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 proyecto26/system-design-skills --skill content-deliverygit clone --depth 1 https://github.com/proyecto26/system-design-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/proyecto26/system-design-skills/content-delivery)<a href="https://agentmods.dev/skills/proyecto26/system-design-skills/content-delivery"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/content-delivery/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/proyecto26/system-design-skills/content-delivery"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/content-delivery.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 | $0.00132 | $0.03077 |
| Opus 5 | $0.00066 | $0.01538 |
| Sonnet 5 | $0.00026 | $0.00615 |
| Haiku 4.5 | $0.00013 | $0.00308 |
Grade A, and why
content-delivery 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 10d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Delivery
Push bytes to the network edge so requests terminate close to the user and never reach the origin. A CDN is the outermost cache layer of a system: get it right and most static/media traffic and a chunk of latency vanish before they hit your servers; get it wrong and you serve stale assets, leak origin load, or pay egress twice.
When to reach for this
The same files (images, video, JS/CSS bundles, downloads, fonts) are read
repeatedly by a geographically spread audience; the origin or its bandwidth is the
bottleneck for static reads; or cross-region latency on first byte hurts (a
cross-continent round trip is ~100 ms — see back-of-the-envelope). A CDN buys
latency and origin offload at once.
When NOT to
Highly personalized, per-request dynamic responses with no cacheable shape (a CDN
adds a hop and caches nothing). Tiny single-region audiences where the origin
already serves reads comfortably (YAGNI — a CDN is another vendor, another bill,
another invalidation problem). Strictly fresh data that cannot tolerate any
staleness window — that belongs at the origin or behind consistency-coordination,
not a TTL-based edge. Naming a CDN before a number shows static reads or geography
is the problem is a red flag.
Clarify first
- Content mix — what fraction is cacheable static/media vs uncacheable dynamic/personalized? (Only the cacheable part benefits.)
- Update cadence & staleness budget — how often do assets change, and how stale may an edge copy be? (Drives TTL and invalidation strategy.)
- Geography — where are users, and how concentrated? (Decides whether edge PoPs and geo-routing matter at all.)
- Object size & egress volume — average asset size × requests = egress; this
sizes the bill and the offload (→
back-of-the-envelope). - Origin shape — object store (S3/GCS/blob) or dynamic app server? Can it survive a cold-cache stampede if the edge flushes?
The options
Distribution model — how content reaches the edge
- Pull (origin-pull): the edge fetches on first miss, caches per TTL, serves the rest. Use when traffic is high and content is large or churny — the edge holds only what's actually requested. The default for most systems.
- Push: you upload assets to the CDN ahead of demand and rewrite URLs. Use when the catalog is small/static or launch spikes can't tolerate a cold first-miss (you pre-warm); you accept managing storage and uploads yourself.
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.
- 10d ago First seen · 201 lines · 132 tokens per session scan A b3a0b2d3136b
content-delivery is a skill published in the GitHub repository proyecto26/system-design-skills (69 stars, last pushed 3mo ago), licensed MIT. It adds 132 tokens to every session and 3,077 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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