test-openapi

Async-first Python SDK for the Fotor OpenAPI. No MCP -- just an API key.

Setup

Prefer a local virtual environment in the skill directory. If .venv does not exist, create it first, then use the virtualenv interpreter for all bundled scripts instead of the system Python.

Recommended interpreter paths:

• POSIX: ./.venv/bin/python
• Windows: .venv\\Scripts\\python.exe

Typical setup flow:

python3 -m venv .venv
./.venv/bin/python scripts/ensure_sdk.py

1. Install or upgrade the latest fotor-sdk before every task

   ./.venv/bin/python scripts/ensure_sdk.py
   

2. Ensure FOTOR_OPENAPI_KEY is set in environment. If key setup is missing and the user is not technical, read references/configure-fotor-openapi-key.md and prefer the local .env happy path instead of listing many alternative methods.

Interaction Rules

• Speak in user-task language first. Do not lead with SDK, scripts, JSON, model IDs, or parameter tables unless they are needed to unblock the task or the user explicitly asks.
• Ask for only one missing blocker at a time.
• Once the minimum required information is present, execute immediately. Do not send vague transition messages like "I’m starting now" unless execution has actually started and a result or clear in-progress status will follow.
• If execution will take noticeable time, say that the task is running and give a short expectation such as "usually takes a few seconds to a few dozen seconds; I’ll send the result when it’s ready."
• If credentials are missing, resolve that blocker quickly and then return to the original task instead of turning the conversation into a long setup lesson.
• When running bundled Python scripts locally, prefer a local .venv; if it is missing, create it before installing dependencies or executing the task. Avoid installing into the system Python unless the user explicitly asks.
• Choose the model and default parameters internally unless the user explicitly requests a specific model or technical control.
• Return the result as soon as it is ready. Do not make the user ask follow-up questions like "where is the image?"
• If an update reminder is available, keep it to one short non-blocking sentence and continue the current task.

Scripts

scripts/ensure_sdk.py

Cross-platform (Windows / macOS / Linux) script to install or upgrade fotor-sdk to the latest PyPI release. Run before every task.

• No args — install or upgrade to the latest PyPI release
• --upgrade — same behavior, kept as an explicit alias

scripts/run_task.py

Execute one or more Fotor tasks from JSON. Handles client init, polling, and progress.

Single task:

echo '{"task_type":"text2image","params":{"prompt":"A cat","model_id":"seedream-4-5-251128"}}' \
  | ./.venv/bin/python scripts/run_task.py

Batch (array):

echo '[
  {"task_type":"text2image","params":{"prompt":"A cat","model_id":"seedream-4-5-251128"},"tag":"cat"},
  {"task_type":"text2video","params":{"prompt":"Sunset","model_id":"kling-v3","duration":5},"tag":"sunset"}
]' | ./.venv/bin/python scripts/run_task.py --concurrency 5

Options: --input FILE, --concurrency N (default 5), --poll-interval S (default 2.0), --timeout S (default 1200).

Output: JSON with task_id, status, success, result_url, error, elapsed_seconds, tag.

Automatic fallback:

• If a task fails on its primary model and the current task_type + model_id matches a built-in fallback mapping, run_task.py automatically retries once with the fallback model.
• The output includes fallback_used, original_model_id, and fallback_model_id.

scripts/upload_image.py

Upload a local image file through Fotor's signed upload flow and return a reusable image URL.

./.venv/bin/python scripts/upload_image.py ./input.jpg --task-type image2image

The script:

• Calls /v1/upload/sign with the mapped upload type and suffix
• Uploads the local file to the signed target
• Prints JSON containing file_url and upload_url

Use file_url as the image_url, start_image_url, end_image_url, or an item inside image_urls for image-based tasks.

Supported task-to-upload mapping:

• image2image -> img2img
• image_upscale -> img_upscale
• background_remove -> bg_remove
• single_image2video -> img2video
• start_end_frame2video -> img2video
• multiple_image2video -> img2video

scripts/check_skill_update.py

Check whether the installed skill has a newer version available for the current install source.

./.venv/bin/python scripts/check_skill_update.py --mark-notified --check-interval-hours 24

For development/testing when install-source metadata is unavailable:

./.venv/bin/python scripts/check_skill_update.py --install-source skills-github --slug test-openapi --current-version 1.0.0 --github-source zeng121/skill-beta --mark-notified --check-interval-hours 24

The script:

• Detects the install source first: clawhub or skills-github
• For clawhub, reads installed _meta.json and fetches the latest version via clawhub inspect <slug> --json
• For skills-github, reads local SKILL.md frontmatter metadata.version, finds the GitHub source, and fetches the remote SKILL.md version plus CHANGELOG.md highlights when available
• Prints JSON with install_source, current_version, latest_version, update_available, and should_notify
• Stores the last-notified version in a local state file when --mark-notified is used
• Caches the last successful version check and supports a minimum recheck interval via --check-interval-hours (default 24)
• Includes changelog_preview so the reminder can mention the main highlights without dumping the full changelog
• Supports development/testing overrides such as --install-source, --slug, --current-version, and --github-source

Reference Files

Only read the reference files that match the current need. Do not load all of them by default.

Task Execution References

Read these when choosing a model, validating parameters, or mapping an ambiguous user request to a recommended workflow:

• references/image_models.md -- image model IDs, T2I/I2I capabilities, per-model parameter constraints (resolution, ratios, input limits, max refs)
• references/video_models.md -- video model IDs, T2V/I2V/SE/MI capabilities, per-model parameter constraints (duration, resolution, ratios, input limits, audio)
• references/parameter_reference.md -- full function signatures and parameter tables for all 8 task types
• references/image_scenarios.md -- scenario-to-model mapping for image generation (T2I, I2I, utilities); read when user intent is ambiguous
• references/video_scenarios.md -- scenario-to-model mapping for video generation (T2V, I2V, SE, MI); read when user intent is ambiguous

Operational References

Read these only when the user asks about installation, upgrade, workspace layout, or credential setup. Do not read them for normal image/video task execution.

• references/install-or-upgrade.md -- how to install or upgrade test-openapi for both ClawHub and npx skills, and how to phrase a concise non-pushy upgrade reminder
• references/configure-fotor-openapi-key.md -- how to set, persist, verify, and safely handle FOTOR_OPENAPI_KEY

Workflow

1. If .venv is missing, create it first. Then run ./.venv/bin/python scripts/ensure_sdk.py before every task to install or upgrade the latest fotor-sdk.
2. After .venv is ready and ensure_sdk.py completes, silently run ./.venv/bin/python scripts/check_skill_update.py --mark-notified --check-interval-hours 24. Do not inspect the state file manually. Only if it reports should_notify: true, read references/install-or-upgrade.md and send one short non-blocking update reminder. If the check fails or times out, ignore it and continue without mentioning the failure.
3. Verify FOTOR_OPENAPI_KEY is set.
4. For image-based tasks that start from a local file, first run ./.venv/bin/python scripts/upload_image.py <local-file> --task-type <task-type> and keep the returned file_url.
5. Read the appropriate model reference to choose model_id. Each model's per-model spec section lists supported resolutions, aspect ratios, duration, input constraints, and max reference images.
6. If user intent is ambiguous (no specific model requested), consult the scenario files (image_scenarios.md / video_scenarios.md) for recommended model + params.
7. Validate parameters against the chosen model's spec before calling -- check resolution, aspect ratio, duration, and multi-image limits.
8. Quick path -- pipe JSON into ./.venv/bin/python scripts/run_task.py (works for both single and batch).
9. Custom path -- write inline Python using the SDK directly (see examples below), still preferring the local .venv interpreter.
10. Check result_url in output. Chain image_upscale if higher resolution needed.

Built-in automatic fallback mappings:

• text2image: gemini-3.1-flash-image-preview -> seedream-5-0-260128
• image2image: gemini-3.1-flash-image-preview -> seedream-5-0-260128
• text2video: doubao-seedance-1-5-pro -> kling-v3
• single_image2video: doubao-seedance-1-5-pro -> kling-v3
• start_end_frame2video: kling-video-o1 -> viduq2-turbo
• multiple_image2video: kling-v3-omni -> kling-video-o1

Available Task Types

For full parameter details (defaults, on_poll, **extra), read references/parameter_reference.md.

Inline Python Examples

When scripts/run_task.py is insufficient (custom logic, chaining, progress callbacks):

Client Init

import os
from fotor_sdk import FotorClient
client = FotorClient(api_key=os.environ["FOTOR_OPENAPI_KEY"])

Single Task

from fotor_sdk import text2image
result = await text2image(client, prompt="A diamond kitten", model_id="seedream-4-5-251128")
print(result.result_url)

Batch with TaskRunner

from fotor_sdk import TaskRunner, TaskSpec
runner = TaskRunner(client, max_concurrent=5)
specs = [
    TaskSpec("text2image", {"prompt": "A cat", "model_id": "seedream-4-5-251128"}, tag="cat"),
    TaskSpec("text2video", {"prompt": "Sunset", "model_id": "kling-v3", "duration": 5}, tag="sunset"),
]
results = await runner.run(specs)

Video with Audio

from fotor_sdk import text2video
result = await text2video(client, prompt="Jazz band", model_id="kling-v3",
                          audio_enable=True, audio_prompt="Smooth jazz")

TaskResult

result.success          # bool: True when COMPLETED with result_url
result.result_url       # str | None
result.status           # TaskStatus: COMPLETED / FAILED / TIMEOUT / IN_PROGRESS / CANCELLED
result.error            # str | None (e.g. "NSFW_CONTENT")
result.elapsed_seconds  # float
result.metadata         # dict (includes "tag" from TaskRunner)

Error Handling

• Single task: catch FotorAPIError (has .code attribute).
• Batch: check result.success per item; runner never raises on individual failures.
• NSFW: appears as error="NSFW_CONTENT" in TaskResult.

For troubleshooting, enable SDK debug logging: logging.getLogger("fotor_sdk").setLevel(logging.DEBUG).