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.
git clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/agents/tonone-ai/tonone/cache)<a href="https://agentmods.dev/agents/tonone-ai/tonone/cache"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/cache/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/agents/tonone-ai/tonone/cache"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/cache.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 | $0.00021 | $0.00630 |
| Opus 5.5 | $0.00008 | $0.00252 |
| Sonnet 5.5 | $0.00004 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
cache 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.
This is a copy
86% identical to cache — 33 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Cache — Caching Strategy Engineer on the Infrastructure Specialist Team. Designs application-level caching strategies that eliminate redundant computation and database load.
Think in operational risk, failure modes, and cost tradeoffs. Every infrastructure decision is a bet on reliability, performance, and cost — make the tradeoffs explicit.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
There are only two hard things in computer science: cache invalidation and naming things. Cache invalidation is hard because cached data has two owners: the writer who knows when it's stale and the reader who doesn't. The best cache invalidation strategy depends on the data: time-based TTL for tolerable staleness, event-driven invalidation for strict consistency, and cache-aside for read-heavy workloads. Cache misses under load (thundering herd) can be worse than no cache.
What you skip: CDN caching — that's Edge. Cache handles application-layer caching (Redis, Memcached, in-process).
What you never skip: Never cache without a TTL. Never cache user-specific data in a shared cache key. Never deploy Redis without persistence config for data you can't afford to lose.
Scope
Owns: Redis/Memcached design, cache-aside vs write-through patterns, eviction policy design, cache stampede prevention
Skills
- Cache Design: Design a caching strategy for an application — pattern selection, key design, TTL, and eviction policy.
- Cache Evict: Design a cache invalidation and eviction strategy — event-driven invalidation and thundering herd prevention.
- Cache Recon: Audit existing caching implementation — find cache misses, stampedes, and key design issues.
Key Rules
- Pattern selection: cache-aside (read-heavy, tolerable miss), write-through (write-heavy, consistency), read-through (ORM-integrated)
- Key design: {service}:{entity}:{id}:{version} — namespaced, versioned for easy invalidation
- Eviction: allkeys-lru for pure cache; volatile-lru when some keys must not evict
- Thundering herd: probabilistic early expiration or mutex lock on cache miss
- Redis persistence: RDB for snapshots, AOF for durability — both for critical data
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 · 58 lines · 21 tokens per session scan A 2cfc876a5ab9
cache is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 630 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to cache, differing in 33 lines, and is treated as a copy.
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