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 2233admin/design-pipeline --skill wrapped-videogit clone --depth 1 https://github.com/2233admin/design-pipelineWrote 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/2233admin/design-pipeline/wrapped-video)<a href="https://agentmods.dev/skills/2233admin/design-pipeline/wrapped-video"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/wrapped-video/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/2233admin/design-pipeline/wrapped-video"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/wrapped-video.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 | $0.00121 | $0.03214 |
| Opus 5 | $0.00060 | $0.01607 |
| Sonnet 5 | $0.00024 | $0.00643 |
| Haiku 4.5 | $0.00012 | $0.00321 |
Grade A, and why
wrapped-video 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 6d 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.
This is a copy
100% identical to wrapped-video — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wrapped Video
Build a "Spotify Wrapped"-style recap: take a row of data about one person (or account, team, year) and turn it into a punchy, shareable vertical video. The core idea is one template × a data table → many personalized videos. Write the template once, then render a unique film for every row.
When to use
- Year-in-review / "your 2026 wrapped" recaps for any product with per-user stats.
- Personalized data videos: fitness year, reading year, spending recap, gaming stats, sales rep recap, student progress.
- Any time the deliverable is "the same video, but with each person's numbers" — at 1 or 100,000 copies.
This is a data → share-bait pattern, not a hand-edited film. If there is no data table (or no per-record data), use a different skill.
The two non-negotiables
- Data drives everything. Every headline, number, name, and color comes from props, never hardcoded. A scene that can't be filled from a data row does not belong in a Wrapped.
- Built to be screenshotted. Each scene must read in under 2 seconds and look good frozen — that frozen frame is what gets shared to a story. Design for the pause, not the play.
The Wrapped scene grammar
A Wrapped is a fixed sequence of short scene types, each ~2.5–4s. Pick 5–7 and order them as a build. Same grammar every year; only the data and palette change.
| Scene type | Job | Data shape |
|---|---|---|
| Intro / "Your 2026, wrapped" | Brand the moment, set palette | name, year |
| Big-number reveal | One hero stat, counts up huge | one number + unit + label |
| Top-X list | Ranked 1→5, staggered in | array of {rank, label, value} |
| Superlative / persona | "You're in the top 1%", an archetype | computed tier/label |
| Comparison | "more than 92% of listeners" | percentile or ratio |
| Time/heatmap | "your busiest month was March" | series or peak |
| Outro / share card | Logo + handle + CTA, holds still | name, handle |
Order as a crescendo: small context first, biggest/most personal stat as the climax, then the still share card. See references/scene-grammar.md for a full 7-scene storyboard with timings.
What ships with it
4 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.
- 6d ago First seen · 208 lines · 121 tokens per session scan A 9c5b20860474
wrapped-video is a skill published in the GitHub repository 2233admin/design-pipeline (9 stars, last pushed 7d ago), licensed MIT. It adds 121 tokens to every session and 3,214 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to wrapped-video, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
designlang-tokens
Use when styling UI for cal.com — references the extracted design system tokens instead of inventing colors, spacing, or typography.
omd-apply
A skill that applies a project's DESIGN.md file as visual and brand guidance for interface work. It covers components, colors, fonts, layout, copy, motion, and visual assets.
ss-review
Review UI code for design system compliance, accessibility, and best practices.
ss-pattern
Generate a composed UI pattern from the active StyleSeed grammar and brand recipe using existing primitives.
ss-reference
Compile screenshots, URLs, Figma exports, or an existing UI into a project-local StyleSeed output grammar with evidence, tokens, confidence, anti-patterns, and a validation screen. Use when the user supplies a design reference that StyleSeed does not already model.
cicd-integration
Generate CI/CD pipeline configurations that automate design system quality checks — token validation, component linting, visual regression, accessibility scanning, and release gating. Produces ready-to-use pipeline files for GitHub Actions, GitLab CI, CircleCI, or Bitbucket Pipelines, configured to enforce the…