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starchild-ai-agent/@554/video

Name: @554/video
Author: starchild-ai-agent

554/video/SKILL.md

npx skillsauth add starchild-ai-agent/community-skills @554/video

Clean

TrivyContainer and dependency vulnerability scanner

Clean

SemgrepStatic code analysis for vulnerabilities

Clean

mcp-scan (Snyk)Model Context Protocol security validation

Skipped

Snyk (dep)Open source security scanning

Skipped

Socket.devSupply chain security analysis

Skipped

VirusTotalMulti-engine malware detection

Skipped

CrowdStrikeAdvanced threat intelligence

Skipped

OSV-ScannerOpen Source Vulnerability database check

Skipped

OWASP Dep-Check

video

Use this skill for all video-generation requests on Starchild.

Core principle: call the provided scripts. Do not re-implement proxy/billing/upload plumbing.

1. Text-to-video (most common)

exec(open('skills/video/generate_video.py').read())
result = generate_video(
    prompt="A cinematic drone shot over snowy mountains at sunrise",
    model="balanced",   # "budget" | "balanced" | "premium"
    duration=5,
)
# result -> {"success": True, "cost": 0.70, "video_url": "...", "local_path": "output/videos/..."}

generate_video automatically: submits → polls → fetches result → downloads mp4 to output/videos/.

2. Image-to-video / video-to-video (reference assets)

fal.ai needs the reference asset as a public https URL. fal storage upload requires a Serverless permission your key currently does not have. The reliable path is to expose the asset via a published Starchild preview.

Standard procedure

Drop or copy the asset into output/fal_assets/ using publish_asset.py.
Make sure a preview named fal-assets is running and published (one-time setup, see §3).
Build the public URL as <preview_base>/<filename>.
Call generate_video(... image_url=public_url).

# Step 1: publish a local image into the asset folder
exec(open('skills/video/publish_asset.py').read())
asset = publish_local('/path/to/your/photo.jpg')
# or: publish_from_url('https://example.com/photo.jpg')

filename = asset['filename']

# Step 2: combine with the preview's public base URL (see §3)
public_url = f"https://community.iamstarchild.com/<user_slug>-fal-assets/{filename}"

# Step 3: image-to-video
exec(open('skills/video/generate_video.py').read())
result = generate_video(
    prompt="gentle cinematic camera push-in",
    model="balanced",
    duration=5,
    image_url=public_url,
)

generate_video auto-rewrites the model path from */text-to-video to */image-to-video whenever image_url is provided. The same approach works for video-to-video models — pass an mp4 URL instead.

Asset constraints (enforced by `publish_asset.py`)

Image: .jpg .jpeg .png .webp .gif .bmp, max 10 MB
Video: .mp4 .mov .webm .mkv .m4v, max 100 MB
Anything outside these is rejected before publish

3. One-time `fal-assets` public preview setup

Run this once per workspace. The preview keeps running across sessions.

# 3.1 ensure the asset folder exists with a placeholder index
import os, pathlib
pathlib.Path('output/fal_assets').mkdir(parents=True, exist_ok=True)
if not os.path.exists('output/fal_assets/index.html'):
    open('output/fal_assets/index.html', 'w').write(
        '<!doctype html><html><body><h1>fal asset host</h1></body></html>'
    )

# 3.2 start the preview
preview(action='serve', dir='output/fal_assets', title='fal-assets')

# 3.3 publish to a public URL
preview(action='publish', preview_id='<id from step 3.2>', slug='fal-assets', title='fal-assets')
# → public base: https://community.iamstarchild.com/<user_slug>-fal-assets/

After publish, the public base URL is reusable for every future image-to-video / video-to-video task. Files dropped into output/fal_assets/ become reachable as <base>/<filename> immediately — no re-publish needed.

Verify with:

curl -sI https://community.iamstarchild.com/<user_slug>-fal-assets/<filename>
# expect: HTTP/2 200, content-type: image/* or video/*

If preview(action='serve') returns No available ports in pool, ask the user which existing preview can be stopped to free a port — never silently kill one.

4. Model selection

| Tier | Model | Cost / 5s | Notes | |------|-------|-----------|-------| | budget | fal-ai/wan/v2.5/text-to-video | $0.25 | Fastest, cheapest; good for prompt iteration | | balanced | alibaba/happy-horse/text-to-video | $0.70 | Default; best lip-sync, most use cases | | premium | bytedance/seedance-2.0/fast/text-to-video | $1.20 | Best motion + camera direction |

Override by passing the full model id to generate_video(model=...). Image-to-video variants are auto-derived by replacing text-to-video with image-to-video.

Pricing details and model registry live in generate_video.py::estimate_cost.

5. Polling an existing request

exec(open('skills/video/poll_status.py').read())
result = poll_video("019ded6c-d871-7290-bbf1-ddc6993f8958")

Use this when an earlier generate_video call timed out or you only have a request_id.

6. Provided scripts

generate_video.py — submit → poll → download. Handles text-to-video and image-to-video.
publish_asset.py — copy local files (or download remote URLs) into output/fal_assets/ so they can be served by the fal-assets preview.
poll_status.py — resume polling by request_id, downloads the result on completion.

7. Troubleshooting

| Problem | Fix | |---------|-----| | image_url must be a public HTTP(S) URL | Use publish_asset.py + fal-assets preview, then pass the public URL | | No available ports in pool (preview serve) | Ask the user which preview to stop; do not auto-kill | | downstream_service_error after COMPLETED | Reference asset host failed mid-render — re-encode/resize to 16:9, re-publish, retry | | HTTP 402 insufficient_credits | Top up balance; cost is pre-charged on submit | | HTTP 403 endpoint_not_allowed | sc-proxy only allows approved fal video endpoints; pick one from the model table | | Generation FAILED upstream | Shorten prompt, drop unusual tokens, retry once before changing model | | Job stuck IN_PROGRESS >15 min | Save request_id, resume later with poll_status.py |

8. Infrastructure (reference)

Caller → sc-proxy → queue.fal.run (and api.fal.ai) → fal model providers
All requests must include Authorization: Key fake-falai-key-12345 (proxy injects the real FAL_KEY)
Pre-charge happens at submit. Poll/result calls are free.
Allowed endpoints: video text-to-video / image-to-video / video-to-video / edit-video for the registered models. Anything else returns 403 endpoint_not_allowed.
Final mp4 lives at https://*.fal.media/... — public CDN, no auth needed for download.

9. Maintenance

Adding a new model → register price in generate_video.py::estimate_cost and in transparent-proxy/apis/falai.py::_VIDEO_PRICING.
Asset hosting via fal storage upload is intentionally not used in this skill: the production FAL_KEY lacks Serverless permission. Keep using the preview-based approach until that changes.

starchild-ai-agent/@554/video

554/video/SKILL.md

End-to-end video generation via fal.ai through Starchild paid proxy. Covers text-to-video, image-to-video, video-to-video, model selection, billing, polling, and serving local reference assets via a public preview.

data-ai

Updated May 4, 2026

$ install --global

skillsauth

npx skillsauth add starchild-ai-agent/community-skills @554/video

Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.

Security Scan Results

3 of 9 scanners reported clean

Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.

Scanners Passed

Scanners in report

Clean

TrivyContainer and dependency vulnerability scanner

95%

Clean

SemgrepStatic code analysis for vulnerabilities

95%

Clean

mcp-scan (Snyk)Model Context Protocol security validation

95%

Skipped

Snyk (dep)Open source security scanning

50%

Skipped

Socket.devSupply chain security analysis

50%

Skipped

VirusTotalMulti-engine malware detection

50%

Skipped

CrowdStrikeAdvanced threat intelligence

50%

Skipped

OSV-ScannerOpen Source Vulnerability database check

50%

Skipped

OWASP Dep-Check

50%

Last scanned: May 4, 2026, 4:09 AM182.4s4 files scanned

SKILL.md

name:: @554/video
version:: 3.0.0
description:: End-to-end video generation via fal.ai through Starchild paid proxy. Covers text-to-video, image-to-video, video-to-video, model selection, billing, polling, and serving local reference assets via a public preview.
emoji:: 🎬
skillKey:: video
env:: [FAL_KEY]
user-invocable:: true
disable-model-invocation:: false

video

Use this skill for all video-generation requests on Starchild.

Core principle: call the provided scripts. Do not re-implement proxy/billing/upload plumbing.

1. Text-to-video (most common)

exec(open('skills/video/generate_video.py').read())
result = generate_video(
    prompt="A cinematic drone shot over snowy mountains at sunrise",
    model="balanced",   # "budget" | "balanced" | "premium"
    duration=5,
)
# result -> {"success": True, "cost": 0.70, "video_url": "...", "local_path": "output/videos/..."}

generate_video automatically: submits → polls → fetches result → downloads mp4 to output/videos/.

2. Image-to-video / video-to-video (reference assets)

Standard procedure

Drop or copy the asset into output/fal_assets/ using publish_asset.py.
Make sure a preview named fal-assets is running and published (one-time setup, see §3).
Build the public URL as <preview_base>/<filename>.
Call generate_video(... image_url=public_url).

# Step 1: publish a local image into the asset folder
exec(open('skills/video/publish_asset.py').read())
asset = publish_local('/path/to/your/photo.jpg')
# or: publish_from_url('https://example.com/photo.jpg')

filename = asset['filename']

# Step 2: combine with the preview's public base URL (see §3)
public_url = f"https://community.iamstarchild.com/<user_slug>-fal-assets/{filename}"

# Step 3: image-to-video
exec(open('skills/video/generate_video.py').read())
result = generate_video(
    prompt="gentle cinematic camera push-in",
    model="balanced",
    duration=5,
    image_url=public_url,
)

Asset constraints (enforced by `publish_asset.py`)

Image: .jpg .jpeg .png .webp .gif .bmp, max 10 MB
Video: .mp4 .mov .webm .mkv .m4v, max 100 MB
Anything outside these is rejected before publish

3. One-time `fal-assets` public preview setup

Run this once per workspace. The preview keeps running across sessions.

# 3.1 ensure the asset folder exists with a placeholder index
import os, pathlib
pathlib.Path('output/fal_assets').mkdir(parents=True, exist_ok=True)
if not os.path.exists('output/fal_assets/index.html'):
    open('output/fal_assets/index.html', 'w').write(
        '<!doctype html><html><body><h1>fal asset host</h1></body></html>'
    )

# 3.2 start the preview
preview(action='serve', dir='output/fal_assets', title='fal-assets')

# 3.3 publish to a public URL
preview(action='publish', preview_id='<id from step 3.2>', slug='fal-assets', title='fal-assets')
# → public base: https://community.iamstarchild.com/<user_slug>-fal-assets/

Verify with:

curl -sI https://community.iamstarchild.com/<user_slug>-fal-assets/<filename>
# expect: HTTP/2 200, content-type: image/* or video/*

If preview(action='serve') returns No available ports in pool, ask the user which existing preview can be stopped to free a port — never silently kill one.

4. Model selection

Override by passing the full model id to generate_video(model=...). Image-to-video variants are auto-derived by replacing text-to-video with image-to-video.

Pricing details and model registry live in generate_video.py::estimate_cost.

5. Polling an existing request

exec(open('skills/video/poll_status.py').read())
result = poll_video("019ded6c-d871-7290-bbf1-ddc6993f8958")

Use this when an earlier generate_video call timed out or you only have a request_id.

6. Provided scripts

generate_video.py — submit → poll → download. Handles text-to-video and image-to-video.
publish_asset.py — copy local files (or download remote URLs) into output/fal_assets/ so they can be served by the fal-assets preview.
poll_status.py — resume polling by request_id, downloads the result on completion.

7. Troubleshooting

8. Infrastructure (reference)

Caller → sc-proxy → queue.fal.run (and api.fal.ai) → fal model providers
All requests must include Authorization: Key fake-falai-key-12345 (proxy injects the real FAL_KEY)
Pre-charge happens at submit. Poll/result calls are free.
Allowed endpoints: video text-to-video / image-to-video / video-to-video / edit-video for the registered models. Anything else returns 403 endpoint_not_allowed.
Final mp4 lives at https://*.fal.media/... — public CDN, no auth needed for download.

9. Maintenance

Adding a new model → register price in generate_video.py::estimate_cost and in transparent-proxy/apis/falai.py::_VIDEO_PRICING.
Asset hosting via fal storage upload is intentionally not used in this skill: the production FAL_KEY lacks Serverless permission. Keep using the preview-based approach until that changes.

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Download

For Claude Desktop. Download once, then upload the file in the app — no terminal needed.

Need help? View full Cowork setup guide →

Install manually

Choose your platform

# Clone the repo
git clone https://github.com/starchild-ai-agent/community-skills.git

# Copy into Claude Code skills folder (global)
cp -r community-skills/554/video ~/.claude/skills/

Claude Code Skills — official skills path docs.

Repository

starchild-ai-agent/community-skills

Compatible with

Claude Code

OpenAI Codex CLI

ChatGPT

Adoption

starchild-ai-agent/@554/video

$ install --global

Security Scan Results

SKILL.md

video

1. Text-to-video (most common)

2. Image-to-video / video-to-video (reference assets)

Standard procedure

Asset constraints (enforced by publish_asset.py)

3. One-time fal-assets public preview setup

4. Model selection

5. Polling an existing request

6. Provided scripts

7. Troubleshooting

8. Infrastructure (reference)

9. Maintenance

Related Skills

starchild-ai-agent/@2004/tqx-quant

starchild-ai-agent/@6171/physics-lab-experiment-suite

starchild-ai-agent/@1892/agent-blog

starchild-ai-agent/@6171/pendulum-g-experiment

starchild-ai-agent/@554/video

$ install --global

Security Scan Results

SKILL.md

video

1. Text-to-video (most common)

2. Image-to-video / video-to-video (reference assets)

Standard procedure

Asset constraints (enforced by publish_asset.py)

3. One-time fal-assets public preview setup

4. Model selection

5. Polling an existing request

6. Provided scripts

7. Troubleshooting

8. Infrastructure (reference)

9. Maintenance

Related Skills

starchild-ai-agent/@2004/tqx-quant

starchild-ai-agent/@6171/physics-lab-experiment-suite

starchild-ai-agent/@1892/agent-blog

starchild-ai-agent/@6171/pendulum-g-experiment

Asset constraints (enforced by `publish_asset.py`)

3. One-time `fal-assets` public preview setup

Asset constraints (enforced by `publish_asset.py`)

3. One-time `fal-assets` public preview setup