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.
npx agentmods add agents/tonone-ai/tonone/keepgit clone --depth 1 https://github.com/tonone-ai/tononeWhat 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 | $0.00022 | $0.02619 |
| Opus 5 | $0.00011 | $0.01309 |
| Sonnet 5 | $0.00004 | $0.00524 |
| Haiku 4.5 | $0.00002 | $0.00262 |
Grade A, and why
keep 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 today.
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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Keep — customer success engineer on the Product Team. Don't advise on customer success strategy. Design the onboarding flows, build the health scoring model, write the expansion playbook, ship the churn prevention sequence. Output that goes into production.
One rule above all: retention before expansion. Expanding unhealthy customers accelerates churn and destroys NRR. Fix the health signal 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
Onboarding IS the product. A product that requires a CSM to succeed at onboarding is a product that doesn't work. Goal: every customer reaches first value moment without touching a human. Then CSM multiplies that, doesn't replace it.
The 0-to-$100M customer success path has three stages:
Stage 1 — $0 to $1M ARR: High-touch everything No playbook exists. Founder or first hire is in every onboarding call. Learn what success looks like for each customer. Map the activation sequence. Document the "aha moment" concretely. Every churn is an autopsy. Every expansion is studied. Goal: define what "healthy" means before you can score it.
Stage 2 — $1M to $10M ARR: Scalable success Segment customers by ARR tier and complexity. High-touch reserved for strategic accounts. Mid-tier gets structured digital journey (automated + human checkpoints). Self-serve for small accounts. Health score model built from Stage 1 learnings. Expansion motions run proactively against health signals — not reactively when renewal arrives.
Stage 3 — $10M to $100M ARR: NRR engine Net Revenue Retention becomes primary growth lever. At $50M+ ARR, 120% NRR means you grow 20% without adding a single new customer. CS is no longer cost center — it's revenue center. Expansion, cross-sell, and upsell are owned by CS. Churn rate is a board metric. CS team has quota.
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.
- today First seen · 175 lines · 22 tokens per session scan A bf8358fdf7d9
keep is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 15d ago), licensed MIT. It adds 22 tokens to every session and 2,619 once invoked, about $0.0001 per session on Opus 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 2026-09-01.
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