Layers Product Strategy
Frame measurable business outcomes, map customer opportunities on a journey, place solution bets, and cheap-test the riskiest assumptions before you commit build scope.
Overview
layers-product-strategy is an agent skill most often used in Validate (also Idea research, Build pm) that links user opportunities to measurable business outcomes, solution bets, and cheap assumption tests.
Install
npx skills add https://github.com/jamiemill/layers-skills --skill layers-product-strategyWhat is this skill?
- Library of techniques (not a rigid script)—assumes `/layers-intro` is already loaded per skill header.
- Forces one measurable, meaningful, bounded business outcome per tree instead of vague “grow the product” goals.
- Opportunities stay problem-space, first-person needs anchored to journey moments with the flip test to reject solution d
- Covers solution bets, cheapest tests for riskiest assumptions, and prioritization rationale when multiple bets compete.
- Explicit efficiency rule: if outcome and bets are already clear, do not rebuild the strategy tree for its own sake.
- Strategy layer enforces one measurable, meaningful, bounded business outcome per tree.
Adoption & trust: 580 installs on skills.sh; 155 GitHub stars; 3/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You have user insights but no honest line from customer pains to a single measurable outcome and ordered bets—so scope keeps expanding before you learn anything.
Who is it for?
Indie PMs and founders using the Layers stack who need structured strategy before MVP, roadmap bets, or re-scope after discovery.
Skip if: Teams that already have a locked spec and approved implementation plan—rebuilding the strategy tree adds friction without new decisions.
When should I use this skill?
User is doing Layers product strategy—outcomes, opportunities, solution bets, assumption tests, or prioritization—and `/layers-intro` context is available.
What do I get? / Deliverables
You leave with one bounded outcome, journey-anchored opportunities that pass the flip test, prioritized solution bets, and a plan to test the riskiest assumptions cheaply—ready to prototype or plan implementation.
- Declared single business outcome with measurable bounds
- Opportunity map, prioritized solution bets, and cheap tests for top risks
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Strategy is where problem-space learning becomes bounded scope and bet order—Validate is the first canonical stop before full Build. Scope subphase fits explicit choices: one outcome per tree, which opportunities connect, which bets to pursue first, and when to skip rebuilding an already-clear tree.
Where it fits
After interviews, restate pains as first-person opportunities on a journey moment and check they survive the flip test before naming features.
Pick one bounded outcome like activation in 30 days, rank solution bets, and design the cheapest test for the riskiest assumption before prototyping.
When backlog growth outpaces learning, re-run strategy disciplines to drop bets that no longer connect to the single declared outcome.
How it compares
Problem-to-bet strategy library within Layers—not a code generator, SEO playbook, or single-phase landing-page skill.
Common Questions / FAQ
Who is layers-product-strategy for?
Solo builders and small product teams practicing Layers who want technique-driven strategy work connecting opportunities, outcomes, and testable bets.
When should I use layers-product-strategy?
Use it in Validate (scope) when scoping an MVP or bet order; in Idea (research) when translating discovery into outcomes; in Build (pm) when reprioritizing roadmap bets after new user evidence.
Is layers-product-strategy safe to install?
It is documentation-style procedural knowledge with no prescribed shell—still review the Security Audits panel on this Prism page before adding any third-party skill pack to your agent.
Workflow Chain
Requires first: layers intro
SKILL.md
READMESKILL.md - Layers Product Strategy
# /layers-product-strategy *Assumes `/layers-intro` has been loaded. This skill is a library of techniques, not a script — see "How to use these skills" there.* Strategy is the first layer of the solution space — where problem-space understanding converts into deliberate decisions about scope and direction. It is about choices: which user needs to serve, and which business outcomes to target. --- ## The decisions this layer makes - The business outcome this work serves - Which user opportunities (needs, pains, desires) genuinely connect to that outcome - What solution bets we're placing on those opportunities - How to test the riskiest assumptions cheaply - Which bets to pursue first, and why If the outcome and the bets are already clear, don't rebuild the tree for its own sake. --- ## Disciplines — what keeps strategy honest - **The outcome is measurable, meaningful, and bounded.** Not "grow the product" but "increase users who activate in the first 30 days." One outcome per tree. - **Opportunities are customer needs/pains/desires — anchored to a journey moment.** First-person, problem-space statements ("I don't know which streaming service has this movie"), not job stories and not features. Apply the **flip test**: if you can restate it as a feature, it's a solution in disguise. Keep them specific, not generic. **Group opportunities by journey moment** — the forcing function that exposes vague opportunities and surfaces moments left unaddressed. (Teresa Torres.) - **Every opportunity connects to the outcome.** If serving it wouldn't move the outcome, it doesn't belong in this tree. - **Every bet names its riskiest assumption,** and there's more than one bet per opportunity — resist early convergence. - **Every experiment is the cheapest way to test the core assumption** — days, not months. --- ## Techniques The Opportunity Solution Tree is the default; the rest serve particular strategic questions. | Technique | Use it when | |---|---| | **Opportunity Solution Tree** (Teresa Torres) | Default. Makes outcome → opportunity → solution → experiment explicit. Good for ongoing discovery. | | **Solution bets** | For a chosen opportunity: *"We could [solution], which we believe would [serve the opportunity] because [reasoning]."* Generate several; name each one's key assumption. | | **Experiments** | Cheapest test of a bet's core assumption — prototype, fake door, concierge, a targeted interview, data analysis. | | **Impact mapping** (Gojko Adzic) | B2B with multiple stakeholders who each must change behaviour. | | **Jobs portfolio mapping** | Many job stories — decide which to target by frequency, severity, strategic fit. | | **Now / Next / Later roadmap** | The team needs a shared timeline view of bets. | | **Kano analysis** | Sort candidate features into hygiene, performance, and delight. | | **HEART / North Star** (Google / Amplitude) | Choosing the outcome metric. HEART structures the choice; North Star distils to one. | | **Wardley mapping** | Positioning depends on where capabilities sit on the evolution curve; build/buy/partner. | | **Bundling / unbundling** (Christensen) | Should this product own more of the workflow, or one job precisely? | | **NPE Canvas** | Consumer products: Narrative, Primitive, Enablers. | | **Critical User Journeys** (Google / Reforge) | Which flows to prioritise — the minimal path to core value (high-traffic, high-revenue, or metric-critical). | When you build the tree as a diagram (`graph TD`): outcome at the top, branching down through opportunities **grouped by journey moment**, then bets, then experiments. Top-to-bottom reads as dependency, not sequence. --- ## Working with the designer Settle the desired outcome first, pushing for specificity. Then map the opportunities that connect to it (applying