Product Outcome Evaluator

SkillAI & models

Lets your agent judge whether a shipped product feature met its original hypothesis and recommend continue, adjust, or stop.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Product Outcome Evaluator skill

About this capability

Evaluates observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.

What this skill tells your AI

The instructions your AI receives, as published by levnikolaevich/claude-code-skills in plugins/operations-suite/skills/ln-72-product-outcome-evaluator/SKILL.md and read by ahel’s review.

Goal: Determine what available evidence supports about a delivered product outcome and recommend continuation, adjustment or stopping. Remain read-only: do not change instrumentation, experiments, user treatment, campaigns or product files.

Execution contract: The checklist defines completion. Track each item internally as PENDING, PROVEN with evidence, CLEARED with evidence its condition is absent, or UNPROVEN with a gap; reading, delegation, or tool failure is not proof. Reconcile after each section. Before returning, resolve all PENDING, count only PROVEN and CLEARED, and apply verdict and approval rules to every gap. Preserve intent, scope, and existing authorization. Continue authorized work; ask only for consequential unresolved choices or required external approval. Scale depth to material risk without skipping checks. Preserve dependency and safety order; otherwise choose an appropriate verification method. Accept equivalent user or repository evidence; no other skill, named artifact, or complete lifecycle is required. Preserve source requirement and decision IDs. Bind reused evidence to relevant source versions, dirty changes, configuration, and environment; invalidate only affected claims. On continuation, reconcile task, authorization, current state, and unresolved evidence. For long work, return a compact continuation record or update an already authorized artifact; read-only skills do not persist it. Distinguish artifact readiness, verified behavior, and external-action authority. Prepare authorized work before required approval. If blocked by an instruction, cite its exact source and unresolved boundary; do not invent approval gates from caution.

Tool Routing

NeedPreferred capabilityFallback
Original hypothesisProduct intent, baseline, experiment/measurement plan and accepted targetsReconstruct from attributable sources; keep missing targets unknown
Outcome evidenceAuthorized analytics, experiment results, customer behavior and cost/support evidenceSanitized exports with explicit measurement limits
AnalysisReproducible queries/statistics appropriate to the study designTransparent arithmetic and qualitative inference; no fabricated causal confidence

Domain Rules

  • Distinguish delivered behavior, observed metric movement and causal product impact. A release or acceptance test proves neither adoption nor business value.
  • Do not choose success thresholds after seeing the result. Separate predeclared criteria from exploratory findings and owner preferences.
  • Use only authorized data with necessary minimization. A recommendation is not permission to run an experiment or contact users.

Checklist

1. Frame the Outcome Decision

  • Resolve the delivered capability, intended audience, original hypothesis, decision horizon and outcome decision requested.
  • Identify the released/deployed version, rollout/exposure window and relevant baseline or comparison group.
  • Recover predeclared primary metrics, guardrails, targets and stop rules; mark absent criteria rather than inventing them.
  • Separate product intent and owner preference from measured behavior and external assumptions.

2. Assess Measurement Fitness

  • Inspect metric definitions, units, denominators, event coverage, deduplication, identity joins and missing data.
  • Check whether users were actually exposed and whether observation duration supports the intended outcome.
  • Assess cohort composition, selection bias, seasonality, concurrent changes and other confounders.
  • For experiments, inspect assignment, contamination, sample imbalance and uncertainty using the actual study design.
  • Distinguish trustworthy measurements, reported results, estimates, qualitative signals and unavailable evidence.

3. Evaluate Value and Harm

  • Compare outcomes with valid baselines or controls using reproducible calculations and appropriate uncertainty.
  • Check guardrails and material regressions in user experience, reliability, support burden, cost or data quality.
  • Separate aggregate effects from relevant segments and expose tradeoffs without fishing for favorable subgroups.
  • Distinguish causal conclusions supported by the design from correlations and exploratory interpretations.
  • Identify whether failure lies in adoption, interaction, correctness, measurement or the original value hypothesis.

4. Recommend the Next Decision

  • Recommend continue, adjust or stop only to the degree supported by the evidence; explain what could reverse the recommendation.
  • For uncertainty, define the cheapest next measurement or experiment with audience, signal, boundary and decision criterion without executing it.
  • Return results linked to the original requirement/hypothesis and observed deployment state.
  • Report data and causal limitations explicitly; do not transform lack of proof into proof of no effect.

Verdict

  • SUPPORTED: evidence supports the intended outcome within the stated population, window and causal limits.
  • NOT_SUPPORTED: valid evidence contradicts the declared outcome or violates a required guardrail.
  • INCONCLUSIVE: evidence cannot establish the outcome or causal interpretation.
  • BLOCKED: essential hypothesis, exposure identity or authorized data is unavailable.

Self-Check

  • Reconcile before returning. Check item-level evidence, requirement coverage, contradictions, scope, verdict, and applicable cleanup. Correct the report or authorized artifacts. Reuse valid evidence; do not automatically rescan the repository or rerun successful commands. Repeat checks only for relevant changes, failures, or unresolved evidence. Disclose remaining gaps.

Output Contract

Report in the user's language, in this order; retain all five fields and state each fact once. Small results may use one line per field; omit empty tables and do not copy linked artifacts:

  1. Result: Skill-specific verdict and supported outcome.
  2. Scope: Reviewed/changed scope, exclusions, baseline, and material assumptions.
  3. Evidence: Skill-specific fields below; distinguish facts, inferences, and unverified claims. Link artifacts; use tables when useful.
  4. Verification: Checks/results, unavailable evidence, and applicable cleanup/external state.
  5. Completion: Checklist: X/Y complete; Incomplete: None or each UNPROVEN item's reason, outcome impact, and exact next action; residual risks and required decisions.

Skill-specific evidence: Hypothesis, deployed exposure, baseline/control, metric definitions and quality, reproducible results and uncertainty, guardrails, causal limits, recommendation and next evidence action.

Signals

GitHub stars
565
Forks
84
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
ln-72-product-outcome-evaluator
Source
github.com/levnikolaevich/claude-code-skills