Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gooseworks-ai/goose-skills --skill render-song-mvgit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-song-mv)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-song-mv"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-song-mv/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-song-mv"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-song-mv.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00142 | $0.01217 |
| Opus 5.5 | $0.00057 | $0.00487 |
| Sonnet 5.5 | $0.00028 | $0.00243 |
| Haiku 4.5 | $0.00014 | $0.00122 |
Grade A, and why
render-song-mv scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-song-mv
Assemble a song-driven music-video ad from a config: a purpose-written, sung song is
the entire narration (no separate voiceover), and every visual beat is timed to the lyrics.
The delivered song sets the timeline; N tableaux (one keyframe → one image-to-video clip per
lyric beat, all in a single look pack) are cut to their lyric windows and hard-concatenated
on the beat, captions are built from the song's OWN word timings with the hook line landing
on the chorus drop, and the spot closes on a PIL brand end card. It reads like a tiny animated
music video, not a demo. scripts/config.example.json is one worked example (Fernhollow "Bring The
Green Back Home", 28s paper-craft 9:16) — its style, song, vocalist, mood, protagonist and
setting are that demo's answers, not defaults; scripts/PIPELINE.md maps every config block
to its step and scripts/README.md documents the free assembly.
Choices
The creative calls are made upstream by the user (the recipe's choices) and arrive in the config;
this assembly never picks them.
- visual_style →
look_pack,clip_engine.motion_opener. The demo used a paper-craft diorama on a rainy morning. - song_genre →
song.prompt/song.bpm. The demo used breezy bedroom-pop at ~80 BPM. - vocalist →
song.prompt. The demo used a warm, airy male lead. - mood →
song.prompt, the lyrics,tableaux. The demo went grey and stuck → fresh and hopeful. - protagonist →
look_pack.style_opener. The demo used a young-man paper-doll. - setting →
tableaux[].keyframe_prompt. The demo went grey apartment → paper indoor jungle.
Run
This is the FREE, deterministic assembly stage — it spends nothing. The three paid
inputs are separate capabilities: the sung song (create-music-elevenlabs, music_v1,
force_instrumental FALSE — the lyrics ARE the script, returns mp3 + words.json), one
keyframe per tableau (create-image-fal), and one Kling 3.0 i2v clip per tableau
(create-video-fal). Given the delivered song + words.json + one clip per beat,
render-song-mv cuts each clip to its lyric window, hard-concats on the beat, builds the
lyric-synced captions, composites the PIL end card, and muxes → the master. Re-cuts reuse
the existing song / keyframes / clips and spend nothing.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed 891fb4dd0249
- 29d ago Changed · +15 lines c8653a4721a8
- 1mo ago First seen · 53 lines · 142 tokens per session scan A 5e662fa14481
render-song-mv is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,272 stars, last pushed 2d ago), licensed MIT. It adds 142 tokens to every session and 1,217 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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