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-video --skill imessage-video-adgit clone --depth 1 https://github.com/gooseworks-ai/goose-videoWrote 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-video/imessage-video-ad)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-video/imessage-video-ad"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-video/imessage-video-ad/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-video/imessage-video-ad"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-video/imessage-video-ad.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00095 | $0.04561 |
| Opus 5 | $0.00048 | $0.02280 |
| Sonnet 5 | $0.00019 | $0.00912 |
| Haiku 4.5 | $0.00010 | $0.00456 |
Grade A, and why
imessage-video-ad 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
imessage-video-ad
Use this skill when an ad concept calls for an authentic iMessage thread reveal: someone screenshots a product/result, sends it to a friend, the friend reacts and asks what app/service it is, and the conversation surfaces the brand + a CTA code.
Everything this skill needs is bundled in this folder — the iMessage HTML renderer (scripts/generate.js + scripts/templates/), both end-card templates, the Apple SFX (assets/sfx/), and the example thread/timeline. No external skill dependencies.
Setup (once)
npm install # Playwright
npx playwright install chromium # browser binary
pip install -r requirements.txt # FAL client (flat-lay background step only)
cp .env.example .env # add FAL_KEY if generating the flat-lay bg
ffmpeg and ffprobe must be on your PATH. All scripts run in place and take a --project <dir> so one copy of the skill drives many ads.
Output format — FRAMED (Variant B) is the standard
Renders the framed look by default: a visible iPhone bezel + Dynamic Island + status bar, sitting on a flat-lay desk background (1080×1920). The old full-bleed (edge-to-edge chat) look is DEPRECATED — reachable only via --full-bleed for backward reproduction.
Framed requires a background at <project>/assets/flat-lay-bg.jpg — generate it with scripts/gen_flat_lay_bg.py (State 3.5) or supply your own. Dark threads (theme: "dark") render dark natively in frame mode, so no manual theme injection is needed.
When to use
- Reaction/discovery ads where the punchline is the recipient's curiosity ("what app is that?", "wait this is real?")
- Promo-code reveals (FREEPACK / FIRSTPACK / WELCOME10) — the conversational delivery feels far less ad-like than a hard CTA card alone
- Any time the brief mentions "fake DM", "screenshot of a chat", "iMessage style", "Tyler/peer"
This skill is for a multi-bubble timeline animated to video. If the chat is just one static beat (a single screenshot), you don't need video — render the HTML once and screenshot it.
What ships with it
24 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.
- .env.example 558 B
- assets/sfx/imessage-receive.mp3 12 KB
- assets/sfx/imessage-send.mp3 4.2 KB
- examples/full-thread.example.json 2.1 KB
- examples/kickoff-card-flex.md 1.8 KB
- examples/strava-card.example.html 5.6 KB
- examples/timeline.example.json 2.9 KB
- package-lock.json 1.6 KB
- package.json 348 B
- README.md 4.2 KB
- references/audio-recipe.md 4.0 KB
- references/end-card-recipe.md 4.7 KB
- references/timeline-schema.md 3.2 KB
- requirements.txt 56 B
- scripts/end-card-photo-bg.html 3.1 KB
- scripts/end-card.html 5.5 KB
- scripts/endcard-to-mp4.sh 1.5 KB runs code
- scripts/gen_flat_lay_bg.py 4.3 KB runs code
- scripts/generate.js 17 KB runs code
- scripts/record-master.js 18 KB runs code
- scripts/render-end-card.js 4.8 KB runs code
- scripts/stitch.sh 3.8 KB runs code
- scripts/templates/chat.css 19 KB
- scripts/templates/icons.js 3.3 KB runs code
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 · 239 lines · 95 tokens per session scan A bc91e0c8d990
imessage-video-ad is a skill published in the GitHub repository gooseworks-ai/goose-video (16 stars, last pushed 3mo ago), licensed MIT. It adds 95 tokens to every session and 4,561 once invoked, about $0.0005 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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