Agent skill · magnus919
product-lifecycle-learning
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.
What it needs
About 8k tokens when loaded.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/product-lifecycle-learning