Agent skill · Code Review & Quality

measure-okr-grader

Scores completed OKR sets at cycle close with KR-level scoring per the canonical OKR type enum (committed | aspirational | learning | operational_health | compliance_or_safety), committed-vs-aspirational interpretation, evidence quality assessment, learning synthesis, and next-cycle recommendations. Refuses to retroactively change targets or shrink committed scope, average away guardrail KRs, treat 0.7 as success for committed or compliance_or_safety KRs, equate effort with impact, or use scores for individual performance. Hands off to iterate-lessons-log, iterate-retrospective, define-hypothe

product-on-purposegithub.com/product-on-purposeGitHub ↗
claude-codecodexApache-2.0
Install
npx skills add product-on-purpose/pm-skills --skill measure-okr-grader --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 16 KB
Bundled scripts: none
Version: 1.0.1
Declared author: product-on-purpose
Path: skills/measure-okr-grader/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 518
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 --> # OKR Grader An OKR Cycle Review is a backward-looking artifact that closes the loop on a completed OKR set. It scores each KR against its baseline and target, separates committed from aspirational interpretation, surfaces what evidence does and does not support, names what the team learned, and prepares input for next-cycle drafting. Done well, a cycle review protects the integrity of the OKR operating system by refusing to dress up missed commitments as aspirational stretch, refusing to celebrate effort over outcome, and refusing to let scoring carry weight it cannot bear. This skill is an evidence interpreter, not an arithmetic engine. Its job is to read final KR values, compare them against the original OKR set's intent, and produce a review that names the learning honestly. It enforces the empirical scoring conventions drawn from Doerr (`Measure What Matters`), Wodtke (`Radical Focus`), Castro (committed vs aspirational interpretation), Grove (`High Output Management`), and the OKR community's accumulated practice on misuse failure modes. It pairs with `foundation-okr-writer` (which produced the

What's inside
Steps it walks through
  1. When to Use
  2. When NOT to Use
  3. Instructions
  4. Constraint Rules (MUST / MUST NOT)
  5. Scoring Rules
  6. Anti-Patterns the Skill Detects
  7. Output Contract (v1.0.0)
  8. Quality Checklist
  9. Examples
Ships with 5 files
  • HISTORY.md
  • evals/output-scenarios/activation-q3-close.md
  • evals/trigger-fixtures.json
  • references/EXAMPLE.md
  • references/TEMPLATE.md
More from pm-skills
All skills →
About this skill
What does the measure-okr-grader skill do?

Scores completed OKR sets at cycle close with KR-level scoring per the canonical OKR type enum (committed | aspirational | learning | operational_health | compliance_or_safety), committed-vs-aspirational interpretation, evidence quality assessment, learning synthesis, and next-cycle recommendations. Refuses to retroactively change targets or shrink committed scope, average away guardrail KRs, treat 0.7 as success for committed or compliance_or_safety KRs, equate effort with impact, or use scores for individual performance. Hands off to iterate-lessons-log, iterate-retrospective, define-hypothe

How do I install it?

Run `npx skills add product-on-purpose/pm-skills --skill measure-okr-grader --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From product-on-purpose/pm-skills, a repository with 518 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going