crest-okr

crest-okr is a skill for Claude Code from tonone-ai/tonone. It costs 69 tokens per session (1,247 once invoked), scanned A, original, MIT.

An OKR design workflow for defining objectives, measurable results, a North Star metric, and supporting metrics. A North Star metric is one main measure of the value a product delivers over time.

In plain words
What is it for?
Use it to create quarterly or annual OKRs, choose a primary product metric, and build a tree of input metrics for tracking progress.
Why use it?
It helps turn broad ambitions into results that can be measured and reviewed. It also connects the main metric to smaller measures and a review schedule.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the TodoWrite tool; mentions Claude Code.

Part of the full-team plugin — 9 skills shipped together

Good fit Use it to create quarterly or annual OKRs, choose a primary product metric, and build a tree of input metrics for tracking progress.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tonone-ai/tonone/crest-okr
Install

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.

Any agent
npx skills add tonone-ai/tonone --skill crest-okr
Clone the repo
git clone --depth 1 https://github.com/tonone-ai/tonone

Made for: Claude Code.

Or install full-team, the plugin that ships this one along with the rest of its 9 skills.

Wrote 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.

agentmods badge for crest-okr

README.md
[![agentmods](https://agentmods.dev/badge/skills/tonone-ai/tonone/crest-okr/github.svg)](https://agentmods.dev/skills/tonone-ai/tonone/crest-okr)
Your own site
<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.

agentmods 80×15 button for crest-okr

Your own site · 80×15
<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>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,247 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 19d ago against content hash f36043e5339f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

bundle/full-team/skills/crest-okr/SKILL.md · 120 lines

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

Read the full file on GitHub · 120 lines

Changes

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.

  1. 19d ago First seen · 120 lines · 69 tokens per session scan A f36043e5339f

Subscribe to this mod's changes

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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