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 cachinggit 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/caching)<a href="https://agentmods.dev/skills/proyecto26/system-design-skills/caching"><img src="https://agentmods.dev/badge/skills/proyecto26/system-design-skills/caching.svg" alt="Measured on agentmods" 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 | $0.00111 | $0.02376 |
| Opus 5 | $0.00056 | $0.01188 |
| Sonnet 5 | $0.00022 | $0.00475 |
| Haiku 4.5 | $0.00011 | $0.00238 |
Grade A, and why
caching 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 8d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Caching
Put a copy of hot data closer to the reader so most requests skip the slow path. Caching is the highest-leverage move for read-heavy systems — and the easiest to get subtly wrong, because a cache adds a second source of truth that can serve stale or wrong data, and can amplify an outage when it misbehaves.
When to reach for this
Reads dominate (a high read:write ratio from back-of-the-envelope); the same
data is read repeatedly; the datastore is the read bottleneck; or recomputation
is expensive. A cache buys read latency and offloads the origin.
When NOT to
Write-heavy or read-once data (low hit rate — pure overhead). Data that must be
exactly current with zero staleness (a cache is a stale copy by nature; when
strict freshness is required, go to the source or use consistency-coordination). Don't
add a cache before a number shows reads are the problem (YAGNI) — it's a new
failure mode and a second thing to operate.
Clarify first
- Read:write ratio and hit rate — is the working set cacheable? (→
back-of-the-envelope, 80/20.) - Staleness tolerance — seconds? minutes? must reads see their own writes?
- Working-set size — does the hot set fit in RAM across cache nodes?
- Consistency on write — can the cache briefly disagree with the store?
- Eviction trigger — what's the access pattern (recency? frequency? time-bound?).
The options
Where to cache (often layered): client/browser → CDN edge (→ content-delivery)
→ application/in-process → distributed cache (Redis/Memcached) → database buffer
pool. This skill focuses on the application and distributed layers.
Read strategy
- Cache-aside (lazy): app checks cache, on miss reads the store and populates. Use when reads are unpredictable; the default for most systems.
- Read-through: the cache library fetches from the store on miss. Use to keep app code simple and caching policy centralized.
Write strategy
- Write-through: write cache and store synchronously. Use when reads right after writes must be fresh and slower writes are acceptable.
- Write-back (write-behind): write cache now, flush to store async. Use for write-heavy/bursty paths that tolerate a small loss window.
- Write-around: write only the store; let the cache fill on read. Use when written data is rarely re-read soon (avoids cache churn).
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.
- 8d ago First seen · 157 lines · 111 tokens per session scan A 8a61154831c9
caching is a skill published in the GitHub repository proyecto26/system-design-skills (69 stars, last pushed 3mo ago), licensed MIT. It adds 111 tokens to every session and 2,376 once invoked, about $0.0006 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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