path

bash

python3 {baseDir}/scripts/vmake_ai.py resolve-input --file /tmp/saved.jpg --output-dir /tmp
# or: --url, --telegram-file-id, --feishu-image-key + --feishu-message-id

Use the JSON path field as --input.

--input as http(s):// URL: In shells, quote the whole URL so & in query strings (e.g. signed OSS links) is not split. Large or slow downloads: defaults are 120s read timeout and 100MB max (same as resolve-input --url); override with MT_AI_URL_READ_TIMEOUT, MT_AI_URL_CONNECT_TIMEOUT, MT_AI_URL_MAX_BYTES. For very large video or flaky links, prefer resolve-input --url then --input with the local path.

If the user already supplied an attachment, path, or URL, retain it as the current input. After execution authorization is granted, proceed automatically without asking for the media or its type again.

When authorization is still required, ask only one concise question, for example:

"I've received the video. May I upload it to Vmake AI and use your account quota for quality restoration?"

After authorization, continue immediately and acknowledge execution without another question, for example:

"Authorization received. Uploading and processing the video now."

---

Step 2 — Confirm dependencies

bash

python3 {baseDir}/scripts/vmake_ai.py install-deps

This is normally already completed before catalog; the command is idempotent. Then continue to Step 3.

---

Step 3 — Run the task

Use the selected catalog entry's modality. media_type: "video" uses only §3b (spawn-run-task + sessions_spawn); media_type: "image" uses only §3a. In legacy mode, use the known modality only after the alias was returned by catalog.

3a — Inline (blocking, image tasks only)

Use for the V2 entry selected with media_type: "image", or a server-confirmed legacy image alias.

bash

python3 {baseDir}/scripts/vmake_ai.py run-task \
  --task "<catalog_material_id_or_confirmed_legacy_alias>" \
  --input "<image_url_or_path>"

Replace the placeholders with the catalog-selected ID and real input.

Default params include rsp_media_type: url. For custom JSON params:

bash

python3 {baseDir}/scripts/vmake_ai.py run-task \
  --task "<catalog_material_id_or_confirmed_legacy_alias>" \
  --input "<url_or_path>" \
  --params '{"parameter":{"rsp_media_type":"url"}}'

When remote config includes an input profile, use it as the default; when the field is omitted, do not synthesize one. Use --profile '<json-object>' only when an explicit per-run override is required; it is deep-merged over the server-provided profile in the same way as --params.

When run-task exits 0, stdout is JSON that includes:

• skill_status: "completed" — the algorithm and polling are finished; the result is in this response. If the user asked for only this stage, proceed to Step 4. If they asked for a multi-stage pipeline, use primary_result_url as --input for the next --task (see Multi-stage pipelines above); Step 4 after the last stage. Do not re-submit run-task for the same task_id (same job); use query-task to resume polling if needed.
• output_urls — ordered http(s) links (same extraction as before: data.result.urls, images, media_info_list, etc.).
• primary_result_url — same as output_urls[0] when present; convenient for delivery scripts.
• task_id — full task id as a top-level string when known (from data.result.id or the polling session). Keep it for manual status recovery or support handoff; do not truncate. Some synchronous completions may omit it if the API does not return an id.
• agent_instruction — short reminder for the model.
• notices — optional server-controlled user notices collected from config, consume, and task responses. Each usable notice contains a non-empty message; fields such as code, level, dedupe_key, and action_url are metadata and may be extended by the server.
• meta / data — full API payload for debugging.

MANDATORY (server notices): When stdout JSON contains notices, include every non-empty notice message in the user-facing reply. Unknown notice codes must still be shown. A warning notice does not change a completed task into a failure. An error notice follows the accompanying skill_status / api_code; do not infer failure from notice level alone. Do not repeat notices with the same dedupe_key in one reply. When action_url is present, surface it as a clickable link. This overseas Skill expects the server-provided message to be English; show it as provided and do not translate it automatically. Locale selection belongs to the server.

MANDATORY (user-visible outcome): When stdout JSON has skill_status: "completed" (from run-task or query-task), you must (1) send the user a short natural-language summary (success + what was done), and (2) complete Step 4 on their channel (delivery scripts below) using primary_result_url or output_urls[0], unless the user explicitly asked only for the URL with no IM delivery. Do not end the turn with only raw JSON in the tool transcript — the user should see a normal reply and the media or link in the chat.

When run-task exits non-zero, stdout is JSON with skill_status: "failed" (or an error field) — explain it to the user; do not treat as success or Step 4 delivery.

MANDATORY (quota / consume failures): When stdout JSON has failure_stage: "consume_quota" and error is credit_required (typically api_code 60002): you must send the user a clear, user-visible message grounded in the server detail (API msg). If the JSON includes pricing_url (extracted from that message when it contains an https link), must include it as a clickable link; if pricing_url is absent, must quote or paste the full detail so any links or instructions from the API still reach the user. Do not only dump raw JSON; do not retry run-task expecting success from tweaking --task / --params alone. When error is membership_required (60001): same rule (pricing_url when present, else full detail). When error is consume_denied: follow every server notice message and action link even when its notice code is unknown; do not reinterpret it as a parameter error or retry unchanged. When error is consume_param_error: treat as parameter / invocation mistakes — fix --task, --input, --params per SKILL and remote config; do not tell the user to recharge.

Video tasks use §3b in the main session. Polling, stderr, MT_AI_*, timeouts, SIGKILL / host caps, query-task / last-task recovery: docs/errors-and-polling.md and §3c–§3d. Optional: raise host tool/session wait limits — does not replace §3b for video.

3b — Async worker (sessions_spawn, video tasks)

Forbidden: Do not call spawn-run-task unless catalog confirms a V2 entry with media_type: "video", or returns the selected legacy video alias. Image tasks use §3a only.

Same pattern as medeo-video spawn-task: the main agent does not block on polling; a sub-session runs run-task and is told exactly how to detect success and deliver.

1. Build the payload with the catalog-selected ID:

bash

python3 {baseDir}/scripts/vmake_ai.py spawn-run-task \
  --task "<catalog_material_id_or_confirmed_legacy_alias>" \
  --input "<video_url_or_path>" \
  --deliver-to "<oc_xxx_or_ou_xxx_or_chat_id>" \
  --deliver-channel "feishu"

Optional: --params '<json>', --profile '<json-object>' (same as run-task), --deliver-channel telegram|discord|..., --run-timeout-seconds (default 3600, aligned with extended poll budget). Do not reduce runTimeoutSeconds below the payload default unless you accept timeout risk — wall time varies (often minutes to tens of minutes).

2. Call OpenClaw sessions_spawn with the printed sessions_spawn_args (task, label, runTimeoutSeconds) without reducing runTimeoutSeconds unless you intentionally accept timeout risk.

3. Reply immediately to the user that processing has started (same as Step 1 acknowledgment). The sub-agent completes install-deps (if needed), run-task, then Step 4 using skill_status / output_urls per the embedded task text. For video tasks on Feishu/Telegram, the payload instructs feishu_send_video.py / telegram_send_video.py after curl download.

Multi-stage + spawn: One embed = one run-task (medeo-style). Video chains: Multi-stage pipelines (rule 4). Image chains: §3a only — run run-task once per stage in the main session (or host-equivalent blocking shell); do not use spawn-run-task for image stages.

3c — Resume polling (query-task)

When you already have a full task_id (from a previous stdout JSON, e.g. success, poll_timeout, or poll_aborted, or from stderr task_id=... lines) and the job may still be running on the server — do not run run-task again for that id; resume polling only:

bash

python3 {baseDir}/scripts/vmake_ai.py query-task \
  --task-id "<full_task_id>"

Optional --task sets the task_name field in the success JSON for your logs (default labels as query_task). Uses the same MT_AK / MT_SK and remote config as the original submit. Stdout JSON and exit codes match run-task: exit 0 with skill_status: "completed" when the task finishes successfully; exit non-zero with skill_status: "failed" / error on timeout, query errors, or API-reported failure.

3d — Last task and history (user-visible)

Local state under ~/.openclaw/workspace/openclaw-vmake-ai/ (last_task.json, history/task_*.json, last 50 records). For async run-task, last_task.json may briefly show skill_status: "polling" with task_id while the client is still polling (checkpoint so query-task can resume if the process is killed mid-poll):

bash

python3 {baseDir}/scripts/vmake_ai.py last-task
python3 {baseDir}/scripts/vmake_ai.py history

Use when the user asks whether a recent job finished, or for a short history summary. Do not expose raw secrets.

---

Step 4 — Deliver result to the channel

Required after success: When skill_status is completed, deliver here — the CLI does not post to IM by itself. Send the processed image or video back on the user’s platform (and keep the Step 3 MANDATORY summary in the same turn).

Resolve deliver-to target

Platform	Source	Format
Feishu group	conversation_label or chat_id without chat: prefix	oc_xxx
Feishu DM	sender_id without user: prefix	ou_xxx
Telegram	Inbound message chat_id	e.g. -1001234567890
Discord	channel_id	e.g. 123456789

Feishu — image tasks

bash

python3 {baseDir}/scripts/feishu_send_image.py \
  --image "<result_url>" \
  --to "<oc_xxx or ou_xxx>"

Feishu — video tasks

bash

curl -sL -o /tmp/vmake_result.mp4 "<primary_result_url_or_output_urls[0]>"
python3 {baseDir}/scripts/feishu_send_video.py \
  --video /tmp/vmake_result.mp4 \
  --to "<oc_xxx or ou_xxx>" \
  --video-url "<primary_result_url_or_output_urls[0]>" \
  [--cover-url "<optional_thumb_url>"] \
  [--duration <milliseconds_if_known>]

--video-url adds a second message with the download link. Optional cover/duration; details: docs/feishu-send-video.md.

Telegram — image tasks

bash

TELEGRAM_BOT_TOKEN="$TELEGRAM_BOT_TOKEN" python3 {baseDir}/scripts/telegram_send_image.py \
  --image "<result_url>" \
  --to "<chat_id>" \
  --caption "✅ Done"

Telegram — video tasks

bash

curl -sL -o /tmp/vmake_result.mp4 "<primary_result_url_or_output_urls[0]>"
TELEGRAM_BOT_TOKEN="$TELEGRAM_BOT_TOKEN" python3 {baseDir}/scripts/telegram_send_video.py \
  --video /tmp/vmake_result.mp4 \
  --to "<chat_id>" \
  --video-url "<primary_result_url_or_output_urls[0]>" \
  [--cover-url "<optional_thumb_url>"] \
  [--duration <seconds>] \
  --caption "✅ Done"

--video-url sends a follow-up text message with the download link. Max ~50 MB for Bot API video; larger files rely on the link line.

Discord

Download the result, then send with the message tool (use .mp4 for video, .jpg / .png for image):

bash

curl -L "<result_url>" -o /tmp/result_image.jpg

Then:

text

message(action="send", channel="discord", target="<channel_id>", filePath="/tmp/result_image.jpg")

For files over ~25MB, send the result URL as a link instead.

WhatsApp / Signal / others

Use the message tool with media, or send the result URL directly.

---

Quick commands reference (agent)

Command	Description	User-facing?
preflight	AK/SK ok / missing	No
install-deps	pip install requirements	No
catalog	Fetch remote V2 catalog or legacy invoke aliases; no upload/consume	Yes — task discovery
run-task	Submit + poll until done	Indirectly
query-task	Resume poll by task_id	When recovering
spawn-run-task	Validate a remote-catalog video task and print sessions_spawn payload	No
resolve-input	IM/URL → local path for --input	No
last-task	Last job JSON	Yes — “last job?”
history	Up to 50 recent records	Yes — “history?”

---

Notes

• Single business entrypoint: catalog discovery, algorithm runs, and config fetch go through vmake_ai.py; agents do not need to open client.py / ai/api.py. Must not bypass this with direct HTTP to AIGC/WAPI — see API submission path (MANDATORY) above. query-task is the supported way to resume polling when a task_id is already known.
• Video tasks: spawn-run-task + sessions_spawn in the main session (mandatory path); the worker runs run-task and delivery. run-task in the main session is for image tasks (§3a) and for recovery (query-task). Polling and env tuning: docs/errors-and-polling.md.
• AK/SK loading: environment variables MT_AK / MT_SK first; if unset, scripts/.env is read automatically (same as SkillClient).
• Config is authoritative: catalog, run-task, and spawn-run-task pull remote config. V2 catalog output exposes each entry's material_id alongside its display fields; pass that exact ID to --task. Legacy aliases are accepted only when the response explicitly contains them. Config failure stops new jobs before upload or consume.
• Bot token safety: pass TELEGRAM_BOT_TOKEN and similar only via environment variables — never as CLI arguments.
• On failure: stdout JSON has skill_status: "failed" / error, exit code ≠ 0 — explain to the user; check AK/SK, network, quotas; timeouts / SIGKILL / no final JSON: docs/errors-and-polling.md. URL input errors may mention HTTP 403 (expired signed URL) or timeout — see MT_AI_URL_* env vars above.
• More docs: README.md, docs/multi-platform.md, docs/im-attachments.md, docs/feishu-send-video.md.