ad-spend-optimizer
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad…
Build products customers actually want. Apply Marty Cagan's Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable. Use when: **New product development** when validating what to build; **Feature prioritization** to ensure you're
$ npx -y skills add guia-matthieu/clawfu-skills --skill product-discovery --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/product-discoveryContext preview
The summary Claude sees to decide when to auto-load this skill.
Build products customers actually want. Apply Marty Cagan's Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable. Use when: **New product development** when validating what to build; **Feature prioritization** to ensure you're
name: product-discovery description: "Build products customers actually want. Apply Marty Cagan's Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable. Use when: **New product development** when validating what to build; **Feature prioritization** to ensure you're solving real problems; **Pivot decisions** when current direction isn't working; **Team alignment** on what problems to solve; **Risk reduction** before committing development resources" license: MIT metadata: author: ClawFu version: 1.0.0 mcp-server: "@clawfu/mcp-skills"
> Build products customers actually want. Apply Marty Cagan's Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable.
| Aspect | Details | |--------|---------| | **Source** | Marty Cagan - Inspired (2008, 2018) and Empowered (2020) | | **Core Principle** | "Fall in love with the problem, not the solution. The best product teams discover what customers need, not just what they ask for." | | **Why This Matters** | Most products fail not because they're built poorly, but because they solve the wrong problem. Discovery ensures you build the right thing before you build the thing right. |
| Claude Does | You Decide | |-------------|------------| | Structures content frameworks | Final messaging | | Suggests persuasion techniques | Brand voice | | Creates draft variations | Version selection | | Identifies optimization opportunities | Publication timing | | Analyzes competitor approaches | Strategic direction |
1. **Frames the four risks** - Value, usability, feasibility, viability 2. **Distinguishes discovery from delivery** - Different mindsets, different processes 3. **Teaches opportunity assessment** - Which problems to solve 4. **Develops prototyping skills** - Test ideas before building 5. **Guides customer research** - Learn what customers need (not want) 6. **Structures continuous discovery** - Ongoing learning, not one-time research
I'm considering building [feature/product]. Apply product discovery principles to assess this opportunity. Context: [target customer, current state, hypothesis]
We're about to build [feature]. Help me identify the key risks and design tests to address them.
I want to implement continuous discovery for my product team. Help me design a weekly discovery rhythm.
## The Four Product Risks Every product idea has four risks to address BEFORE building: ### 1. Value Risk "Will customers buy/use this?" **Questions:** - Does this solve a real problem? - Is the problem painful enough to pay/switch for? - Will users actually adopt this? **Tests:** - Customer interviews - Demand testing - Fake door tests - Concierge MVP ### 2. Usability Risk "Can customers figure out how to use it?" **Questions:** - Is it intuitive? - Can users accomplish their goals? - What's the learning curve? **Tests:** - Prototype testing - Usability studies - Wizard of Oz tests - A/B tests on UX ### 3. Feasibility Risk "Can we build this?" **Questions:** - Do we have the technology? - Can we do it in reasonable time? - What are the technical dependencies? **Tests:** - Technical spike - Proof of concept - Architecture review - Build vs. buy analysis ### 4. Viability Risk "Should we build this?" **Questions:** - Does it fit our strategy? - Can we support/maintain it? - Is it legal/compliant? - Does the business model work? **Tests:** - Business case - Stakeholder review - Compliance review - Financial modeling
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## Two Tracks: Discovery and Delivery ### Discovery (Figure out WHAT to build) **Mindset:** - Embrace uncertainty - Test assumptions - Fail fast and cheap - Learn over deliver **Activities:** - Customer interviews - Prototyping - Experiments - Opportunity assessment **Outcome:** - Validated problems - Tested solutions - Confidence to build - Clear success metrics ### Delivery (BUILD it right) **Mindset:** - Reduce uncertainty - Execute efficiently - Ship quality - Hit timelines **Activities:** - Engineering - QA - Launch prep - Documentation **Outcome:** - Working software - Happy customers - Business impact - Technical quality ### The Critical Point Most teams skip discovery and jump to delivery. **Result:** - Build features no one wants - Waste engineering resources - Miss market opportunities - Frustrated team, frustrated customers **The ratio:** Spend 10-20% of time on discovery to avoid wasting 80-90% of delivery time on wrong things.
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## Assessing Product Opportunities ### The Opportunity Assessment Framework Before committing to solve a problem, answer: **1. Is this problem worth solving?** | Factor | Questions | |--------|-----------| | **Frequency** | How often does this problem occur? | | **Intensity** | How painful is it when it happens? | | **Willingness** | Will people pay/switch to solve it? | | **Reach** | How many customers have this problem? | **Scoring:** - High frequency + High intensity = Strong opportunity - Low frequency OR Low intensity = Weak opportunity **2. Can we solve it effectively?** | Factor | Questions | |--------|-----------| | **Capability** | Do we have the skills/tech? | | **Fit** | Does it align with our stra
175 expert marketing methodologies for AI agents. Free. Open source. MIT licensed. Dunford on positioning. Schwartz on copywriting. Cialdini on persuasion. Ogilvy on advertising. Hormozi on offers. Voss on negotiation.
Repo: guia-matthieu/clawfu-skills
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