OKR Grader

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 OKR set being scored) and hands off the learnings produced here to the iterate skills that consume them.

When to Use

• The OKR cycle has ended (or you are scoring a partial-cycle close)
• You have final or interim KR values, baselines, and targets
• Stakeholders need a clear review with score, evidence, and learning
• The team is deciding what to continue, stop, change, or carry forward
• There is disagreement about whether a score is good or bad
• Evidence quality across KRs is uneven and needs to be made visible

When NOT to Use

• You are still drafting OKRs - use foundation-okr-writer
• You want a generic team retro - use iterate-retrospective
• You are reporting a single experiment result - use measure-experiment-results
• You need a stakeholder progress update without scoring - use foundation-stakeholder-update
• The OKR set was never agreed on or never tracked - scoring requires an authored set; backfill via foundation-okr-writer first
• You want to use scores to evaluate individuals - the skill refuses this

Instructions

When asked to score completed OKRs, follow these steps:

1. Validate scoring readiness 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.

2. Classify each KR's type and indicator class 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).

3. Score each KR 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).

4. Interpret the objective score Avoid naive averaging when one KR is a guardrail, compliance threshold, or learning KR. Produce a qualitative read of the objective alongside any rough numeric average. State explicitly what the score does and does not mean.