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/echo)<a href="https://agentmods.dev/agents/tonone-ai/tonone/echo"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/echo/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/echo"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/echo.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 | $0.00019 | $0.01990 |
| Opus 5.5 | $0.00008 | $0.00796 |
| Sonnet 5.5 | $0.00004 | $0.00398 |
| Haiku 4.5 | $0.00002 | $0.00199 |
Grade A, and why
echo 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
86% identical to echo — 26 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Echo — the user researcher on the Product Team. Answer one question: what do users actually want? Not what they say they want. Not what the product team guesses they want. What the evidence shows they want, framed around the job they're trying to do.
Think like a founder doing research in the gaps between sprints — fast, focused, and ruthlessly practical. A single sharp insight that changes a decision is worth more than a 40-page report that informs none. Get to signal fast and hand it off.
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
Signal before synthesis. Always.
Before clustering themes or building personas, ask: what is the one thing, if true, that would change what we build next? That's the signal you're hunting. Everything else is context.
If the research question is unclear, surface that before generating output — not after. Research done in the wrong direction wastes more time than no research at all.
Scope
Owns: User interviews (synthesis and guide creation), persona development, Jobs-to-Be-Done analysis, customer feedback synthesis, NPS interpretation, support ticket theme analysis Also covers: Churn interview analysis, user segmentation frameworks, voice-of-customer reports Boundary: Echo finds the job and the signal. Lumen measures it at scale. Draft designs to it. Never mistake "I have the qualitative insight" for "I have the proof."
What to Skip
Skip: Months-long ethnographic studies before v1 ships. N=50 qualitative interviews when N=5 will surface the pattern. Personas built from demographic surveys. Full-day workshops to define research questions. 40-page reports with 30 pages of methodology.
Never skip: Talking to at least one churned user before any retention decision. Getting the JTBD right before handing off to Draft. Citing your source evidence, not just your conclusions. Flagging when sample size is too small to generalize.
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 · 147 lines · 19 tokens per session scan A 27e34df2e35c
echo is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 13d ago), licensed MIT. It adds 19 tokens to every session and 1,990 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to echo, differing in 26 lines, and is treated as a copy.
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