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-chatgpt-chatgit 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-chatgpt-chat)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-chatgpt-chat"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-chatgpt-chat/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-chatgpt-chat"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-chatgpt-chat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 45 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 122 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00148 | $0.02130 |
| Opus 5 | $0.00074 | $0.01065 |
| Sonnet 5 | $0.00030 | $0.00426 |
| Haiku 4.5 | $0.00015 | $0.00213 |
Grade A, and why
render-chatgpt-chat 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-chatgpt-chat
The free renderer for the chatgpt-chat video ad format — the "I just asked ChatGPT…" creative, where someone asks ChatGPT a question and the streamed assistant answer is the punchline (the brand surfacing as the natural response). Deterministic Playwright + ffmpeg assembly; no generative video of the UI, so the bubble text and streamed answer stay pixel-crisp.
This is the ChatGPT sibling of render-imessage-chat. Reach for this one when
ChatGPT is the more credible host for the answer; reach for iMessage when the
punchline is a peer's reaction in a DM. The template recipe (DB) supplies the
per-brand thread + timeline + end_card config and gates the paid music call
(music bed → create-music-elevenlabs) to its own capability.
What it renders
One continuous take — never scene-by-scene (every reload flickers):
- User types in the composer with the iOS keyboard up (
composer-type). - Send-tap is ONE beat — the user bubble pops, the keyboard slides down, and
the header right-cluster swaps (
personPlus/dottedCircle→edit/more) all on the samet. Never sequence them across frames. - One gray loading dot holds ~500ms (never three — three reads as iMessage typing, wrong app), silently (no SFX on the dot).
- The assistant answer streams in word-by-word (
stream-words, ~7 wps) with a soft opacity ramp; the conversation auto-scrolls to keep it in view. - Crossfade to a designed end card (wordmark + ⭐ proof row + trust trio + CTA pill) and mux a ducked music bed → master MP4.
The chat records at the ChatGPT-native ~9:19.5 (default 750×1624) to match a real iPhone screen recording. Never stretch the chat to a different aspect ratio — the end card is scaled-to-fit + padded to the chat's dimensions in stitch, so the chat is never touched.
Run
cd scripts && npm install # once — installs Playwright
npx playwright install chromium # once
node record-chat.js --config config.json --out-dir <work> # → master-chat.mp4 + .sfx.json
node render-end-card.js --config config.json --out-dir <work> # → scene-end-endcard.mp4
bash stitch.sh --chat <work>/master-chat.mp4 --end <work>/scene-end-endcard.mp4 \
--sfx <work>/master-chat.sfx.json --out <work>/master-final.mp4 \
--pad-color "#ffffff" [--music <work>/music-bed.mp3] [--also-1x1]
What ships with it
15 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.
- assets/sfx/key-tap.wav 3.1 KB
- assets/sfx/response-done.wav 14 KB
- assets/sfx/send-tap.wav 7.8 KB
- assets/sfx/stream-tick.wav 2.0 KB
- scripts/config.example.json 3.1 KB
- scripts/end-card.template.html 2.5 KB
- scripts/mockup/generate.js 16 KB runs code
- scripts/mockup/templates/chat.css 17 KB
- scripts/mockup/templates/icons.js 12 KB runs code
- scripts/package.json 320 B
- scripts/record-chat.js 15 KB runs code
- scripts/render-end-card.js 6.2 KB runs code
- scripts/stitch.sh 6.6 KB runs code
- skill.meta.json 315 B
- tests/smoke-test.md 2.4 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.
- 12d ago First seen · 134 lines · 148 tokens per session scan A cfcee7d7fa92
render-chatgpt-chat is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 148 tokens to every session and 2,130 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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