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-podcast-skitgit 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-podcast-skit)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-podcast-skit"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-podcast-skit/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-podcast-skit"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-podcast-skit.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 | $0.00203 | $0.01053 |
| Opus 5 | $0.00102 | $0.00526 |
| Sonnet 5 | $0.00041 | $0.00211 |
| Haiku 4.5 | $0.00020 | $0.00105 |
Grade A, and why
render-podcast-skit 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-podcast-skit
Assemble a two-host fake-podcast skit ad from a config: a skeptic and a believer at an absurd themed podcast desk do a snappy back-and-forth about the product (the set is deliberately unrelated — that is the joke). Each line is its own lipsync clip so the edit can cut on the dialogue beat (~1.8s avg); this capability is the FREE, deterministic assembly that concatenates those clips, renders the WHITE captions, and appends the brand end card.
scripts/config.example.json is the worked example (Ladder run-02 "Laundromat 2am", ~49s
1080×1920 9:16, ~22 lines); 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. The paid inputs are
separate capabilities: one ElevenLabs with-timestamps VO per line (one voice per host) via
create-vo-elevenlabs; two photoreal base stills at the themed desk plus ~10 expression variants
(mouths NEUTRAL/CLOSED, gpt-image-2 quality=high, not nano-banana) via
create-image-gpt-image-fal; and one lipsync clip per (still, VO) pair via create-video-fal.
Given the per-line clips + their VO timestamps + the brand wordmark SVG, render-podcast-skit
walks the scenes in script order, builds the global caption timeline, renders the WHITE captions,
hard-concats the clips, auto-appends the end card, and final-encodes crf28 → the master. Re-cuts
reuse the existing VOs / stills / clips and cost $0.
Contract (the free assembly)
- Dialogue-carried, no music bed by default. The per-line VO is the audio; a podcast skit needs no music (an optional low ambience is a taste call, off by default).
- One line = one scene = one hard cut, in script order. Hard-concat the per-line clips in order (scale/pad to 1080×1920, re-encode) — no dissolves.
- Captions from the VO's OWN char-level timestamps, not Whisper (script-window). Build a
global
words.jsonby offsetting each line's char-level word timings by the cumulative clip start, group into ≤5-word cues broken on sentence-final punctuation, and render WHITE#FFFFFFbottom-center captions (black outline), word-wrapped to stay in-frame and held ≥0.9s — PIL PNG overlays when the host ffmpeg lacks libass (common), else ASS. (Yellow 3-word karaoke was the old style, rejected in testing.) Whisper on the rendered clips mistimes; the VO timestamps are ground truth. - End card via Playwright/PIL from the real wordmark — never AI-render brand text. The lockup is a deterministic HTML → PNG → 2.5s mp4 from the brand's real wordmark SVG (black bg, brand wordmark, CTA pill, URL), auto-appended after the last line. A diffusion model garbles a wordmark.
- FFmpeg composite, deterministic, FREE. Concat the clips, overlay the WHITE caption PNGs (or
burn ASS via libass), append the end-card mp4, and final-encode
-preset slow -crf 28+ aac 96k → a 1080×1920 h264+aac master (~6MB for ~28s; the old-crf 20produced ~16MB). No paid calls, no 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.
- 13d ago First seen · 52 lines · 203 tokens per session scan A a1d65b496860
render-podcast-skit is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 203 tokens to every session and 1,053 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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