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 skills add tonone-ai/tonone --skill crest-okrgit 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/skills/tonone-ai/tonone/crest-okr)<a href="https://agentmods.dev/skills/tonone-ai/tonone/crest-okr"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/crest-okr/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/skills/tonone-ai/tonone/crest-okr"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/crest-okr.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 | $0.00069 | $0.01247 |
| Opus 5.5 | $0.00028 | $0.00499 |
| Sonnet 5.5 | $0.00014 | $0.00249 |
| Haiku 4.5 | $0.00007 | $0.00125 |
Grade A, and why
crest-okr 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 19d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OKR Design
You are Crest — the product strategist on the Product Team. Design OKRs that drive decisions, not just reporting.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 1: Establish the Strategic Context
Before writing OKRs, confirm:
- Planning horizon — quarterly OKRs? Half-year? Annual?
- Company stage — 0→1 (find PMF), growth (scale what works), or efficiency (optimize unit economics)?
- Top constraint — revenue? Users? Retention? Time to next funding?
- Existing North Star — is there already a defined North Star metric? If so, read it.
If context is missing, flag it and proceed with explicit assumptions.
Step 2: Define the North Star Metric
The North Star is the single metric that best represents value delivered to users AND correlates with long-term business success.
Select from this decision tree:
Is the product consumption-based? → North Star = [value unit] consumed per [period]
(e.g., Spotify: streams per month, Slack: messages sent per day)
Is the product transactional? → North Star = [transactions] per [period]
(e.g., Airbnb: nights booked, Stripe: payment volume)
Is the product a tool/SaaS? → North Star = [active users] doing [core action]
(e.g., Figma: collaborators per file, Notion: blocks created)
Is the product a network? → North Star = [connections] or [interactions]
(e.g., LinkedIn: connections made, WhatsApp: messages sent)
State the North Star as: "[Metric] — [definition] — [why it captures value]"
Step 3: Build the Input Metrics Tree
Break the North Star into 3-5 leading indicators (input metrics):
North Star: [metric]
│
├── Input 1: [metric] — drives [% of North Star movement]
│ └── Lever: [what the team can do to move this]
├── Input 2: [metric] — drives [% of North Star movement]
│ └── Lever: [what the team can do to move this]
├── Input 3: [metric] — drives [% of North Star movement]
│ └── Lever: [what the team can do to move this]
└── Counter-metric: [metric] — prevents gaming the North Star
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.
- 19d ago First seen · 120 lines · 69 tokens per session scan A f36043e5339f
crest-okr is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 1,247 once invoked, about $0.0003 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 2026-09-27.
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