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-editorial-motion-podcastgit 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-editorial-motion-podcast)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-editorial-motion-podcast"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-editorial-motion-podcast/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-editorial-motion-podcast"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-editorial-motion-podcast.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 | $0.00171 | $0.01370 |
| Opus 5 | $0.00086 | $0.00685 |
| Sonnet 5 | $0.00034 | $0.00274 |
| Haiku 4.5 | $0.00017 | $0.00137 |
Grade A, and why
render-editorial-motion-podcast 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-editorial-motion-podcast
Assemble an editorial-motion podcast-clip ad from a config: a real clipped podcast audio line carries the whole narrative and every visual beat is timed to the sentence it describes, in a bold flat 2-tone editorial-illustration look ("a New Yorker spot-illustration that moves"). The motion is not generative video but deterministic ffmpeg ken-burns on static keyframes, so it reads as a printed page that moves. This capability is that FREE, deterministic assembly — the ffmpeg motion, hard-concat, audio mux, caption burn, and PIL end card.
scripts/config.example.json is the worked example (Klarify "Rat Park", ~40.8s 1080×1920
9:16, 6 beats); scripts/PIPELINE.md maps every config block to its source step and
scripts/README.md documents the free assembly.
Run
This is the FREE, deterministic assembly stage — it spends nothing on the motion layer.
The paid inputs are separate: the real podcast MP3 is clipped from source (free ffmpeg) with
its Whisper word timings, and one editorial-illustration keyframe per beat (chained ref images
so cage/character geometry holds) comes from create-image-fal (Nano Banana). Given the
clipped audio + words.json + the per-beat keyframes + the real brand wordmark PNG,
render-editorial-motion-podcast renders each keyframe as a ken-burns segment, hard-concats
on the beat, muxes the real audio, burns the mid-sentence captions, and composites the PIL end
card → the master. Re-cuts reuse the existing audio / keyframes and cost $0.
Contract (the free assembly)
- A spoken narration carries the whole spot — no generated SONG. Mux the provided
narration MP3 (
-map 0:v:0 -map 1:a:0) — a real clipped podcast line (preferred) OR an approved generated VO (create-vo-elevenlabs). Never a sung/generated track. (Clip-vs-generate is the recipe's STEP-0 intake decision — if no source episode is supplied, ASK the user.) - NO generative i2v — deterministic ffmpeg ken-burns only. Animate each static keyframe
with
zoompan(push-in / pull-back, 1.0→~1.06×, 24fps); Seedance/Kling are photoreal-trained and invent naturalistic middle states that collapse the 2-tone look. Never-loop 1withzoompan d=N(it balloons the duration); feed a single image and clamp with-t+trim. - Hard cuts on the beat — no crossfades. Crossfades ghost two drifting cages through each other; hard-concat each beat's segments and split long beats into micro-cuts (target 8–10 distinct visual moments). Each beat's visual STARTS within ~0.5s of its spoken line.
- Captions from Whisper word-timestamps, ON only mid-sentence. Burn
frosted-subtlecaptions while the speaker talks; leave silent/reflective beats and the end card uncaptioned. THREE mandatory rules (each bit us in prod — bake them in):- NON-OVERLAP — clamp every line to END before the next STARTS
(
end = min(last_word_end + ~0.15, next_start - 0.03)). Two boxes must never stack at the same spot; an end-tail bleeding into the next window is the #1 caption bug. - SAFE AREA — captions sit in the lower third, so the keyframe's subject must stay in the
upper ~75% (see the recipe's
look_pack.caption_safe_area). If a finished keyframe's subject intrudes into the caption band, deterministically shift the subject UP into the empty top space (PIL: paste up ~0.24H onto a canvas pre-filled with the exact paper color from a clean corner) — never let the box sit on the subject. - BURN ENGINE — prefer libass (
ass/subtitlesfilter), but checkffmpeg -filtersfirst: many builds (Homebrew) lack libass/drawtext. If absent, use the deterministic overlay fallback — render each line as a transparent PNG (frosted rounded box + white text, PIL) and composite via the ffmpegoverlayfilter with timedenable='between(t,st,en)'windows. Same look, no libass.
- NON-OVERLAP — clamp every line to END before the next STARTS
(
- End card via PIL from the real wordmark PNG — never AI-render brand text. The lockup is composited deterministically (stretched-gradient bg + feathered mascot crop + wordmark + tagline with a system font); a diffusion model garbles a wordmark ("therapits"). The video runs a ~1.5s silent hold past the audio on the end card (fade first/last 0.3s).
- FFmpeg composite, deterministic, FREE. Ken-burns each keyframe, hard-concat, mux the real audio, burn the captions, hold on the end card → a 1080×1920 h264+aac master. No paid calls.
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.
- 13d ago First seen · 67 lines · 171 tokens per session scan A 7e15e22b9bb7
render-editorial-motion-podcast is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 171 tokens to every session and 1,370 once invoked, about $0.0009 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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