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-proof-points-overlaygit 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-proof-points-overlay)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-proof-points-overlay"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-proof-points-overlay/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-proof-points-overlay"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-proof-points-overlay.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 | $0.00135 | $0.00809 |
| Opus 5 | $0.00068 | $0.00404 |
| Sonnet 5 | $0.00027 | $0.00162 |
| Haiku 4.5 | $0.00014 | $0.00081 |
Grade A, and why
render-proof-points-overlay 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 12d 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 — 25 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-proof-points-overlay
Build the deterministic PIL/FFmpeg overlays for the "Instagram comparison-tool reviewer" UGC ad — a white "we got a perfect 10/10 score" headline pill (trailing medal), an orange "but here's also why you'll love us" sub pill (trailing finger-down, width-matched to the header), and 3-4 green-check proof pills — then composite them onto a base clip in the format's signature diagonal cascade and mux the music into the master. FREE and deterministic: the pills are PIL-rendered so the score, checks, and wordmark stay pixel-crisp (a video model would smear type). The base clip (create-image-fal keyframe -> create-video-fal i2v) and the music bed (create-music-elevenlabs) come from separate paid capabilities; this one only does the free rendering.
Run
fetch_icons.py --run-dir ; build_overlays.py --config config.json --out-dir /generated/overlays ; compose_master.py --config config.json --run-dir — reads /generated/clip-handheld.mp4 + generated/music-bed.m4a, writes /master-final.mp4. 1080x1920, deterministic, $0. (Add --no-music to compose for a silent design preview.)
Scripts
fetch_icons.py— downloads the three Twemoji PNGs (medal 1f3c5, finger-down 1f447, check 2705) to<run>/assets/icons. PIL cannot render Apple Color Emoji, so pills paste Twemoji PNGs. Free, local.build_overlays.py— PIL: renders the white score header (trailing medal), the orange subhead (trailing finger-down, width-matched to the header), and N green-check proof pills auto-sized to their copy. Bold weight and icon-centered-on-pill-middle are load-bearing.compose_master.py— FFmpeg: scale/crop the base clip to 1080x1920@30, composite the always-on headers, cascade the proof pills (eachenable='gte(t,T)'on its own alternating LEFT/RIGHT row), mux the music, apply the anti-AI grain pass, re-encode crf23/maxrate12M -> master-final.mp4.
Contract
- Deterministic + FREE (PIL + FFmpeg); no paid calls, no AI-rendered text — the score, checks, and wordmark are composited, never generated.
- Config-driven off one
config.json(overlays,layout,duration_sec, optionalmusic/post_production); the template recipe supplies the config fromrecipe.config. - Always re-run
build_overlays.pybeforecompose_master.py— the compositor reads pre-rendered PNGs and silently reuses stale ones on a copy change. - Headers stay on 0-duration and must not cover the bottle face; proof pills cascade one-per-beat down the diagonal (NOT four-corners) — the cascade is the format's signature.
- Requires
Pillow+ffmpeg. No API keys.
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.
- 12d ago First seen · 25 lines · 135 tokens per session scan A 2456ae394947
render-proof-points-overlay is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 135 tokens to every session and 809 once invoked, about $0.0007 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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