Claude Skills is a library of reusable capability packages that give Claude task-specific instructions, terminology, and output formats for building, launching, operating, and growing websites and brands. People working on branding, design, content, SEO, development, operations, growth, or research can use the skills across different website stacks. The catalogue contains selected skills and plugins from the library.
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 rampstackco/claude-skills --skill media-asset-managementgit clone --depth 1 https://github.com/rampstackco/claude-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/rampstackco/claude-skills/media-asset-management)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/media-asset-management"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/media-asset-management/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/rampstackco/claude-skills/media-asset-management"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/media-asset-management.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 | $0.00111 | $0.02722 |
| Opus 5.5 | $0.00044 | $0.01089 |
| Sonnet 5.5 | $0.00022 | $0.00544 |
| Haiku 4.5 | $0.00011 | $0.00272 |
Grade A, and why
media-asset-management 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 1mo 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Media Asset Management
Design how images, video, and downloadable files get stored, processed, organized, and served. Stack-agnostic. The principles apply whether you're running a custom pipeline or using a hosted service.
When to use
- Designing or redesigning the image and media pipeline
- Choosing a media CDN or image service
- Setting up responsive image delivery
- Planning a digital asset management (DAM) system
- Auditing media performance issues
- Picking video hosting and embedding strategy
- Setting up workflows for designers and writers to upload assets
- Migrating media from one platform to another
When NOT to use
- Performance optimization beyond media (use
performance-optimization) - Brand identity or photography direction (use
brand-identity,art-direction) - Content production strategy (use
content-strategy) - Single-image optimization (covered in
performance-optimization)
Required inputs
- Current media inventory: where assets live, in what formats
- Volume: how many assets, how much storage, how much traffic
- Sources: who creates and uploads media (designers, writers, automated tools)
- Platforms: where media is consumed (web, email, app, partners)
- Performance baseline: current image sizes, load times
- Budget reality: hosted services have monthly costs
The framework: 4 stages
The media pipeline has four stages. Each has its own decisions.
Stage 1: Source
Where assets enter the system.
Sources:
- Designers (Figma exports, Photoshop, Illustrator)
- Photographers (RAW or JPEG from camera)
- Stock photo libraries
- AI-generated images
- User-generated content (uploads)
- Automated systems (e.g., screenshots, generated thumbnails)
At source, decide:
- File formats accepted (RAW, TIFF, PSD, AI vs delivered formats)
- Naming conventions
- Required metadata (alt text, captions, credits, rights)
- Maximum source resolution (high enough to derive any size; not so high it's wasteful)
- Where source files live (separate from delivered assets)
What ships with it
2 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.
- 1mo ago First seen · 312 lines · 111 tokens per session scan A 85055e955d5b
media-asset-management is a skill published in the GitHub repository rampstackco/claude-skills (1,001 stars, last pushed yesterday), licensed MIT. It adds 111 tokens to every session and 2,722 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
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