measure-okr-grader

Name: measure-okr-grader
Author: product-on-purpose

486stars64forks

How to install measure-okr-grader?

product-on-purpose/pm-skills/measure-okr-grader

$npx -y skills add product-on-purpose/pm-skills --skill measure-okr-grader

Installs into the current project.

Summary

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-cy

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SKILL.md

1	<!-- PM-Skills \| https://github.com/product-on-purpose/pm-skills \| Apache 2.0 -->
2	# OKR Grader
3
4	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.
5
6	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 OKR set being scored) and hands off the learnings produced here to the iterate skills that consume them.
7
8	## When to Use
9
10	- The OKR cycle has ended (or you are scoring a partial-cycle close)
11	- You have final or interim KR values, baselines, and targets
12	- Stakeholders need a clear review with score, evidence, and learning
13	- The team is deciding what to continue, stop, change, or carry forward
14	- There is disagreement about whether a score is good or bad
15	- Evidence quality across KRs is uneven and needs to be made visible
16
17	## When NOT to Use
18
19	- You are still drafting OKRs - use `foundation-okr-writer`
20	- You want a generic team retro - use `iterate-retrospective`
21	- You are reporting a single experiment result - use `measure-experiment-results`
22	- You need a stakeholder progress update without scoring - use `foundation-stakeholder-update`
23	- The OKR set was never agreed on or never tracked - scoring requires an authored set; backfill via `foundation-okr-writer` first
24	- You want to use scores to evaluate individuals - the skill refuses this
25
26	## Instructions
27
28	When asked to score completed OKRs, follow these steps:
29
30	1. Validate scoring readiness
31	Check inputs: original OKR set, cycle dates, final KR values (or interim values for partial-close), baselines, targets, evidence sources, and OKR types (committed \| aspirational \| learning \| operational_health \| compliance_or_safety). If a value is missing, mark it explicitly (`not-yet-observable`, `not-instrumented`, `not-supplied`); never fabricate. Refuse to grade KRs whose original definitions are missing entirely.
32
33	2. Classify each KR's type and indicator class
34	The OKR type is one of `committed \| aspirational \| learning \| operational_health \| compliance_or_safety` (the five values produced by `foundation-okr-writer`). The indicator class is one of `leading \| lagging \| guardrail \| health \| evidence_generation`. Carry both forward from the original OKR set, or assign defaults if the original set did not specify. The OKR type determines the scoring convention: `aspirational` uses the 0.6 to 0.7 sweet spot; `committed` targets 1.0; `compliance_or_safety` is binary; `operational_health` is pass \| fail \| drift-within-tolerance against a threshold band; `learning` grades by validated or invalidated rather than by score. The indicator class adds independent rules that apply on top of the type's scoring (see Step 3).
35
36	3. Score each KR
37	Score each KR using the convention for its OKR type, then apply the indicator-class rules on top; see the Scoring Rules section below for the full per-type convention table and the guardrail rule (do not restate them here). For each score, state the calculation or rationale and the evidence confidence (high \| medium \| low \| unknown).
38
39	4. Interpret the objective score
40	Avoid naive averaging when one KR is a guardrail, compliance thr

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6 picks

Type

measure-okr-grader

486stars64forks

View on GitHub

How to install measure-okr-grader?

product-on-purpose/pm-skills/measure-okr-grader

$npx -y skills add product-on-purpose/pm-skills --skill measure-okr-grader

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	<!-- PM-Skills \| https://github.com/product-on-purpose/pm-skills \| Apache 2.0 -->
2	# OKR Grader
3
4	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.
5
6	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 OKR set being scored) and hands off the learnings produced here to the iterate skills that consume them.
7
8	## When to Use
9
10	- The OKR cycle has ended (or you are scoring a partial-cycle close)
11	- You have final or interim KR values, baselines, and targets
12	- Stakeholders need a clear review with score, evidence, and learning
13	- The team is deciding what to continue, stop, change, or carry forward
14	- There is disagreement about whether a score is good or bad
15	- Evidence quality across KRs is uneven and needs to be made visible
16
17	## When NOT to Use
18
19	- You are still drafting OKRs - use `foundation-okr-writer`
20	- You want a generic team retro - use `iterate-retrospective`
21	- You are reporting a single experiment result - use `measure-experiment-results`
22	- You need a stakeholder progress update without scoring - use `foundation-stakeholder-update`
23	- The OKR set was never agreed on or never tracked - scoring requires an authored set; backfill via `foundation-okr-writer` first
24	- You want to use scores to evaluate individuals - the skill refuses this
25
26	## Instructions
27
28	When asked to score completed OKRs, follow these steps:
29
30	1. Validate scoring readiness
31	Check inputs: original OKR set, cycle dates, final KR values (or interim values for partial-close), baselines, targets, evidence sources, and OKR types (committed \| aspirational \| learning \| operational_health \| compliance_or_safety). If a value is missing, mark it explicitly (`not-yet-observable`, `not-instrumented`, `not-supplied`); never fabricate. Refuse to grade KRs whose original definitions are missing entirely.
32
33	2. Classify each KR's type and indicator class
34	The OKR type is one of `committed \| aspirational \| learning \| operational_health \| compliance_or_safety` (the five values produced by `foundation-okr-writer`). The indicator class is one of `leading \| lagging \| guardrail \| health \| evidence_generation`. Carry both forward from the original OKR set, or assign defaults if the original set did not specify. The OKR type determines the scoring convention: `aspirational` uses the 0.6 to 0.7 sweet spot; `committed` targets 1.0; `compliance_or_safety` is binary; `operational_health` is pass \| fail \| drift-within-tolerance against a threshold band; `learning` grades by validated or invalidated rather than by score. The indicator class adds independent rules that apply on top of the type's scoring (see Step 3).
35
36	3. Score each KR
37	Score each KR using the convention for its OKR type, then apply the indicator-class rules on top; see the Scoring Rules section below for the full per-type convention table and the guardrail rule (do not restate them here). For each score, state the calculation or rationale and the evidence confidence (high \| medium \| low \| unknown).
38
39	4. Interpret the objective score
40	Avoid naive averaging when one KR is a guardrail, compliance thr

People who liked this also viewed

6 picks

Type