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-value-propgit 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-value-prop)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-value-prop"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-value-prop/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-value-prop"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-value-prop.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 | $0.00117 | $0.00887 |
| Opus 5 | $0.00059 | $0.00443 |
| Sonnet 5 | $0.00023 | $0.00177 |
| Haiku 4.5 | $0.00012 | $0.00089 |
Grade A, and why
render-value-prop 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 11d 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-value-prop
Render a designed 'value prop' video from a config: a hook sticker, then one beat per short noun-phrase benefit claim (<=4 words each — "Drug-Free", "Zero Sugar", "NSF Certified"), each pairing the claim headline with a per-SKU product visual (the hero SKU rotates beat to beat so the eye anchor shifts), then a brand-wordmark end card. Text + product carry the spot — no narration, no talking head — and it is built to be legible sound-off. Every beat is a pure function of beat-local time t (deterministic PIL start frames + Playwright hyperframes + FFmpeg); no CSS keyframes, no setTimeout. FREE (no paid calls); music is a separate capability (create-music-elevenlabs), or ship silent for $0.
Run
render_master.py --config config.json --project <dir> -> <dir>/finals/master-clean.mp4 (silent),
1080x1920, deterministic, $0. The renderer is fully config-driven — palette, copy, SKUs,
pacing, hook, logo and end card all come from config.json (schema = ad_sample.recipe.config;
see config.example.json). Nothing is hardcoded to one brand. build_storyboard_preview.py is an
optional free preview gallery for the gate; build_text_overlays.py is optional (transparent
text-zone PNGs for compositing claims over a motion clip).
Environment: run with a Python that has Playwright (override the frame-render interpreter with
RENDER_PYTHON); ffmpeg is auto-discovered (FFMPEG env > PATH > common prefixes). The frame
renderer render_hyperframe.py is bundled in scripts/ — no external atom to fetch.
Contract
- Deterministic + FREE (Playwright frame-step + FFmpeg); no paid calls, no AI-rendered text.
- Claims are noun phrases, <=4 words; never <3, never >5. Optional benefit sentence <=12 words.
- One product visual per beat; rotate which SKU is the hero. Never reuse a flat variety-pack image as every canvas.
- Sound-off legibility is the bar: the headline uses the config
palette.inkcolor onpalette.bg; the per-beat accent color (fromvalue_props[].accent— a SKU-accent slug or a hex) is the accent rule, not the headline. - Product widths auto-scale from each image's aspect ratio (target display height), so tall sachet cutouts and wide product packshots both frame correctly.
- Assets are packshots, not always transparent cutouts: set
palette.bgto the product image's background color for seamless compositing (free — avoids a paid background-removal step). - Uniform pacing (hook ~3.0s, props 2.0-2.5s each, endcard ~2.0s); total lands in the 10-20s window (~17s). No acceleration curve.
- No human face is the focus. End card uses the brand wordmark image when a hi-res one
(aspect >= ~1.2, i.e. a real >=1200x600 wordmark) is provided via
config.logo; otherwise it falls back to a typographicbrand_namewordmark (many brands ship only a favicon). - Music is added separately by create-music-elevenlabs (quiet instrumental bed at -14 dB), or ship silent.
What ships with it
23 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.
- scripts/build_storyboard_preview.py 11 KB runs code
- scripts/build_text_overlays.py 6.6 KB runs code
- scripts/config.example.json 1.9 KB
- scripts/render_hyperframe.py 4.5 KB runs code
- scripts/render_master.py 17 KB runs code
- shared/_shared.css 4.7 KB
- shared/_shared.js 2.5 KB runs code
- shared/animations/registry.yml 4.2 KB
- shared/animations/sachet-entrance/cascade-from-top.js 980 B runs code
- shared/animations/sachet-entrance/fade-stagger.js 1013 B runs code
- shared/animations/sachet-entrance/slide-in-lateral.js 1.2 KB runs code
- shared/animations/sachet-entrance/springScale-pop.js 883 B runs code
- shared/animations/sachet-entrance/tilt-fan.js 1.1 KB runs code
- shared/animations/text-entrance/fade-stagger.js 1.3 KB runs code
- shared/animations/text-entrance/mask-reveal.js 1.1 KB runs code
- shared/animations/text-entrance/type-on.js 1.3 KB runs code
- shared/animations/text-entrance/word-by-word.js 1.9 KB runs code
- shared/beat-templates/endcard-wordmark.html 1.9 KB
- shared/beat-templates/hook-sticker.html 2.4 KB
- shared/beat-templates/prop-hero.html 2.7 KB
- shared/beat-templates/prop-row.html 3.0 KB
- skill.meta.json 311 B
- tests/smoke-test.md 477 B
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.
- 11d ago First seen · 39 lines · 117 tokens per session scan A 891512005ce1
render-value-prop is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 117 tokens to every session and 887 once invoked, about $0.0006 per session on Opus 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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