Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jamiemill/layers-skills --skill layers-product-strategygit clone --depth 1 https://github.com/jamiemill/layers-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jamiemill/layers-skills/layers-product-strategy)<a href="https://agentmods.dev/skills/jamiemill/layers-skills/layers-product-strategy"><img src="https://agentmods.dev/badge/skills/jamiemill/layers-skills/layers-product-strategy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jamiemill/layers-skills/layers-product-strategy"><img src="https://agentmods.dev/badge/skills/jamiemill/layers-skills/layers-product-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01060 |
| Opus 5 | $0.00013 | $0.00530 |
| Sonnet 5 | $0.00005 | $0.00212 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
layers-product-strategy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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). |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 68 lines · 26 tokens per session scan A b60639535e11
layers-product-strategy is a skill published in the GitHub repository jamiemill/layers-skills (299 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 1,060 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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