# Product Lifecycle Learning

Close the launch-to-learning loop — compare intended outcomes with what actually
happened, update your assumptions with evidence, assess feature health, and make
disciplined continue, improve, harvest, pivot, pause, or retire decisions.
Includes full retirement lifecycles with deprecation, migration, customer
treatment, and durable retained learning records.

## Why Install This Skill

After you launch a feature, the work is not done — the learning starts. Teams
ship features, watch dashboards for a few weeks, and then move on, never
systematically closing the loop between what they expected and what actually
happened. Assumptions that drove the original decision go unexamined. Features
linger past their useful life because nobody owns the retirement decision. When
features are retired, existing users are left without migration paths or clear
communication.

This skill gives your agent a disciplined method for the entire post-launch
learning cycle. It collects observed outcomes, compares them against what was
expected, identifies gaps with explicit confidence intervals, updates the
assumption ledger, assesses feature health across multiple dimensions, and makes
one of six lifecycle decisions — continue, improve, harvest, pivot, pause, or
retire. When the decision is retire, it covers the full retirement lifecycle:
deprecation communication, migration paths, customer treatment during sunset,
and internal cleanup.

Most importantly, every cycle produces a durable retained learning record — not
a transient meeting summary, but an evidence-backed artifact that informs future
roadmap, analytics, adoption, experimentation, and specification work. The loop
actually closes.

## What You Get

| Directory | Purpose |
|---|---|
| [SKILL.md](SKILL.md) | Core methodology: launch-to-learning loop, epistemic discipline, lifecycle decisions, routing |
| [references/discovery-brief.md](references/discovery-brief.md) | Bounded discovery: maps existing lifecycle and learning material, ownership boundaries |
| [references/epistemic-discipline.md](references/epistemic-discipline.md) | Full taxonomy: expected, observed, uncertain, and inferred claim categories with field guide |
| [references/retirement-lifecycle.md](references/retirement-lifecycle.md) | Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup |
| [references/feedback-destinations.md](references/feedback-destinations.md) | Routing guide for each downstream feedback destination |
| [templates/outcome-review.md](templates/outcome-review.md) | Structured post-launch outcome review comparing expected vs. observed |
| [templates/assumption-ledger-update.md](templates/assumption-ledger-update.md) | Assumption ledger update with confidence shifts |
| [templates/feature-health-record.md](templates/feature-health-record.md) | Multi-dimensional feature health assessment |
| [templates/retirement-decision.md](templates/retirement-decision.md) | Justified retirement or continuation decision record |
| [templates/sunset-plan.md](templates/sunset-plan.md) | Deprecation communication, migration, customer treatment, internal cleanup |
| [templates/retained-learning-record.md](templates/retained-learning-record.md) | Durable reusable learning artifact |

## Quick Start

Load `SKILL.md` for the methodology overview and loading guide, then load
specific references and templates as the situation demands. Start with
[templates/outcome-review.md](templates/outcome-review.md) if you have a feature
that has been live long enough to produce data.

## Triggers

- Reviewing post-launch outcomes for a feature or capability
- Comparing expected outcomes (from spec or roadmap) against observed data
- Updating assumptions based on new evidence from a live feature
- Assessing whether a feature is healthy, struggling, or ready for retirement
- Deciding whether to continue, improve, harvest, pivot, pause, or retire a feature
- Planning a feature retirement, deprecation, or sunset
- Designing migration paths and customer communication for retiring features
- Capturing durable learning from a completed feature lifecycle
- Closing the loop between launch evidence and roadmap/analytics/adoption/experimentation/specs

## Requirements

No technical dependencies. Consumes data from product analytics, adoption
metrics, experimentation results, and operational monitoring systems — but does
not own any of those. Requires human judgment for retirement and lifecycle
decisions; no automated thresholds are prescribed or applied.