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-brand-identity-revealgit 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-brand-identity-reveal)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-brand-identity-reveal"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-brand-identity-reveal/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-brand-identity-reveal"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-brand-identity-reveal.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 | $0.00205 | $0.01179 |
| Opus 5 | $0.00102 | $0.00589 |
| Sonnet 5 | $0.00041 | $0.00236 |
| Haiku 4.5 | $0.00020 | $0.00118 |
Grade A, and why
render-brand-identity-reveal 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 13d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-brand-identity-reveal
Render the 'brand identity reveal' format from a config. The signature is a single fixed poster frame in a REAL, softly-lit space (the illuminated poster-frame look of a boutique/cinema): a real wall, a real soft-focus plant in the lower-left corner, dappled leaf shadow. The artwork INSIDE the frame HARD-CUTS (no crossfade) through 11 beats — 10 on-brand poster mockups + a brand END CARD held ~3s. Music bed only, NO voiceover; copy is baked into each mockup, never overlaid as captions.
This capability is the FREE assembly only. The paid parts are separate generic
capabilities the recipe names — create-image-fal (the one-shot environment plate) and
create-music-elevenlabs (the bed). Never re-implement them here.
Three layers
- Environment plate (PAID,
create-image-fal, flux-pro ultra, 9:16) — an empty lit poster frame in a real space, high-res so the camera can be pushed closer by cropping. Pick a wall color that makes the brand's poster colors POP (complement of the dominant hue). - Mockups (FREE) — real-DOM HTML/CSS (
scene.html), one poster per beat, built from the brand's real assets + approved copy, frame-stepped via Playwright (render_art.py, bare mode, device_scale_factor 2). - Composite (FREE) —
measure_frame.pydetects the blank poster interior quad;composite.pyperspective-warps each poster into it AND multiplies the plate's real leaf-shadow/light back onto the poster (so it reads as behind glass) + a glass sheen;build_video.shsequences the frames with the music bed.
Inputs
config.json— copyscripts/config.example.jsonand edit (canvas, plate prompt + wall_style, camera crop, per-beat durations, end-card copy). Schema + per-file working-dir layout are documented inscripts/PIPELINE.md.- The brand's REAL product/packaging/lifestyle stills, wordmark, and icon (recreate the icon
as an SVG
<mask>if the only source has an occluding element). Approved copy only.
What ships with it
10 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_video.sh 1.2 KB runs code
- scripts/composite.py 3.0 KB runs code
- scripts/config.example.json 3.5 KB
- scripts/measure_frame.py 1.7 KB runs code
- scripts/PIPELINE.md 3.2 KB
- scripts/recrop.py 1.8 KB runs code
- scripts/render_art.py 1.3 KB runs code
- scripts/scene.html 20 KB
- skill.meta.json 333 B
- tests/smoke-test.md 1.1 KB
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.
- 13d ago First seen · 76 lines · 205 tokens per session scan A a708294dff55
render-brand-identity-reveal is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 205 tokens to every session and 1,179 once invoked, about $0.0010 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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