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-logo-equation-cardgit 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-logo-equation-card)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-logo-equation-card"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-logo-equation-card/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-logo-equation-card"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-logo-equation-card.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00166 | $0.01205 |
| Opus 5.5 | $0.00066 | $0.00482 |
| Sonnet 5.5 | $0.00033 | $0.00241 |
| Haiku 4.5 | $0.00017 | $0.00120 |
Grade A, and why
render-logo-equation-card 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 18d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-logo-equation-card
A card-led format: everything the viewer reads is on a static black card in the top third; under it, b-roll moves like a timelapse to stop the scroll. One animated event: the CTA line hard-cuts in at 2.0s. No voice, no captions, no cuts.
Run
python render_card.py card.json --out-dir work/ # card-no-cta.png, card-with-cta.png
python compose.py --broll broll.mp4 --cards work/ --out ad.mp4 --in 3 --speed 4 [--bed bed.mp3]
python measure_motion.py ad.mp4 --cta-at 2.0 # exit 1 = reject
card.json:
{"logos": ["partner-icon.png", "brand-icon.png"],
"partner": {"name": "Slack", "integration_confirmed": true},
"headline": ["= your standup, written", "before anyone opens Slack"],
"steps": ["1. Connect your Slack workspace", "2. ...", "3. ...", "4. ..."],
"cta": "Try it free at example.com"}
The Slack standup card above is an illustration of the schema, not a default: the headline, the partner and the steps come from the user's brand and choices.
Choices
The recipe asks these before any paid step; this renderer only draws what card.json and the
flags say. The demo's value is an example, never a default:
headline_angle— what the "= …" headline promises (time saved, a pain removed, a new ability, one workflow in one place). Setscard.jsonheadline+steps. Demo: time saved ("= your standup, written before anyone opens Slack").broll_subject— what the timelapse b-roll shows (the product in use, the team working, a workspace, customers). Picks the--brollclip and window. Demo: the brand's own footage of work happening.music— the optional--bed(built from the music choice, instrumental, no artist names) or none (ship silent). Demo: driving minimal electronic, ~144 BPM.
Guardrails (enforced by render_card.py)
Two logos side by side already imply a link between the companies. The platform names another brand only as plain co-existence ("works with"), never as an endorsement, partnership or ranking. So:
What ships with it
6 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.
- 18d ago Changed · +17 lines 0895734188b6
- 19d ago First seen · 67 lines · 166 tokens per session scan A fc5ed4a9614e
render-logo-equation-card is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,254 stars, last pushed today), licensed MIT. It adds 166 tokens to every session and 1,205 once invoked, about $0.0007 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 2026-09-29.
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