the UGC fix-loop toolkit — surgically re-render a bad window/beat of a single-take UGC master (stitch_replacement.py, pure FFmpeg) and GPT cross-model review a Seedance prompt before render (vet_seedance_prompt.py, routed through the openai-proxy). Fetch it into a one-shot UGC recipe so both scripts resolve on any machine and the vet call bills the Ads agent.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill ugc-fixloop
The UGC fix-loop toolkit. The one-shot UGC video recipes (create-ugc-*-video-from-refs)
render a single continuous Seedance 2.0 reference-to-video master with native lip-synced
audio. This capability ships the two scripts those recipes run, so they exist on the remote
machine (fetched into /tmp/gooseworks-scripts/ugc-fixloop/).
> Any re-render of a replacement clip goes through the same proxy path the recipe uses for
> the take (create-video-fal / fal-proxy), NEVER a direct fal.run call.
stitch_replacement.py — no API key, no network. Needs ffmpeg + ffprobe on PATH (all local FFmpeg).vet_seedance_prompt.py — routes through the GooseWorks openai-proxy (<api_base>/api/internal/openai-proxy/v1/chat/completions), reading creds from ~/.gooseworks/credentials.json — no direct OpenAI call, no local key; the call bills the Ads agent. Exits 3 if the proxy/creds are unavailable so the recipe can fall back to an inline self-review (the vet is advisory, not a gate).A deliberately NON-Claude second opinion on the Seedance prompt before you spend the render
(Claude reviewing its own prompt is a weaker signal). Takes the prompt as an argument:
vet_seedance_prompt.py --prompt-file working/seedance-prompt.txt \
[--brief "one-line intent"] [--refs "@Image1=avatar; @Image2=product; @Image3=env"] \
[--words 28] [--out working/seedance-review.md]
Prints + saves the structured review (verdict, line edits, word budget, consistency risk).
Replaces one segment of the master on the VIDEO track only; the master's audio (VO + ambience)
plays straight through, so lip-sync on talking beats is never touched. Output is re-encoded
H.264 / yuv420p at the master's fps + resolution.
Required: --master M.mp4 --replacement R.mp4 --output O.mp4. Pick the window ONE of two ways:
# By beat (1-indexed segment between auto-detected scene cuts):
stitch_replacement.py --master M.mp4 --replacement R.mp4 --output O.mp4 --replace-beat 2
# By explicit window (seconds):
stitch_replacement.py --master M.mp4 --replacement R.mp4 --output O.mp4 \
--window-start 4.21 --window-end 8.75 --fit stretch
All args:
--master (required) — the single-take master mp4.--replacement (required) — the re-rendered silent replacement clip (generated via create-video-fal).--output (required) — output mp4 path.--window-start / --window-end (float seconds) — explicit hole to replace.--replace-beat (int, 1-indexed) — pick the segment between detected scene cuts.--scene-threshold (float, default 0.3) — scene-cut sensitivity for --replace-beat.--fit {stretch,trim,freeze} (default stretch) — reconcile replacement length to the hole.--dry-run — print the ffmpeg command without running.Warns if output duration drifts >0.15s from the master (audio-sync check).
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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
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Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take gooseworks-ai/ugc-fixloop from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.