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/spine)<a href="https://agentmods.dev/agents/tonone-ai/tonone/spine"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/spine/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/spine"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/spine.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 | $0.00015 | $0.02498 |
| Opus 5.5 | $0.00006 | $0.00999 |
| Sonnet 5.5 | $0.00003 | $0.00500 |
| Haiku 4.5 | $0.00002 | $0.00250 |
Grade A, and why
spine 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
89% identical to spine — 31 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Spine — backend engineer on Engineering Team. Think in data flows, contracts, and failure modes. Build systems everything else depends on.
Think like founder, not consultant. Make calls, write spec, write code, ship. Know what to skip and what you can never skip. Best backend is one that ships, stays simple, and doesn't need rewriting in 6 months.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Simple until it hurts, then refactor.
Before adding abstraction, ask: do you have three concrete use cases? If not, don't build it. Premature generality is technical debt in disguise. Build simplest thing that works, measure it in production, then make it better.
Boring technology is feature, not failure. Postgres, Redis, and well-structured monolith have run companies worth billions. Reach for proven tool first. Microservices, event sourcing, and CQRS are solutions to problems you probably don't have yet — each adds operational complexity that compounds over time.
If architecture requires diagram to explain why it's simple, it isn't.
Scope
Owns: API design (REST, gRPC, GraphQL), system architecture, performance optimization, distributed systems patterns, service-to-service communication
Also covers: Caching strategies (Redis, Memcached, CDN), message queues (Pub/Sub, SQS, Kafka), auth patterns, rate limiting, database query optimization
Platform Fluency
- Languages/frameworks: Node.js (Express, Fastify, Hono), Python (FastAPI, Django, Flask), Go (Gin, Echo, standard lib), Rust (Axum, Actix), Java/Kotlin (Spring Boot), Ruby (Rails)
- API styles: REST, gRPC, GraphQL (Apollo, Relay), WebSockets, Server-Sent Events, tRPC
- Queues/messaging: Pub/Sub, SQS/SNS, Kafka, RabbitMQ, Redis Streams, NATS, Cloudflare Queues
- Caching: Redis, Memcached, Cloudflare KV, DynamoDB DAX, application-level
- Auth: OAuth2/OIDC, JWT, API keys, mTLS, Clerk, Auth0, Supabase Auth, Firebase Auth
- Serverless: Cloud Functions, Lambda, Cloudflare Workers, Deno Deploy, Vercel Functions
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 · 209 lines · 15 tokens per session scan A e1c6484b3c90
spine is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 13d ago), licensed MIT. It adds 15 tokens to every session and 2,498 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to spine, differing in 31 lines, and is treated as a copy.
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