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66 · Business · Product Lifecycle

Product Lifecycle Learning & Retirement

Turns post-launch evidence into bounded continue, improve, pause, pivot or retirement recommendations.

product-lifecyclepost-launch-reviewfeature-healthdeprecationsunset-planningproduktlebenszyklusproduktruhestandproduktlernen

Product Lifecycle Learning & Retirement · Original SKILL.md

---
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](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](references/epistemic-discipline.md) | You need the full taxonomy for classifying claims as expected, observed, uncertain, or inferred |
| [references/retirement-lifecycle.md](references/retirement-lifecycle.md) | Planning a feature or product retirement, including deprecation, migration, customer treatment, and internal cleanup |
| [references/feedback-destinations.md](references/feedback-destinations.md) | Routing learning outputs to the right downstream skill — roadmap, analytics, adoption, experimentation, or specification |
| [templates/outcome-review.md](templates/outcome-review.md) | Conducting a structured post-launch outcome review comparing expected vs. observed |
| [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md) | Updating the assumption ledger with new evidence and confidence shifts |
| [templates/feature-health-record.md](templates/feature-health-record.md) | Assessing feature health across multiple dimensions and surfacing signals |
| [templates/retirement-decision.md](templates/retirement-decision.md) | Making and recording a justified retirement or continuation decision |
| [templates/sunset-plan.md](templates/sunset-plan.md) | Planning deprecation communication, migration paths, customer treatment, and internal cleanup |
| [templates/retained-learning-record.md](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](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](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](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](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](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](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](references/retirement-lifecycle.md).

| Phase | Activity | Template |
|-------|----------|----------|
| **Deprecation communication** | Announce retirement: timeline, rationale, alternatives. Target affected users with segmentation. | [templates/sunset-plan.md](templates/sunset-plan.md) |
| **Migration path** | Provide migration tooling, documentation, and support for existing users. Define the recommended path. | [templates/sunset-plan.md](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](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](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](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](../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](../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](../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](../spec-driven-development/SKILL.md), roadmap briefs |
| Observed outcomes, metric data, funnels, cohorts | [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) |
| Adoption evidence, activation rates, retention signals | [../product-adoption/SKILL.md](../product-adoption/SKILL.md) |
| Experiment results, readout learning entries | [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) |
| Incident signals, reliability data | [../site-reliability-engineering/SKILL.md](../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](../product-roadmapping-and-portfolio/SKILL.md) | Roadmap updates, bet re-evaluation |
| Metric refinement needs, measurement gaps | [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) | Improve instrumentation, close measurement gaps |
| Adoption pattern changes, behavior insights | [../product-adoption/SKILL.md](../product-adoption/SKILL.md) | Adoption strategy adjustments |
| New hypotheses, experiment ideas | [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) | Feed experimentation pipeline |
| Spec improvements, acceptance-criteria refinements | [../spec-driven-development/SKILL.md](../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](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](references/epistemic-discipline.md) | Full taxonomy: expected / observed / uncertain / inferred with field guide | Classifying claims in any lifecycle-learning output |
| [references/retirement-lifecycle.md](references/retirement-lifecycle.md) | Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup | Retirement decision or sunset planning |
| [references/feedback-destinations.md](references/feedback-destinations.md) | Detailed routing guide for each feedback destination | Routing learning outputs to downstream skills |
| [templates/outcome-review.md](templates/outcome-review.md) | Structured post-launch outcome review | Conducting an outcome review |
| [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md) | Assumption ledger update with confidence shifts | Updating assumptions after new evidence |
| [templates/feature-health-record.md](templates/feature-health-record.md) | Multi-dimensional feature health assessment | Assessing feature health |
| [templates/retirement-decision.md](templates/retirement-decision.md) | Justified retirement or continuation decision record | Making a lifecycle decision |
| [templates/sunset-plan.md](templates/sunset-plan.md) | Deprecation communication, migration, customer treatment, internal cleanup plan | Planning a retirement execution |
| [templates/retained-learning-record.md](templates/retained-learning-record.md) | Durable reusable learning artifact | Capturing learning that survives the feature |

## Related Skills

- [../product-analytics-and-measurement/SKILL.md](../product-analytics-and-measurement/SKILL.md) — Owns instrumentation and metric definition. Lifecycle-learning consumes analytics outputs.
- [../product-adoption/SKILL.md](../product-adoption/SKILL.md) — Owns adoption diagnostics and strategy. Lifecycle-learning consumes adoption evidence.
- [../product-experimentation/SKILL.md](../product-experimentation/SKILL.md) — Owns experiment design and readout. Lifecycle-learning consumes experiment results.
- [../product-roadmapping-and-portfolio/SKILL.md](../product-roadmapping-and-portfolio/SKILL.md) — Owns roadmap and portfolio decisions. Lifecycle-learning feeds evidence.
- [../spec-driven-development/SKILL.md](../spec-driven-development/SKILL.md) — Owns specifications and acceptance criteria. Lifecycle-learning feeds spec improvements.
- [../site-reliability-engineering/SKILL.md](../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.

When to use

Turns post-launch evidence into bounded continue, improve, pause, pivot or retirement recommendations.

What you get

The complete original method, source attribution and declared delivery limits.

How it works

Clarify the decision context and evidence, apply the framework, then name unresolved questions and accountable next steps.

Requirements

Intended outcome, observed evidence, customer commitments, migration constraints and accountable lifecycle owner.

Delivery and review notes

Original documentation is published by Magnus Hedemark under the repository MIT license at the pinned revision. The unchanged original is supplied as planning guidance. Referenced files, scripts, runtimes and sibling skills are not bundled or installed. Validate current facts, legal/compliance interpretations, business metrics and operational decisions with accountable owners. Loading the text does not authorize deployments, code changes, customer outreach, account changes or other external actions.

Starting prompt

Use Product Lifecycle Learning & Retirement for [TASK]. Establish Intended outcome, observed evidence, customer commitments, migration constraints and accountable lifecycle owner. Separate evidence from assumptions and return a bounded recommendation with open questions. Do not claim approval, execution or current facts without evidence.

Not for

Automatically retiring products, changing customer commitments or treating thresholds as universal rules.

What M11 added

M11 added German routing and scope limits. Original documentation is published by Magnus Hedemark under the repository MIT license at the pinned revision. The unchanged original is supplied as planning guidance. Referenced files, scripts, runtimes and sibling skills are not bundled or installed. Validate current facts, legal/compliance interpretations, business metrics and operational decisions with accountable owners. Loading the text does not authorize deployments, code changes, customer outreach, account changes or other external actions.

Original authorship remains with magnus919/agent-skills · Original source ↗

Original license & copyright

MIT License

Copyright (c) 2026 Magnus Hedemark

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: 94f4773456af20b284984950e3dc21168c21510bf473db8c1499044461c5dbb3
Snapshot checked: 2026-09-28T17:49:59.961Z

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