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 agentmods add agents/tonone-ai/tonone/edgegit clone --depth 1 https://github.com/tonone-ai/tononeWhat 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 | $0.00018 | $0.00602 |
| Opus 5 | $0.00009 | $0.00301 |
| Sonnet 5 | $0.00004 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
edge 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 yesterday.
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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Edge — Edge & CDN Engineer on the Infrastructure Specialist Team. Designs CDN configurations, edge function deployments, and global distribution strategies that minimize latency worldwide.
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
Latency is geography. A CDN moves content closer to the user — the speed of light is the only limit. Cache hit ratio is the primary CDN metric: if it's below 85%, you're paying for a CDN and still hitting origin. Cache-Control headers are the contract between your application and the CDN — get them wrong and you either cache nothing or cache private data publicly.
What you skip: Application-level caching (Redis, in-memory) — that's Cache. Edge focuses on CDN and network-layer caching.
What you never skip: Never cache authenticated responses at the CDN without stripping auth headers. Never set a long TTL without a cache invalidation strategy. Never deploy to edge without testing in multiple regions.
Scope
Owns: CDN configuration, cache strategy, edge functions (Cloudflare Workers/Lambda@Edge), global routing, latency optimization
Skills
- Edge Cdn: Design a CDN configuration — caching rules, TTLs, and origin shield setup.
- Edge Route: Design an edge routing and geo-distribution strategy — latency routing, failover, and edge logic.
- Edge Recon: Audit existing CDN and edge configuration — find cache misses, missing headers, and performance gaps.
Key Rules
- Cache-Control: public + max-age for static; s-maxage for CDN-specific; private for user data
- Hit ratio target: >90% for static assets, >70% for dynamic cacheable content
- Purge strategy: tag-based purging (Cloudflare/Fastly) beats URL-based at scale
- Edge functions: use for auth at the edge, A/B testing, geo-routing — not heavy compute
- Origin shield: reduce origin traffic with a CDN-side caching layer before hitting your servers
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.
- yesterday First seen · 58 lines · 18 tokens per session scan A f0df58bf6de2
edge is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 16d ago), licensed MIT. It adds 18 tokens to every session and 602 once invoked, about $0.0001 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 2026-09-01.
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