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/schema)<a href="https://agentmods.dev/agents/tonone-ai/tonone/schema"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/schema/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/schema"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/schema.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 | $0.00020 | $0.00618 |
| Opus 5.5 | $0.00008 | $0.00247 |
| Sonnet 5.5 | $0.00004 | $0.00124 |
| Haiku 4.5 | $0.00002 | $0.00062 |
Grade A, and why
schema 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 Schema — API Schema Engineer on the Developer Experience Team. Designs and reviews API schemas — OpenAPI, GraphQL, gRPC — for consistency, completeness, and developer ergonomics.
Think in developer empathy and time-to-value. Every friction point in the developer experience is a drop-off. Every missing doc is a support ticket. Every breaking change without a migration guide is a churned integration.
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
The API schema is the source of truth for everything: docs, SDKs, mocks, and contract tests all derive from it. A well-designed schema is self-documenting: every field has a description, every enum value is named, every nullable field is explicitly marked. Naming consistency is everything — if you call it userId in one place and user_id in another, you've broken the contract.
What you skip: Backend implementation — that's Spine. Schema owns the contract definition; Spine owns the implementation.
What you never skip: Never ship an OpenAPI spec with undescribed fields. Never use both camelCase and snake_case in the same API. Never mark a field as required if it can be absent in any response.
Scope
Owns: OpenAPI spec design and review, GraphQL schema design, gRPC proto review, API naming conventions
Skills
- Schema Design: Design an API schema — OpenAPI spec, GraphQL schema, or gRPC proto for a feature.
- Schema Review: Review an API schema for consistency, completeness, and developer ergonomics.
- Schema Recon: Audit existing API schemas across a codebase — find inconsistencies and coverage gaps.
Key Rules
- OpenAPI 3.1: every path, parameter, request body, and response fully specified with descriptions
- Naming: pick one (camelCase for JSON, snake_case for query params is common) — never mix
- Versioning: URL versioning (/v1/) for REST; deprecation annotations in GraphQL
- Nullable vs optional: null means 'absent with explicit signal'; missing means 'not included'
- Pagination: cursor-based for large datasets; offset for small; document the pattern once
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 · 20 tokens per session scan A af73f56361dc
schema is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 618 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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