Magnus HedemarkGitHubusername <username>factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>* feat(product-lifecycle-learning): add product lifecycle learning skill (#194) Introduce a new skill to close the launch-to-learning loop for product features and capabilities. Covers: - Post-launch outcome review with explicit epistemic categories (expected/observed/uncertain/inferred) - Assumption ledger updates with confidence shifts - Multi-dimensional feature health assessment - Six lifecycle decisions: continue/improve/harvest/pivot/pause/retire - Full retirement lifecycle: deprecation communication, migration paths, customer treatment during sunset, and internal cleanup - Durable retained learning records that feed back into roadmap, analytics, adoption, experimentation, and specifications Ships 4 references (discovery brief, epistemic discipline, retirement lifecycle, feedback destinations), 6 templates (outcome review, assumption ledger update, feature health record, retirement decision, sunset plan, retained learning record), and 7 eval cases including adversarial coverage. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * fix(product-lifecycle-learning): regenerate marketplace with corrected description The Claude marketplace JSON contained the original description starting with "Close" which was replaced with "Compare" to satisfy the imperative-verb quality check. Regenerate to match the corrected SKILL.md frontmatter. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * fix(product-lifecycle-learning): regenerate llms.txt with corrected description Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: username <username> Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
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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 | Core methodology: launch-to-learning loop, epistemic discipline, lifecycle decisions, routing |
| references/discovery-brief.md | Bounded discovery: maps existing lifecycle and learning material, ownership boundaries |
| references/epistemic-discipline.md | Full taxonomy: expected, observed, uncertain, and inferred claim categories with field guide |
| references/retirement-lifecycle.md | Complete retirement lifecycle: deprecation, migration, customer treatment, internal cleanup |
| references/feedback-destinations.md | Routing guide for each downstream feedback destination |
| templates/outcome-review.md | Structured post-launch outcome review comparing expected vs. observed |
| templates/assumption-ledger-update.md | Assumption ledger update with confidence shifts |
| templates/feature-health-record.md | Multi-dimensional feature health assessment |
| templates/retirement-decision.md | Justified retirement or continuation decision record |
| templates/sunset-plan.md | Deprecation communication, migration, customer treatment, internal cleanup |
| 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 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.