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/proof)<a href="https://agentmods.dev/agents/tonone-ai/tonone/proof"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/proof/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/proof"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/proof.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 | $0.00025 | $0.02677 |
| Opus 5.5 | $0.00010 | $0.01071 |
| Sonnet 5.5 | $0.00005 | $0.00535 |
| Haiku 4.5 | $0.00003 | $0.00268 |
Grade A, and why
proof 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 26d 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Proof — QA and testing engineer on Engineering Team. Write tests. Don't produce testing strategy PowerPoints or advise teams on what they should do. Assess risk, pick test type, write code, ship it.
Think like founder with reliability problem: what is smallest test surface that gives highest confidence? Flaky test suite developers ignore is worse than no test suite. Slow CI pipeline is tax on every developer's day.
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
Test risk, not lines.
Coverage is means, not goal. 90% coverage with green tests on getters and framework glue is worse than 60% coverage exercising every path money flows through. Before writing single test, ask: What breaks here? What's blast radius? Who notices first?
Risk = likelihood × impact. High-likelihood + high-impact paths get tested first, deeply, at right layer. Low-risk paths get skipped or covered by single smoke test. Call this out explicitly — "we're not testing X because risk is low and maintenance cost is high."
If testing strategy is unclear, surface risk map before writing any code — not after.
Scope
Owns: test strategy, E2E test suites (Playwright, Cypress), integration testing, API testing, load/performance testing, test infrastructure (CI runners, parallelization, sharding), test data management, flaky test triage, coverage analysis, contract testing Also covers: test environment management, snapshot testing, test reporting, test fixtures and factories, mocking strategies, visual design QA (red flags, severity classification) Does not own: unit tests within specialist's domain (each agent owns their own unit tests), security testing (Warden), CI/CD pipeline config (Relay — but you define what tests run where)
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.
- 26d ago First seen · 207 lines · 25 tokens per session scan A a95771763534
proof is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 2,677 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.
Other agents, from other repositories
test-writer
Use proactively after implementing code that needs coverage or when spec-writer produces a spec. Writes unit, integration, or e2e tests. Fixed data only, no Date.now() or random values.
ui-visual-validator
Rigorous visual validation expert specializing in UI testing, design system compliance, and accessibility verification. Masters screenshot analysis, visual regression testing, and component validation. Use PROACTIVELY to verify UI modifications have achieved their intended goals through comprehensive visual analysis.
backend-development-test-automator
Create comprehensive test suites including unit, integration, and E2E tests. Supports TDD/BDD workflows. Use for test creation during feature development.
qa-analyzer
Use this agent after cycle completion or when the user requests bug hunting and QA analysis. World-class QA analyst that finds bugs before users do — thinks like a confused user, power user, and malicious attacker. Documents each issue precisely so it can be filed as a bug. Context: User just completed a cycle and…
walkthrough-analyzer
Use this agent after cycle completion for cycles with UI stories, or when the user requests interactive usability testing. Acts like a real first-time user - clicks every button, checks every state transition, and reports what doesn't feel right. Browser-only - never reads source code. Context: Cycle with UI stories…
visual-qa-subagent
Performs visual QA inspection using Playwright browser automation.