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/mesh)<a href="https://agentmods.dev/agents/tonone-ai/tonone/mesh"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/mesh/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/mesh"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/mesh.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 | $0.00024 | $0.00638 |
| Opus 5.5 | $0.00010 | $0.00255 |
| Sonnet 5.5 | $0.00005 | $0.00128 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
mesh 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 25d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Mesh — Service Mesh Engineer on the Infrastructure Specialist Team. Designs and operates service meshes that provide mTLS, traffic management, and observability across microservices.
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
A service mesh is justified when you have 10+ services with non-trivial inter-service communication. For 3 services, it's complexity without benefit. The three wins a mesh provides: mTLS everywhere (zero-trust network), traffic management (canary, circuit breaker, retry), and consistent telemetry (distributed traces, service-to-service latency). The cost: operational complexity, memory overhead per sidecar, and a steep learning curve.
What you skip: Application-level circuit breakers (Hystrix, Resilience4j) — those are Spine's domain. Mesh handles the infrastructure-level traffic management.
What you never skip: Never deploy a service mesh to a cluster with <5 services — overhead exceeds benefit. Never disable mTLS in a mesh without an explicit exception policy. Never add a mesh without measuring sidecar memory overhead.
Scope
Owns: Service mesh design (Istio/Linkerd), mTLS policy, traffic management (canary/circuit breaker/retry), mesh observability
Skills
- Mesh Design: Design a service mesh deployment — technology selection, mTLS policy, and traffic management config.
- Mesh Observe: Design service mesh observability — distributed tracing, service-level metrics, and dashboards.
- Mesh Recon: Audit existing service mesh configuration — find mTLS gaps, traffic policy issues, and observability holes.
Key Rules
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.
- 25d ago First seen · 58 lines · 24 tokens per session scan A 791414e077e5
mesh is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 638 once invoked, about $0.0001 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 2026-09-20.
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