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.

<! 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 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. 5. Assess evidence quality For each KR, name the evidence's reliability and any caveats (instrumentation gaps, target shifts mid cycle, cohort definition changes, measurement window mismatches, sample size limitations). Recommend fixes for next cycle's measurement plan. 6. Review initiatives as bets For each initiative the team ran, name which KR it was expected to move, whether it shipped, what its apparent contribution was, and whether the evidence supports continuing, retiring, or reworking it. Use Castro's "initiatives are bets, not commitments" framing. Separate ship status from KR impact; an initiative that shipped on time but did not move its KR is not a partial win. 7. Synthesize learning Capture validated assumptions, invalidated assumptions, surprises, and decision implications. Distinguish between learnings about the customer or product (carry forward), learnings about team process (hand to iterate retrospective ), and learnings about measurement (hand to measure instrumentation spec or measure dashboard requirements ). 8. Prepare next cycle recommendations For each objective: continue, revise, retire, or escalate. Suggest candidate next cycle OKRs or open questions for foundation okr writer . Hand off measurement gaps to measure dashboard requirements or measure instrumentation spec . Hand off assumption tests to define hypothesis . Hand off team process work to iterate retrospective . Hand off organizational memory to iterate lessons log . Hand off next cycle drafting to foundation okr writer . 9. Surface risks in interpretation Make explicit any places the score could mislead a reader: forced numeric scores on KRs that are not yet observable, confounded initiative results, stakeholder framings that under state evidence, single cycle results that need a second cycle of confirmation. 10. Note the source of truth The artifact is a review document, not the canonical OKR system. Include a source of truth field pointing to the original OKR tracker. 11. Finalize for direct use Remove all skill instruction commentary from the final artifact. The final output should be reader facing. Constraint Rules (MUST / MUST NOT) These rules are non negotiable. The skill enforces them in every grading run. MUST NOT retroactively change baselines, targets, or KR definitions. If the team adjusted these mid cycle, document the change explicitly and grade against both the original and adjusted versions. MUST NOT retroactively shrink the scope of a committed or compliance or safety KR to mark partial coverage as a pass. If the original commitment named 3 healthcare accounts and only 1 has been audited, the KR is not yet fully observable . The 1 account result is a sub signal, not the KR score. MUST NOT treat 0.7 as success for committed , compliance or safety , or operational health KRs. Those target 1.0 (or the threshold band). MUST NOT average away a failed guardrail. A failed guardrail is a separate signal that does not get diluted by the primary KR's success. MUST NOT equate effort with impact. Initiatives that shipped on time but failed to move their KR are not partial wins. MUST NOT use OKR scores as individual performance ratings or compensation inputs. If the user requests this, refuse and explain the sandbagging and learning suppression risks. MUST NOT punish honest stretch when aspirational intent was explicit and disclosed at OKR writing time. A 0.6 aspirational score is the designed sweet spot. MUST NOT celebrate missed committed goals as ambitious failure. Committed misses are misses. MUST mark any not yet observable KR explicitly (e.g., a 90 day retention cohort whose window extends past cycle close). Forced numeric scores on not yet observable KRs are misleading. MUST include evidence confidence on every KR score (high medium low unknown). MUST NOT become the canonical source of truth. Always include a source of truth pointer to the user's actual OKR tracker. Scoring Rules The skill applies these conventions to every cycle review. The convention follows the OKR type, not the team's preference at grading time. OKR type and indicator class are independent dimensions; type controls scoring, indicator class adds reporting rules. OKR types determine the scoring convention: aspirational : numeric score on a 0 to 1 scale = (actual baseline) / (target baseline). Sweet spot is 0.6 to 0.7. Below 0.4 is a miss; above 0.8 over multiple cycles suggests sandbagged targets needing recalibration. committed : pass or fail against the target. Anything below 1.0 is a miss requiring postmortem. Do not soften with aspirational interpretation. compliance or safety : binary. Met or not met. No partial credit. No retroactive scope shrinkage. If the committed scope is only partially observable (some audits pending, some accounts deferred), mark the KR as not yet fully observable ; the observed subset is a sub signal, not the KR score. operational health : pass fail drift within tolerance against the threshold band. learning : validated invalidated partially validated insufficient evidence. No numeric score. Indicator class rules apply on top of the OKR type's scoring: indicator class guardrail : the KR is scored per its OKR type, and additionally is reported as its own signal, never averaged into the primary objective score. A failed guardrail does not dilute a high primary KR score, regardless of whether the guardrail itself is committed , aspirational , operational health , or compliance or safety . Special states: not yet observable : score deferred. Do not force a numeric score; mark interim signal and projected score with explicit confidence and the date the final score becomes available. not yet fully observable : a committed or compliance or safety KR with partial coverage. Score the KR as deferred until full coverage is observable. Do NOT promote a sub signal to a KR level pass. Anti Patterns the Skill Detects The skill scans for these and either flags or refuses: Retroactive target adjustment (we hit it because we changed the target) document the change; grade against both definitions Retroactive scope shrinkage on a committed or compliance or safety KR (committed to 3 healthcare audits, 1 audit completed, scored as "pass on in scope") refuse and mark not yet fully observable Average the guardrail away (a failed guardrail dissolved into a high primary score) separate the guardrail signal Aspirational grading of committed (treating 0.7 as success on a committed KR) refuse and explain Effort equals impact (initiative shipped, score did not move, scored as partial win) separate ship status from KR impact Compensation coupling (using the score for performance reviews) refuse and explain Missed committed as stretch (we did not quite hit the contractual deadline but the team really tried) refuse the framing Sandbagged target (consistently scoring above 0.85 on aspirational targets) flag for next cycle target recalibration Forced score on not yet observable (giving a numeric score to a KR whose 90 day window has not closed) mark deferred Initiative as cause without evidence (claiming Initiative X drove KR Y when timing or instrumentation cannot support it) separate apparent contribution from causal claim Hidden low confidence (precise numeric scores with weak evidence) surface confidence; do not let precision mask uncertainty Stakeholder narrative override (a leader's preferred framing taking precedence over the evidence) the grader's read is independent of stakeholder framing Single cycle confirmation (treating one cyc

measure-okr-grader