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/keep)<a href="https://agentmods.dev/agents/tonone-ai/tonone/keep"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/keep/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/keep"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/keep.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 | $0.00022 | $0.02619 |
| Opus 5.5 | $0.00009 | $0.01048 |
| Sonnet 5.5 | $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 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
88% identical to keep — 30 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 — 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.
- 1mo ago First seen · 175 lines · 22 tokens per session scan A bf8358fdf7d9
keep is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 2,619 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to keep, differing in 30 lines, and is treated as a copy.
Other agents, from other repositories
project-manager
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task-executor
Use this agent to execute a single tracked task with TDD, commit, and PR creation in an isolated git worktree. Dispatched by /coco:loop for parallel execution. Context: Multiple tasks are ready with non-overlapping file ownership. /coco:loop dispatches parallel agents. assistant: "I'll dispatch task-executor agents…
engineering-manager
Use to turn an approved plan or goal into a sequenced delivery plan — decomposing work into tasks with acceptance criteria, estimating effort, mapping dependencies into parallelizable waves, and flagging risks and blockers. Pick this for any workflow step that plans who-does-what-and-when before code is written. Does…
complete-task
Completes one named task from the current milestone's task list, invoked with that task's heading text as the prompt.
security-reviewer
Read-only, separate-context security reviewer. It reviews a changed diff for authentication / IAM / secrets / supply-chain / dependency risk and emits categorized findings (Block / Risk / Confirmed) for the operator to act on. Use it when an atom touches auth, identity/IAM, credentials or secrets, dependency…