fotor-skills

Fotor AI image generator and AI video generator for photo editing, background remover, background replacement, product photos, ad creatives, social media gra...

Fotor OpenAPI skill for AI image generation, AI photo editing, AI video generation, product photos, ad creatives, social media graphics, background removal, photo restoration, and image upscaling.

This skill should match user requests expressed in outcome language first, not SDK language. Keep technical details behind the scenes unless they are needed to unblock execution.

When This Skill Matches

Use this skill when the user asks for outcomes such as:

• Generate AI images from a text prompt
• Edit or restyle an existing photo
• Turn a product shot into an e-commerce or ad-ready asset
• Create posters, banners, covers, thumbnails, or social media graphics
• Remove or replace an image background
• Restore, enhance, or upscale a blurry or old photo
• Generate AI videos from text, one image, multiple images, or start/end frames
• Batch-produce visual assets for branding, content, or marketing campaigns

Search Intent Coverage

Common search phrases this skill should be able to match include:

• AI photo editor
• AI image generator
• AI video generator
• Image to image
• Text to image
• Text to video
• Product photo generator
• Ad creative generator
• Marketing visual generator
• Poster maker
• Banner maker
• Social media post generator
• Cover and thumbnail generator
• Background remover
• Background replacement
• Photo restoration
• Image upscaler
• E-commerce image generation
• Brand asset generation

For API key application and product details, see https://developers.fotor.com/fotor-skills/.

Use uv as the bootstrap layer. Prefer a skill-local Python 3.12 environment and run bundled scripts from that local environment instead of the system Python.

Runtime Setup

Keep setup lightweight and local to the skill directory.

Install uv first if it is missing:

bash

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Typical first-run setup:

bash

uv python install 3.12
uv venv --python 3.12 .venv
./.venv/bin/python scripts/ensure_sdk.py

Setup rules:

1. Prefer a local Python 3.12 environment in the skill directory.
2. Use uv to prepare Python 3.12 and create .venv when the local environment is missing.
3. Run bundled scripts from the local skill environment, not the system Python.
4. Ensure FOTOR_OPENAPI_KEY is set. If the user asks where to get a key, wants the official fotor-skills homepage during setup, or needs a key + homepage walkthrough, read references/get_api_key.md first.

Current default interpreter paths:

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

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 the local skill environment is missing, prepare it with uv 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 the user asks how to recharge, buy credits, top up, or purchase tokens, use references/credits-and-recharge.md and follow its recharge guidance flow.
• If a task fails because credits are insufficient, do not stop at the raw error. Use references/credits-and-recharge.md to explain the failure and provide recharge guidance.
• 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 with uv pip install --python <interpreter>. 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:

bash

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

Batch (array):

bash

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, creditsIncrement, 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.
• If the failure is insufficient credits (code=510 / No enough credits), run_task.py returns the failure immediately and does not retry on a 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.

bash

./.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.

bash

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

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

bash

./.venv/bin/python scripts/check_skill_update.py --install-source skills-github --slug fotor-skills --current-version 1.0.0 --github-source fotor-ai/fotor-skills --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 top-level version field, falls back to legacy 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, API key acquisition, or the official fotor-skills homepage. Do not read them for normal image/video task execution.

• references/credits-and-recharge.md -- credit lookup, payment links, recharge guidance, and insufficient-credit handling
• references/get_api_key.md -- guide for giving the official fotor-skills homepage, sending users to the API-key dashboard entry, and configuring FOTOR_OPENAPI_KEY
• references/install-or-upgrade.md -- how to install or upgrade fotor-skills for both ClawHub and npx skills, and how to phrase a concise non-pushy upgrade reminder

Workflow

1. If uv is missing, install it first using the platform-appropriate installer shown in Setup.
2. If Python 3.12 is not already available to uv, run uv python install 3.12.
3. If .venv is missing, create it with uv venv --python 3.12 .venv.
4. Run ./.venv/bin/python scripts/ensure_sdk.py before every task to install or upgrade the latest fotor-sdk with uv.
5. 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.
6. If the user asks how to get an API key or wants the official fotor-skills homepage during setup, read references/get_api_key.md and include both links before expanding into lower-level setup details.
7. Verify FOTOR_OPENAPI_KEY is set.
8. If the user asks about account credits, recharge, buying credits, or payment links, read references/credits-and-recharge.md, then use the appropriate balance or payment-links flow from that reference.
9. 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.
10. 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.
11. If user intent is ambiguous (no specific model requested), consult the scenario files (image_scenarios.md / video_scenarios.md) for recommended model + params.
12. Validate parameters against the chosen model's spec before calling -- check resolution, aspect ratio, duration, and multi-image limits.
13. Quick path -- pipe JSON into ./.venv/bin/python scripts/run_task.py (works for both single and batch).
14. Custom path -- write inline Python using the SDK directly (see examples below), still preferring the local .venv interpreter.
15. Check result_url in output. Chain image_upscale if higher resolution needed.

If the user asks to check account credits or remaining credits, read references/credits-and-recharge.md and use the SDK client flow described there instead of run_task.py.

Built-in automatic fallback mappings are defined in references/fallback_models.json.

run_task.py reads that file directly. Keep exact fallback pairs there instead of duplicating them in SKILL.md or scenario references.

Available Task Types

task_type	Function	Required Params
text2image	text2image()	prompt, model_id
image2image	image2image()	prompt, model_id, image_urls
image_upscale	image_upscale()	image_url
background_remove	background_remove()	image_url
text2video	text2video()	prompt, model_id
single_image2video	single_image2video()	prompt, model_id, image_url
start_end_frame2video	start_end_frame2video()	prompt, model_id, start_image_url, end_image_url
multiple_image2video	multiple_image2video()	prompt, model_id, image_urls (≥2)

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

Credits and Recharge

For any balance lookup, recharge guidance, or insufficient-credit case, read references/credits-and-recharge.md.

Keep SKILL.md focused on routing:

• Use the credits reference when the user asks about remaining balance, total credits, recharge, top-up, or payment links.
• Use the same reference when a task fails with code=510 or No enough credits.
• Keep detailed API examples, field meanings, and user-facing recharge wording in the reference instead of expanding this main skill file.

Inline Python Examples

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

Client Init

python

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

Single Task

python

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

python

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

python

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

TaskResult

python

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.creditsIncrement # int | float: credits consumed by this task
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.
• Insufficient credits: if result.error, exception text, or a combined fallback error contains code=510 or No enough credits, treat it as a recharge case. Tell the user credits are insufficient, then fetch and present payment links.

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