---
title: "Product feature review planner"
type: "skill"
url: "https://useaiblocks.com/skills/review-a-feature-and-plan-its-future"
updated: "2026-10-28"
first_published: "2026-10-25"
job: "Build a product people want"
tested: true
licence: "MIT"
source: "https://github.com/magnus919/agent-skills/tree/92c327e525da69c9ad3812fd223a1770e977c2e3/product-lifecycle-learning"
instructions: "https://useaiblocks.com/skills/review-a-feature-and-plan-its-future/SKILL.md"
---

# Product feature review planner

> Share feature goals, results and feedback. Get a review and options for what to do next.

Share the feature goals, results and user feedback. Compare facts with hopes. Get a decision brief for the person who owns the choice.

Free skill for the job "Build a product people want" · Level: intermediate · Thinking: medium · Tested · Updated 2026-10-28

## Links

- Page: https://useaiblocks.com/skills/review-a-feature-and-plan-its-future
- Instructions (SKILL.md): https://useaiblocks.com/skills/review-a-feature-and-plan-its-future/SKILL.md
- All files (zip): https://useaiblocks.com/skills/review-a-feature-and-plan-its-future.zip
- JSON version: https://useaiblocks.com/skills/review-a-feature-and-plan-its-future.json
- In your AI: connect https://useaiblocks.com/mcp and ask "Use AI Blocks to get review-a-feature-and-plan-its-future"

## What it does

Share the feature goals, results and user feedback. Compare facts with hopes. Get a decision brief for the person who owns the choice.

## What you give it

- The original feature goals, observed outcomes, customer impact, costs and known measurement limits.

## What you get

- An outcome review, assumption updates and a decision brief with accountable owners and follow-up.

## Example: what goes in and what comes out

### What goes in

- Your feature goals and targets
- Usage and outcome numbers, with any known gaps
- Dated user feedback
- Costs and effort to run the feature (optional)

### What comes out

- **Outcome review**: What you expected, what you saw, what is unsure and what is a guess, kept apart.
- **Assumption updates**: Each early assumption, marked as backed, weakened or still unknown, with the evidence.
- **Feature health assessment**: Adoption, technical, operational and strategic evidence shown side by side, not as one score.
- **Decision brief**: The options (continue, improve, harvest, pivot, pause, retire), the trade-offs, and who decides.
- **Retirement plan**: If retiring: notice to users, migration, support and internal cleanup.
- **Retained learning record**: What the team learned, saved for the roadmap, measurement, adoption, tests and future specs.

## Use it when

- You want to review a feature and plan its future.

## Skip it when

- You need to find why an outage or bug happened: run a post-incident review with your team instead.
- You want to set up tracking or build a metrics dashboard: use your analytics tool or ask an analyst.
- You want a rule like "retire if usage drops below a number": this skill leaves the choice to a person.

## How to use it

1. Gather three things first: your feature goals and targets, your usage and outcome numbers with any known gaps, and your user feedback with dates.
2. If one is missing, say so. Your AI will mark it as missing and will not guess. Add what you can find, or note it as a gap.
3. Add the skill to your AI tool, or paste its text into a chat. No code or web access is needed.
4. Paste your three inputs and ask your AI to work in four stages, in this order. First the outcome review, from your goals and numbers. Then the assumption updates, from your early assumptions and the feedback. Then the feature health record, from usage, cost and support load. Last the decision brief. The skill folder has a template for each stage in templates/.
5. Check the facts and gaps first. Then let the person who owns the choice make the decision.
6. The skill points to follow-up skills for customer success and incident learning that are not yet available. Do those by hand. If you retire the feature, the product owner and support lead write the customer notice and the export or migration steps. Send any incident found to your on-call lead for a normal incident review.

## What to do next

- [Idea evidence reviewer](https://useaiblocks.com/skills/idea-evidence-review): Test the idea with real people

## Sources and credits

- From magnus919: https://github.com/magnus919/agent-skills/tree/92c327e525da69c9ad3812fd223a1770e977c2e3/product-lifecycle-learning, shared under the MIT licence (original name: product-lifecycle-learning).
- [product-lifecycle-learning skill](https://github.com/magnus919/agent-skills/tree/92c327e525da69c9ad3812fd223a1770e977c2e3/product-lifecycle-learning), magnus919: Original instructions and supporting files, with attribution and the MIT licence. Display copy describes the task.

## Instructions (SKILL.md)

## 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.

If the feature has a written spec or requirements document, use it as the source of what was expected.

<!-- needs: written by tools/add-needs.mjs for AI Blocks; not part of the original skill. Edit that tool, not this section. -->
### Before you start
Use the material below to do this job. If facts or tool access are missing, explain what you can finish and what must wait.

**What you need** (use your own material or the linked skill):
- **Your question** (yours): the decision or problem, and what a good outcome looks like.
- **Your results** (yours): an export of your numbers for set dates, from the tool that holds them: an ad account, your store, analytics or your product.
- **Your notes** (yours): what to keep, change or drop.

**Makes it better (optional):**
- **A document** (yours): a finished document, such as a Word file, a PDF or a page of notes.
- **The product spec** (yours): what to build and how to tell it's done: a requirements doc or user stories.

How to start:
1. **Look first.** Use what they've already attached, pasted or put in their about-me note. In a coding assistant with file access, look in the project too for these by name.
2. **Check what's missing.** Look for your question, your results, your notes. They can attach files, paste the details, or share a file path your assistant can read. Ask only for missing facts that change the result. If they ask you to start with what they have, complete the parts you can and mark the gaps.
3. **Do the job.** Follow the skill below with the facts and tools you have. Mark each gap with `MISSING:` where it belongs and never invent a fact to fill it.
<!-- /needs -->

### 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.

<!-- next: written by tools/add-needs.mjs for AI Blocks; not part of the original skill. Edit that tool, not this section. -->
### After this
When you've handed it back, suggest what they could do next in one short question. It's a suggestion: if they're done, that's fine. Offer these, and say they can type their own:
- **Test the idea with real people** with the [Idea evidence reviewer](https://useaiblocks.com/skills/idea-evidence-review). It takes the lessons and audit report and plan.
If they pick one and that skill is added here, carry straight on with what you just made. If not, give the link and say what to bring.
<!-- /next -->