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-myth-vs-factgit 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-myth-vs-fact)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-myth-vs-fact"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-myth-vs-fact/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-myth-vs-fact"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-myth-vs-fact.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 | $0.00258 | $0.02189 |
| Opus 5 | $0.00129 | $0.01094 |
| Sonnet 5 | $0.00052 | $0.00438 |
| Haiku 4.5 | $0.00026 | $0.00219 |
Grade A, and why
render-myth-vs-fact 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-myth-vs-fact
The free, deterministic renderer for the myth-vs-fact video ad format — the calm, sound-off-safe kinetic-typography explainer that busts N common myths and hands the viewer a credible resolution. Red-strike MYTH cards flip to teal-check FACT cards over a calm-authority VO, then a "what actually works" turn + an optional proof reveal + a punch line + a static brand end card.
Every on-screen word is a deterministic HTML hyperframe — no AI image/video gen, no b-roll, no character. Visuals cost $0. The only metered spend is upstream (VO + Whisper word-timestamps + an optional music bed), gated to its own capabilities. This capability OWNS the whole FREE assembly: beat-snap → render → captions → mix → burn → master. Iterate the cut for free; re-roll only the offending paid audio beat.
It ports the validated build from the Clinikally "acne myths" run — brand-neutralised,
config-driven, and portable (no /Users, no clients/; everything via --config +
--work-dir).
SOUND-OFF SAFE is the whole point: every claim is legible on-screen and the VO only reinforces it. VO-FIRST: render the VO, extract Whisper word onsets on the RENDERED audio, then snap every beat boundary + strike wipe + reveal to those onsets.
The 8-beat spine (roles)
hook → 3× myth-fact (the flip triad — identical grammar so it reads as a pattern) →
turn (the "what actually works" pivot) → proof (optional actives/proof reveal, omit if
empty) → punch (full-frame closer) → end-card (the static brand PNG). Each beat carries
its role, duration, and its copy; ONE role template renders any pair.
Scripts (free — Python + Playwright + ffmpeg, no paid calls)
scripts/beat_snap.py— VO-first alignment. FAL Whisper word-timestamps on the RENDERED VO → re-snap every beat boundary to the nearest word onset. Writesbeat-manifest.json+whisper/words-flat.jsoninto the work dir.--no-whisperkeeps the config durations un-snapped for a fully offline run.scripts/render_beats.py— the deterministic renderer. Permgbeat: pick the role template underhyperframes/, inject the beat's copy + palette + fonts aswindow.BEAT, drivewindow.renderAt(t)frame-by-frame via Playwright, screenshot each frame → ffmpeg at EXACTLY the configured fps (default 25/1). Theend-cardbeat is built from the pre-supplied brand PNG (scale/crop + a ~0.35s fade-up) — never generated per run.scripts/make_captions.py— karaoke.assfrom the manifest + Whisper words. ≤3 words per cue; close on a >0.4s gap / beat-window edge / sentence-ending punctuation. Captions are burned ONLY in caption-allowed windows; the proof + end-card beats are suppressed.scripts/compose.py— the assembler: concat the beats → mix VO + optional music (music −20 dB,amix normalize=0, ~0.8s tail fade) → burn the.assLAST → master mp4.scripts/config.example.json— the shape of the brandconfigthe recipe binds (the brand-neutralised Clinikally values as a worked reference).scripts/hyperframes/— the bundled hyperframe scaffold:_shared.css(palette-tokened tokens + card/tag/fact/pill/chain type),_shared.js(theinitRenderer/springScale/buildLineStrikes+strikeLinesper-line-strike /popIn/revealWordshelpers + config injection), and one template per role (beat-hook.html,beat-myth-fact.html,beat-turn.html,beat-proof.html,beat-punch.html).
What ships with it
14 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/beat_snap.py 6.1 KB runs code
- scripts/compose.py 6.8 KB runs code
- scripts/config.example.json 4.0 KB
- scripts/hyperframes/_shared.css 6.7 KB
- scripts/hyperframes/_shared.js 7.8 KB runs code
- scripts/hyperframes/beat-hook.html 3.2 KB
- scripts/hyperframes/beat-myth-fact.html 4.1 KB
- scripts/hyperframes/beat-proof.html 2.9 KB
- scripts/hyperframes/beat-punch.html 1.2 KB
- scripts/hyperframes/beat-turn.html 1.9 KB
- scripts/make_captions.py 4.6 KB runs code
- scripts/render_beats.py 7.3 KB runs code
- skill.meta.json 315 B
- tests/smoke-test.md 5.6 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 · 113 lines · 258 tokens per session scan A 0fb8c64a10f2
render-myth-vs-fact is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 258 tokens to every session and 2,189 once invoked, about $0.0013 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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