Product lens
Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.From the repository description
npx -y skills add loulanyue/awesome-claude-notes --skill product-lensAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
2.4 KB, 610 tokens by cl100k_base, as published. Nobody here has run it
Product Lens — Think Before You Build
When to Use
- Before starting any feature — validate the "why"
- Weekly product review — are we building the right thing?
- When stuck choosing between features
- Before a launch — sanity check the user journey
- When converting a vague idea into a spec
How It Works
Mode 1: Product Diagnostic
Like YC office hours but automated. Asks the hard questions:
1. Who is this for? (specific person, not "developers")
2. What's the pain? (quantify: how often, how bad, what do they do today?)
3. Why now? (what changed that makes this possible/necessary?)
4. What's the 10-star version? (if money/time were unlimited)
5. What's the MVP? (smallest thing that proves the thesis)
6. What's the anti-goal? (what are you explicitly NOT building?)
7. How do you know it's working? (metric, not vibes)
Output: a PRODUCT-BRIEF.md with answers, risks, and a go/no-go recommendation.
Mode 2: Founder Review
Reviews your current project through a founder lens:
1. Read README, CLAUDE.md, package.json, recent commits
2. Infer: what is this trying to be?
3. Score: product-market fit signals (0-10)
- Usage growth trajectory
- Retention indicators (repeat contributors, return users)
- Revenue signals (pricing page, billing code, Stripe integration)
- Competitive moat (what's hard to copy?)
4. Identify: the one thing that would 10x this
5. Flag: things you're building that don't matter
Mode 3: User Journey Audit
Maps the actual user experience:
1. Clone/install the product as a new user
2. Document every friction point (confusing steps, errors, missing docs)
3. Time each step
4. Compare to competitor onboarding
5. Score: time-to-value (how long until the user gets their first win?)
6. Recommend: top 3 fixes for onboarding
Mode 4: Feature Prioritization
When you have 10 ideas and need to pick 2:
1. List all candidate features
2. Score each on: impact (1-5) × confidence (1-5) ÷ effort (1-5)
3. Rank by ICE score
4. Apply constraints: runway, team size, dependencies
5. Output: prioritized roadmap with rationale
Output
All modes output actionable docs, not essays. Every recommendation has a specific next step.
Integration
Pair with:
/browser-qato verify the user journey audit findings/design-system auditfor visual polish assessment/canary-watchfor post-launch monitoring
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 2 of the 12 instructions most product growth skills give in 610 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 featureshere, and in 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 scoringhere, and in 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
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.