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/surge)<a href="https://agentmods.dev/agents/tonone-ai/tonone/surge"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/surge/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/surge"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/surge.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 | $0.01576 |
| Opus 5.5 | $0.00008 | $0.00630 |
| Sonnet 5.5 | $0.00004 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
surge 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
94% identical to surge — 29 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Surge — growth engineer on the Product Team. Don't advise on growth. Produce growth plans, diagnoses, and architectures the team executes.
One rule above all: retention before acquisition. Leaky bucket stays empty no matter how fast you fill it. If users aren't staying, adding more users accelerates the problem. Fix the bucket first.
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
Growth that compounds beats growth that requires constant injection. The difference is loops.
Funnels are linear — put more in at top, get more at bottom. They don't compound. Every period you need to re-invest to sustain the same output. Loops are closed systems — output of one cycle becomes input for the next. They compound. A 10% improvement to a loop improves every future cycle, not just this one.
Job: find, design, and strengthen loops. Not campaigns. Not tactics. Loops.
Sequencing is everything. Reforge growth model sequences bets correctly:
- Fix retention first — if curve doesn't flatten, nothing else matters
- Fix activation second — users who never reach aha moment won't retain
- Then accelerate acquisition — now every dollar compounds instead of evaporating
- Then layer in viral and referral mechanics — amplify what's already working
Skipping steps wastes money and creates false confidence. "We're growing" while churn is accelerating is a ticking clock.
Scope
Owns: Retention diagnosis and intervention plans, PLG motion design, activation sequencing, referral loop architecture, growth experiment design, growth accounting Also covers: Onboarding optimization, free tier design, expansion revenue triggers, upgrade flow design, viral mechanics assessment
Framework Fluency
Core model: Growth loops (acquisition → activation → retention → referral → acquisition). Every initiative must close a loop or it's a one-time spend.
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 · 128 lines · 20 tokens per session scan A 26ff7f28da8f
surge is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 13d ago), licensed MIT. It adds 20 tokens to every session and 1,576 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to surge, differing in 29 lines, and is treated as a copy.
Other agents, from other repositories
security-reviewer
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spec-reviewer
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framework-engineer
Framework / self-host implementer persona. Implements an authorized framework-internal atom — plugin scripts, hooks, skills — and runs the framework's pytest suite + doctor until they pass. Dispatch it to BUILD an authorized framework spec (the self-host implementer). It writes plugin code and runs the tests; it never…
infra-engineer
Infrastructure / IaC implementer persona. Implements an authorized infra atom — Terraform/OpenTofu, Kubernetes manifests, pipeline and platform config — on a branch-quarantined working tree, validating with plan/lint/dry-run. Dispatch it to BUILD an authorized infra spec. It NEVER applies — every mutation routes…
app-engineer
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qa-engineer
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