agentsclimarketplace

Product lens

Skill loulanyue/awesome-claude-notes/skills/product-lens

Community-maintained distribution of reusable AI coding agents, commands, skills, hooks, and cross-harness workflows.From the repository description

Install
npx -y skills add loulanyue/awesome-claude-notes --skill product-lens

Assembled 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-qa to verify the user journey audit findings
  • /design-system audit for visual polish assessment
  • /canary-watch for 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.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.