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name: product-lifecycle-learning description: >- Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or site-reliability-engineering); do not use for analytics instrumentation or metric dashboard design (routes to product-analytics-and-measurement); do not use arbitrary thresholds as universal retirement rules — decisions require human judgment and context. license: MIT metadata: tags: product-lifecycle-learning, post-launch-review, outcome-review, feature-health, assumption-update, retirement-decisions, deprecation, sunset-planning, retained-learning, evidence-ledger, epistemic-discipline, lifecycle-closure

Product Lifecycle Learning

Close the loop from launch to learning. This skill compares what was intended against what actually happened, maintains an evidence-backed assumption ledger, assesses feature health, and makes disciplined continue/improve/harvest/pivot/pause/retire decisions — including full retirement lifecycles. It produces a durable retained learning record that feeds back into roadmap, analytics, adoption, experimentation, and future specifications.

Loading Guide

Load only the reference or template relevant to the task. Do not load every file at once.

File	Load when
references/discovery-brief.md	You need to understand how lifecycle-learning concepts map across skills and where this skill's boundaries are
references/epistemic-discipline.md	You need the full taxonomy for classifying claims as expected, observed, uncertain, or inferred
references/retirement-lifecycle.md	Planning a feature or product retirement, including deprecation, migration, customer treatment, and internal cleanup
references/feedback-destinations.md	Routing learning outputs to the right downstream skill — roadmap, analytics, adoption, experimentation, or specification
templates/outcome-review.md	Conducting a structured post-launch outcome review comparing expected vs. observed
templates/assumption-ledger-update.md	Updating the assumption ledger with new evidence and confidence shifts
templates/feature-health-record.md	Assessing feature health across multiple dimensions and surfacing signals
templates/retirement-decision.md	Making and recording a justified retirement or continuation decision
templates/sunset-plan.md	Planning deprecation communication, migration paths, customer treatment, and internal cleanup
templates/retained-learning-record.md	Capturing durable reusable learning that survives beyond the feature

Core Methodology

The Launch-to-Learning Loop

LAUNCH → [OBSERVE] → [COMPARE] → [IDENTIFY GAPS] → [UPDATE ASSUMPTIONS] → [ASSESS HEALTH] → [DECIDE] → [CAPTURE LEARNING] → (feed back)
              |            |              |                 |                    |               |               |
         Collect      Expected vs.    Gap analysis     Assumption         Feature health    Continue /      Retained
         outcome      observed        with confidence  ledger update      dimensions        Improve /       learning
         data         outcomes        intervals                                              Harvest /       record
                                                                                            Pivot /
                                                                                            Pause /
                                                                                            Retire

The loop starts after launch (the feature or capability is live and generating data) and ends with a durable learning artifact that feeds the next cycle of roadmap, analytics, adoption, experimentation, and specification work.

Stage-by-Stage

Stage	Input	Activity	Output
Observe	Analytics data, adoption metrics, user feedback, support tickets, operational metrics	Collect outcome evidence from observed behavior and system data. Distinguish signal from noise. Flag missing or low-confidence data.	Collected outcome data with confidence labels
Compare	Expected outcomes (from spec/roadmap), observed outcomes, confidence intervals	Compare the two; identify alignment, deviation, and surprise. Do not conflate expectation with observation.	Gap analysis: what matched, what diverged, what was ambiguous
Identify gaps	Gap analysis, assumption ledger	Identify which assumptions held and which broke. Distinguish between measurement gaps (could not observe) and outcome gaps (observed deviation).	Assumption gap register with confidence
Update assumptions	Assumption gap register, prior assumption ledger	Revise assumptions: strengthen confirmed ones, weaken contradicted ones, add new ones surfaced by the data. Record confidence shifts.	Updated assumption ledger. Use templates/assumption-ledger-update.md.
Assess health	Updated assumptions, adoption data, operational metrics, user feedback	Evaluate feature health across adoption, technical, operational, and strategic dimensions. Do not reduce to a single score.	Feature health assessment. Use templates/feature-health-record.md.
Decide	Feature health assessment, business context, portfolio priorities	Choose one of six lifecycle decisions. The decision requires human judgment; no automated threshold.	Decision record with accountable owner. Use templates/retirement-decision.md.
Capture learning	Decision record, gap analysis, updated assumptions, context	Produce a durable retained learning record: what was learned, why, and how it should inform future work. Not a transient meeting summary.	Retained learning record. Use templates/retained-learning-record.md.
Feed back	Retained learning record	Route learning to downstream skills: roadmap, analytics, adoption, experimentation, specifications. See references/feedback-destinations.md.	Routed learning outputs

Epistemic Discipline

Every claim in lifecycle-learning output is classified into exactly one of four categories. These are not conflated; a comparison is not an observation, and an inference is not a fact.

Category	Definition	Example	Source
Expected	What was intended or predicted before launch	"We expected activation to reach 60% within 30 days"	Spec, roadmap, launch brief
Observed	What actually happened, measured from data	"Activation reached 43% at 30 days (95% CI: 39-47%)"	Analytics, adoption data, operational metrics
Uncertain	What is ambiguous, noisy, or contested	"Attribution is confounded by a simultaneous pricing change; cannot isolate feature effect"	Confidence intervals, conflicting signals, data-quality issues
Inferred	What is concluded from evidence, with reasoning	"The gap between expected 60% and observed 43% suggests the onboarding redesign did not reduce time-to-value as hypothesized; the pricing change confound means we cannot rule out an external cause"	Reasoned implication from evidence

Full taxonomy and field guide in references/epistemic-discipline.md.

Lifecycle Decisions

Six outcomes are available after assessment. The choice requires human judgment informed by evidence; no numeric threshold or automated rule replaces context and accountability.

Decision	Meaning	Typical evidence profile	Follow-up
Continue	Keep as-is; feature is healthy	Outcomes match or exceed expectations; stable, low-risk	Schedule next review
Improve	Invest in enhancement	Adoption gap exists but fixable; underlying need confirmed	Feed roadmap and experimentation
Harvest	Reduce investment, maintain for existing users	Declining growth but stable base; not worth expanding	Monitor for retirement signals
Pivot	Change direction significantly	Need confirmed but current approach failed	Feed roadmap, discovery, experimentation
Pause	Temporarily halt investment	Ambiguous results, external confounds, or resource constraint	Schedule re-assessment with new evidence
Retire	Deprecate and remove	Sustained non-adoption, replacement exists, or strategic misalignment	Execute retirement lifecycle

Retirement Lifecycle

When the decision is Retire, a structured retirement lifecycle covers the full path from deprecation announcement through internal cleanup. Full detail in references/retirement-lifecycle.md.

Phase	Activity	Template
Deprecation communication	Announce retirement: timeline, rationale, alternatives. Target affected users with segmentation.	templates/sunset-plan.md
Migration path	Provide migration tooling, documentation, and support for existing users. Define the recommended path.	templates/sunset-plan.md
Customer treatment	Support commitments during sunset: data export, grace periods, extended support windows, SLA preservation, refund/credit policies where applicable. Coordinate with customer-success.	templates/sunset-plan.md; route communication plans to conditional-customer-success
Internal cleanup	Remove feature flags, archive code, update documentation, retire monitoring and alerting, reclaim infrastructure.	templates/sunset-plan.md
Learning closure	Capture what the feature's lifecycle taught — not a postmortem, but a closure record that completes the learning loop.	templates/retained-learning-record.md

Retained Learning Record

Every lifecycle-learning cycle produces a durable retained learning record — not a transient meeting summary. The record captures:

• What the feature or capability was intended to achieve (expected outcomes)
• What actually happened (observed outcomes, with confidence)
• What was uncertain and why
• What assumptions were updated and how
• What decision was made (continue/improve/harvest/pivot/pause/retire) and who made it
• Why that decision was reached, with evidence
• What should inform future decisions — reusable patterns, anti-patterns, assumptions to test next time
• Where the learning was routed (roadmap, analytics, adoption, experimentation, specifications)

This record is the durable learning artifact. It is the evidence that the launch-to-learning loop actually closed.

When Not to Use

This skill does not own:

• Incident postmortems, root-cause analysis, or operational incident review — these belong to incident-learning (not yet landed) and ../site-reliability-engineering/SKILL.md. Lifecycle-learning consumes incident signals as input but does not produce postmortems.
• Analytics instrumentation, metric dashboard design, tracking-plan creation, or event taxonomy — these belong to ../product-analytics-and-measurement/SKILL.md. Lifecycle-learning consumes analytics data as input but does not own measurement infrastructure.
• Customer-success account management, renewal decisions, or health scoring — these belong to conditional-customer-success (not yet landed). Lifecycle-learning routes retirement communication plans and customer-treatment strategies there.
• Roadmap prioritization or portfolio allocation — these belong to ../product-roadmapping-and-portfolio/SKILL.md. Lifecycle-learning feeds evidence into roadmap decisions but does not make them.
• Arbitrary or automated retirement thresholds — this skill never applies rules like "retire if DAU < 100" or "kill if NPS < 30" without context about the product, market, user base, and alternatives. Retirement decisions require human judgment and named accountability.

Routing and Feedback

Inputs (consumed by lifecycle-learning)

Input	Source
Expected outcomes, acceptance criteria	../spec-driven-development/SKILL.md, roadmap briefs
Observed outcomes, metric data, funnels, cohorts	../product-analytics-and-measurement/SKILL.md
Adoption evidence, activation rates, retention signals	../product-adoption/SKILL.md
Experiment results, readout learning entries	../product-experimentation/SKILL.md
Incident signals, reliability data	../site-reliability-engineering/SKILL.md, incident-learning
Customer feedback, support trends, health signals	conditional-customer-success

Outputs (produced by lifecycle-learning, routed to)

Output	Destination	Purpose
Revised assumptions, decision evidence	../product-roadmapping-and-portfolio/SKILL.md	Roadmap updates, bet re-evaluation
Metric refinement needs, measurement gaps	../product-analytics-and-measurement/SKILL.md	Improve instrumentation, close measurement gaps
Adoption pattern changes, behavior insights	../product-adoption/SKILL.md	Adoption strategy adjustments
New hypotheses, experiment ideas	../product-experimentation/SKILL.md	Feed experimentation pipeline
Spec improvements, acceptance-criteria refinements	../spec-driven-development/SKILL.md	Future specification quality
Retirement communication plans, migration coordination, customer treatment during sunset	conditional-customer-success	Customer-facing retirement execution; prose reference (skill not yet landed)
Incident-driven learning signals	incident-learning	Incident-driven learning loop; prose reference (skill not yet landed)

At least five feedback destinations must be updated per cycle: roadmap, analytics, adoption, experimentation, and specifications. Additional routing to customer-success and incident-learning is conditional on the decision.

File Map

File	Purpose	Load when
references/discovery-brief.md	Maps existing lifecycle, learning, and retirement material; ownership boundaries	Understanding the skill's place in the catalog
references/epistemic-discipline.md	Full taxonomy: expected / observed / uncertain / inferred with field guide	Classifying claims in any lifecycle-learning output
references/retirement-lifecycle.md	Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup	Retirement decision or sunset planning
references/feedback-destinations.md	Detailed routing guide for each feedback destination	Routing learning outputs to downstream skills
templates/outcome-review.md	Structured post-launch outcome review	Conducting an outcome review
templates/assumption-ledger-update.md	Assumption ledger update with confidence shifts	Updating assumptions after new evidence
templates/feature-health-record.md	Multi-dimensional feature health assessment	Assessing feature health
templates/retirement-decision.md	Justified retirement or continuation decision record	Making a lifecycle decision
templates/sunset-plan.md	Deprecation communication, migration, customer treatment, internal cleanup plan	Planning a retirement execution
templates/retained-learning-record.md	Durable reusable learning artifact	Capturing learning that survives the feature

Related Skills

• ../product-analytics-and-measurement/SKILL.md — Owns instrumentation and metric definition. Lifecycle-learning consumes analytics outputs.
• ../product-adoption/SKILL.md — Owns adoption diagnostics and strategy. Lifecycle-learning consumes adoption evidence.
• ../product-experimentation/SKILL.md — Owns experiment design and readout. Lifecycle-learning consumes experiment results.
• ../product-roadmapping-and-portfolio/SKILL.md — Owns roadmap and portfolio decisions. Lifecycle-learning feeds evidence.
• ../spec-driven-development/SKILL.md — Owns specifications and acceptance criteria. Lifecycle-learning feeds spec improvements.
• ../site-reliability-engineering/SKILL.md — Owns operational reliability. Lifecycle-learning consumes incident signals.
• conditional-customer-success — Consumer for retirement communication plans, customer treatment during sunset, migration support coordination. Prose reference; skill not yet landed.
• incident-learning — Destination for incident-driven learning signals. Prose reference; skill not yet landed.