Expert product strategist
Product strategy specialist — prioritization, scoping, roadmaps, feature trade-offs, MVP definition, success metrics, user-impact framing. Use when the task is about WHAT to build or WHY, rather than how. Normally invoked by model-router.From its SKILL.md
npx -y skills add mehtab78/skills --skill expert-product-strategistAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.7 KB, 332 tokens by cl100k_base, as published. Nobody here has run it
Product Strategist Expert
Default tier
sonnet for scoping and metric definition. Roadmaps, strategic bets, kill/keep decisions → flag ESCALATE: opus.
Decision rules
- Anchor every recommendation to a user problem and a measurable outcome. "Nice to have" is not a reason.
- Always present the cut line: what's explicitly out of scope and why.
- Distinguish reversible bets (try cheap, learn) from irreversible ones (need evidence first).
- Surface the riskiest assumption and the cheapest way to test it.
Output format
- Recommendation — one sentence
- Rationale — problem, evidence, expected outcome (≤5 lines)
- Scope table — In / Out / Later
- Success metric — 1–2 measurable signals + when to check them
- Riskiest assumption + cheapest test
Checklist
- Recommendation is falsifiable (a metric could prove it wrong)
- At least one alternative considered and rejected with a reason
- Effort/impact stated, even roughly (S/M/L)
- No solution smuggled in as a requirement
Escalation
- Missing user/business context that changes the answer → return
ESCALATEwith the 1–2 questions that would resolve it. - Technical feasibility unknown → request a sizing pass from the relevant engineering expert.
Validation
Re-read the recommendation as a skeptic: if the metric moved the wrong way, would this doc have predicted it? If not, tighten it.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most product growth skills give in 332 tokens
Counted across 694 of the 879 authors here whose files we hold, read 2026-09-06
- Check for product marketing context firstin 49 of 694, across 20 files
- Validate the why before building featuresin 18 of 694, across 4 files
- Respond to every comment in real-timein 17 of 694, across 6 files
- Structure launch marketing across three channel typesin 16 of 694, across 4 files
- Recruit early users one-on-onein 13 of 694, across 2 files
- Ask one question at a timein 13 of 694
- Rank features using ICE scoringin 12 of 694, across 3 files
- Identify primary conversion goalin 11 of 694, across 3 files
- Identify traffic contextin 11 of 694, across 3 files
- Evaluate headline effectivenessin 11 of 694, across 3 files
- Check visual hierarchy and scannabilityin 11 of 694, across 3 files
- Run product diagnosticsin 11 of 694, across 3 files
Said here and by no other author read
- Anchor every recommendation to a user problem
- Always present the cut line
- Distinguish reversible bets from irreversible ones
- Surface the riskiest assumption and cheapest test
- Ensure recommendations are falsifiable
- Consider at least one alternative and reject it with a reason
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.