Modellix Skill

Modellix is a Model-as-a-Service (MaaS) platform for asynchronous image, video, and audio workflows. Prefer the official CLI (modellix-cli) so submit, wait, and download stay one coherent workflow. Host-specific persistent session guardrails also ship under rules/*.mdc.

Official Docs

• AI Onboarding: https://docs.modellix.ai/get-started.md
• REST API: https://docs.modellix.ai/ways-to-use/api.md
• Full Models Index: https://docs.modellix.ai/llms.txt
• Docs MCP (search / read docs): https://docs.modellix.ai/mcp
• CLI package (source of truth for CLI behavior): https://www.npmjs.com/package/modellix-cli

Documentation lookup policy

This plugin may expose the Modellix Docs MCP (.mcp.json → https://docs.modellix.ai/mcp). It is a read-only documentation server (search_modellix, docs filesystem query, optional feedback). It does not submit generation tasks, poll, download, or handle API keys.

When looking up product/API/install docs vs request-body schema:

1. For request/response schema, use modellix-cli model get-schema <slug> (JSON is the default; the endpoint is public and needs no API key). If CLI is unavailable, use Docs MCP, then docs_url from model describe or https://docs.modellix.ai/llms.txt.
2. For product/install narrative, prefer Docs MCP when the host has it connected (search, then read the matching page). Else use model describe <slug> --json → docs_url, or browse llms.txt and fetch the model .md.
3. For CLI command syntax and flags, prefer this skill, references/cli-playbook.md, the npm README, or modellix-cli --help — do not trust website CLI pages over the CLI package (docs can lag).

If the Docs MCP exposes a skill resource, treat this SKILL.md as the execution policy source of truth (CLI-first, defaults, paid-submit safety).

Do not rely on the website CLI guide page for command syntax.

Execution Policy (CLI-first)

Choose the path in this order:

1. CLI after scripts/preflight.py --json resolves it. Preflight checks public npm latest, installs only a newer exact version before execution, and keeps an existing CLI when update infrastructure is unavailable.
2. REST only when CLI is not installed, unsuitable, or missing a needed capability.
3. Prefer machine-readable output (--json or --quiet) for automation.

Canonical single-task flow:

python3 scripts/preflight.py --json
modellix-cli doctor --json
modellix-cli model get-schema <provider/model>
modellix-cli model run \
  --model-slug <provider/model> \
  --body '<json>' \
  --wait --timeout 5m --json
modellix-cli task download <task_id> --output-dir ./outputs --json

Skip get-schema when the skill default plus the example below already lists the required fields, or the user supplied a complete body.

model invoke is a compatibility alias of model run. New commands should use model run.

Do not reinvent polling loops when CLI wait is available. Do not invent deprecated flags (for example --model-type). Use --help only when behavior is unclear.

Default Models

When the user does not name a model, use these defaults immediately (do not scan the catalog first):

API Key Lifecycle Policy

Handle credentials as: discover -> request -> use-session -> (optional) persist.

1) Discover existing key first

Before asking the user:

1. Session / process env MODELLIXAPIKEY
2. Saved CLI profile (modellix-cli auth status / doctor — key via --profile or MODELLIX_PROFILE or currentProfile)
3. If still missing, request a key from the user

Never ask again when a usable key is already discoverable. CLI key resolution order is: --api-key → MODELLIXAPIKEY → selected saved profile.

2) Request key only when missing

• Ask for a key from Modellix Console.
• Do not print or echo key values.
• Prefer session env for the current run.

3) Optional persistence

Default: do not persist automatically.

When the user explicitly asks to persist:

1. Preferred: modellix-cli auth login or modellix-cli init (CLI validates and stores the profile securely).
2. Alternative: user-level MODELLIXAPIKEY only if they insist on env persistence.
3. Do not write system-level env or other agents' config files.

4) Key rotation

If the user provides a new key: update session first; if they requested persistence, replace via auth login/init (or user-level env). Re-check with modellix-cli doctor --json (or scripts/preflight.py --json) before continuing.

Preflight and Deterministic Execution

Required first-workflow check when Python 3 is available:

python3 scripts/preflight.py --json

Bundled helpers:

1. scripts/preflight.py — checks public npm latest, safely updates a missing/older global CLI before paid work, pins newer local installs instead of downgrading, wraps doctor, and recommends cli, rest, or none.
2. scripts/invokeandpoll.py — performs the same resolution before submission, pins the resolved executable for the workflow, uses model run --wait on CLI, and otherwise keeps the REST submit+poll fallback.

Set MODELLIXCLIAUTO_UPDATE=0 (also accepts false or off) only when the environment must keep its installed CLI version. A registry/install failure is non-destructive: use the existing CLI if it still passes doctor, or REST when no CLI is usable and a session API key exists. Never update or swap the CLI after a paid submission has started.

When preflight/doctor reports missing credentials, apply the lifecycle above.

When CLI is unavailable:

1. Use REST (references/rest-playbook.md).
2. Report the preflight update warning; do not repeatedly attempt installation inside the same paid workflow.

Core Workflow

1) Ready the environment

• Discover or request API key (lifecycle above).
• Run scripts/preflight.py --json; continue only with the CLI path whose doctor check passed, or with an authenticated REST fallback.
• Continue only when auth and connectivity look healthy (or REST key is set).

2) Select model

1. If the user did not specify a model: use the Default Models table (do not scan the catalog first).
2. If they named a model or need discovery: modellix-cli model list / modellix-cli model describe <slug> (catalog metadata; describe returns docs_url).
3. For request body schema: modellix-cli model get-schema <slug> (JSON default; public, no API key). Call this when the user named a non-default slug, when the body needs fields beyond the documented examples, after HTTP 400, or when reporting required fields. Skip when the skill default plus the example already lists required fields, or the user supplied a complete body.
4. If CLI is unavailable for schema or discovery: use Docs MCP or browse llms.txt, then fetch the target model .md. Do not invent slugs from filenames (decimals often matter, e.g. bytedance/seedance-2.0-mini-t2v).

3) Run and wait

Default (single task):

modellix-cli model run \
  --model-slug google/nano-banana-2-lite \
  --body '{"prompt":"A cinematic sunset over a futuristic city skyline"}' \
  --wait --timeout 5m --json

Split flow when useful (pipelines, concurrency):

TASK_ID=$(modellix-cli model run --model-slug ... --body '...' --output task-id)
modellix-cli task wait "$TASK_ID" --timeout 10m --json

Batch (paid guard required): modellix-cli model batch tasks.jsonl --max-tasks N [--wait].

Manual REST: references/rest-playbook.md. Optional helper: scripts/invokeandpoll.py.

4) Download results

modellix-cli task download <task_id> --output-dir ./outputs --json

If download fails with Resource host resolves to a private or reserved network address (common when a local proxy/VPN maps CDN hosts like file.modellix.ai into 198.18.0.0/15), retry with --allow-private-network for trusted Modellix CDN hosts, or fall back to downloading the result.resources[].url with curl/wget.

Resource URLs expire in about 7 days — persist promptly. If downloading manually (REST path), name files:

modellix-{model_slug}-{timestamp}.{ext}

(replace / in the slug with -).

Examples:

• modellix-google-nano-banana-2-lite-20260430-113000.png
• modellix-bytedance-seedance-2.0-mini-t2v-20260430-113500.mp4

Quick Examples

T2I (default model) — prompt required:

modellix-cli model run \
  --model-slug google/nano-banana-2-lite \
  --body '{"prompt":"A cinematic sunset over a futuristic city skyline"}' \
  --wait --timeout 5m --json

T2V (default model) — prompt required:

modellix-cli model run \
  --model-slug bytedance/seedance-2.0-mini-t2v \
  --body '{"prompt":"A cat playing in a sunny garden"}' \
  --wait --timeout 10m --json

I2I — prompt + image array:

modellix-cli model run \
  --model-slug google/nano-banana-2-lite-edit \
  --body '{"prompt":"Convert to watercolor style","image":["https://example.com/input.jpg"]}' \
  --wait --timeout 5m --json

I2V — at least one image param (firstframeimage, lastframeimage, or reference_images):

modellix-cli model run \
  --model-slug bytedance/seedance-2.0-fast-i2v \
  --body '{"prompt":"A cat playing in the garden","first_frame_image":"https://example.com/frame.jpg"}' \
  --wait --timeout 10m --json

V2V — video_urls array:

modellix-cli model run \
  --model-slug bytedance/seedance-2.0-fast-v2v \
  --body '{"video_urls":["https://example.com/source.mp4"]}' \
  --wait --timeout 10m --json

TTS (default model) — text + voice required (verify voice with model get-schema or the model doc; Flash voices only):

modellix-cli model run \
  --model-slug alibaba/qwen-audio-3.0-tts-flash \
  --body '{"text":"There is a large garden behind my house.","voice":"longanhuan_v3.6"}' \
  --wait --timeout 5m --json

STT (default model) — public audio url required:

modellix-cli model run \
  --model-slug openai/whisper-1 \
  --body '{"url":"https://example.com/meeting.mp3"}' \
  --wait --timeout 5m --json

STS (default model) — clone reference url + synthesis text + target CosyVoice model:

modellix-cli model run \
  --model-slug alibaba/cosyvoice-clone \
  --body '{"model":"cosyvoice-v3.5-plus","url":"https://example.com/reference.wav","text":"There is a large garden behind my house."}' \
  --wait --timeout 5m --json

5) Post-task CLI recommendation

If this session used REST because CLI was missing, suggest installing the CLI afterward.

Progressive Reference Routing

Read only what the task needs:

• references/cli-playbook.md — install, auth, run/wait/download, batch, recovery
• references/rest-playbook.md — REST submit/poll when CLI is unavailable
• references/capability-matrix.md — CLI ↔ REST mapping and fallback rules

Bundled Assets

• assets/output/task-result.schema.json

Credential and Data Egress

• Primary credential: MODELLIXAPIKEY (also via CLI profiles).
• Network egress: https://api.modellix.ai (override only with trusted --base-url / MODELLIXBASEURL).
• User prompts and media inputs may be sent to Modellix during invocation.
• Never expose API keys in logs, screenshots, transcripts, or commits.
• Default to session-only credentials; persistent writes need explicit user approval.

Error / Retry Policy

Verification Checklist

• Doctor/preflight passed or REST key ready
• Model chosen (default table or user/catalog)
• Body schema checked via model get-schema when non-trivial (or model doc when CLI is unavailable)
• Used model run --wait (or task wait) instead of hand-rolled poll loops
• Results downloaded (task download or manual persist before 7-day expiry)
• No blind retry after unknown paid submission
• REST used only when CLI path unavailable