--- name: product-agent description: Discover and validate product ideas, analyze markets, scope MVPs, and optimize app store presence for iOS/macOS apps. Use when user asks to discover, validate, assess, scope, or analyze product ideas, market opportunities, or when they mention "product agent", "app idea validation", "should I build this", "MVP", "market analysis", or "ASO". allowed-tools: Bash(product-agent:*), Read --- # Product Agent Skill Product Agent is an AI-powered CLI tool for iOS/macOS app product development. It uses specialized agents to guide you from idea to launch. ## When to Use This Skill Use this Skill when the user wants to: - Discover or validate product ideas - Analyze market opportunities - Check if an app idea is worth building - Understand competitive landscape - Assess problem severity - Get honest feedback on app concepts ## Quick Start The most common use case is **Problem Discovery** - validating whether an app idea solves a real problem: ```bash product-agent discover \ --idea "APP_IDEA_DESCRIPTION" \ --output-format json ``` **Always use `--output-format json`** for structured, machine-readable output. ## Available Commands ### `discover` - Problem Discovery Agent Validates product ideas by analyzing: - Core problem statement - Target users - Pain points - Severity and frequency - Current solutions and their limitations - Market opportunity - **Honest recommendation** (build/don't build) **Required Options:** - `--idea TEXT` - The app idea to analyze (required) **Optional Options:** - `--platform TEXT` - Target platform (default: "iOS/macOS") - `--target-user TEXT` - Target user persona if known - `--output-format FORMAT` - Output format: `text`, `json`, or `markdown` (default: text) - `--save` - Save output to file - `--output PATH` - Output file path (default: problem-analysis.json) - `--verbose` - Show execution time and model info **Example:** ```bash product-agent discover \ --idea "Menu bar app that reminds developers to take breaks every 20 minutes" \ --platform "macOS" \ --target-user "developers" \ --output-format json ``` ### `info` - System Information Shows configuration and system status: ```bash product-agent info ``` No options needed. Displays: - Current mode (development/production) - Claude CLI path or API key status - Environment variables - Available agents ## Output Formats ### JSON (Recommended for Analysis) Use `--output-format json` when you need to: - Analyze results programmatically - Chain with other tools/agents - Extract specific fields - Save structured data **JSON Schema:** ```json { "problem_statement": "One-sentence core problem", "target_users": "Who experiences this problem", "pain_points": ["List of specific pain points"], "severity_score": "1-10 rating", "frequency": "How often users encounter this", "current_solutions": ["Existing alternatives and their limitations"], "opportunity": "Market opportunity assessment", "recommendation": "Honest verdict: build or don't build, and why" } ``` ### Text (Human-Readable) Use `--output-format text` for: - Quick validation during conversation - Human review - Terminal-friendly output ### Markdown (Documentation) Use `--output-format markdown` for: - Saving reports - Sharing with stakeholders - Documentation ## Interpreting Results ### Key Field: `recommendation` This is the **most important field**. It contains: - Honest assessment of whether to build - Market reality check - Competitive analysis - Specific reasons for the verdict **The agent is brutally honest** - if it says "don't build", there's usually a good reason. ### Severity Score - **1-3**: Weak problem, low urgency - **4-6**: Moderate problem, decent opportunity - **7-8**: Strong problem, good opportunity - **9-10**: Critical problem, excellent opportunity ### Opportunity Assessment Look for keywords: - "WEAK" - Saturated market or marginal problem - "MODERATE" - Some opportunity with differentiation - "STRONG" - Clear gap in market - "EXCELLENT" - Underserved need with high demand ## Common Workflows ### 1. Quick Idea Validation ```bash product-agent discover \ --idea "YOUR_IDEA" \ --output-format json ``` Then analyze the `recommendation` and `severity_score` fields. ### 2. Deep Market Analysis ```bash product-agent discover \ --idea "YOUR_IDEA" \ --platform "iOS/macOS" \ --target-user "specific persona" \ --output-format json \ --verbose ``` Review all fields, especially `current_solutions` and `opportunity`. ### 3. Save for Later ```bash product-agent discover \ --idea "YOUR_IDEA" \ --output-format markdown \ --save \ --output "idea-analysis" ``` Creates `idea-analysis.md` with full report. ### 4. Compare Multiple Ideas Run discovery on each idea, save as JSON, then compare the: - `severity_score` - `opportunity` assessment - `recommendation` verdict ## Best Practices ### 1. Always Use JSON Format Unless the user specifically asks for text or markdown, use: ```bash --output-format json ``` JSON enables better analysis and integration. ### 2. Provide Context When Available If you know the platform or target user: ```bash --platform "macOS" \ --target-user "software developers" ``` More context = better analysis. ### 3. Read the Recommendation Carefully The `recommendation` field often includes: - Specific reasons not to build - Alternative approaches - Market insights - Risk factors Don't just look at the score - read the reasoning. ### 4. Save Important Results When the user might want to reference results later: ```bash --save --output "descriptive-name" ``` ### 5. Use Verbose Mode for Debugging If execution seems slow or behaves unexpectedly: ```bash --verbose ``` Shows execution time, model, and token usage. ## Handling Results ### After Running Discovery 1. **Parse the JSON output** (if using json format) 2. **Highlight the recommendation** - this is what the user cares about most 3. **Explain the severity score** - put it in context 4. **Summarize pain points** - these validate the problem 5. **Discuss opportunity** - is the market good? 6. **Present alternatives** - if "don't build", what should they do instead? ### Example Analysis Flow ``` 1. Run: product-agent discover --idea "..." --output-format json 2. Parse JSON 3. Check recommendation field 4. If "DO NOT BUILD": - Explain why (market saturation, weak problem, etc.) - Suggest alternatives or pivots 5. If "BUILD" or "PROCEED WITH CAUTION": - Highlight key differentiators needed - Discuss risks - Suggest next steps ``` ## Troubleshooting ### "Claude CLI not found" The tool is configured for development mode but can't find Claude Code CLI. **Solution:** Run `product-agent info` to check configuration. ### "Invalid output format" Valid formats are: `text`, `json`, `markdown` (lowercase only). ### JSON Parsing Issues Sometimes the LLM returns JSON wrapped in markdown code blocks. The tool automatically extracts it, but if you see issues, check the raw output. ### Slow Execution Normal execution time is 20-40 seconds. The tool is calling an LLM to do deep analysis. Use `--verbose` to see exact execution time. ## Configuration Product Agent uses environment variables for configuration: - `CLAUDE_PATH` - Path to Claude CLI binary (default: /usr/local/bin/claude) - `PRODUCT_AGENT_MODE` - `development` or `production` - `ANTHROPIC_API_KEY` - API key for production mode - `CLAUDE_MODEL` - Model to use **For this Skill, always use development mode** (default). It's free and uses Claude Code CLI. ## Advanced Usage For advanced patterns like agent chaining, batch processing, and custom workflows, see [REFERENCE.md](REFERENCE.md). ## Example Session **User asks:** "Should I build a password manager for the Apple ecosystem?" **You run:** ```bash product-agent discover \ --idea "Password manager built specifically for Apple ecosystem with iCloud sync" \ --platform "iOS/macOS" \ --output-format json ``` **You analyze:** - Parse JSON output - Check `recommendation` field - Read `current_solutions` (iCloud Keychain, 1Password, etc.) - Assess `opportunity` (likely WEAK - Apple already provides this) - Present findings: "Based on the analysis, this is not recommended. The market is saturated with Apple's own iCloud Keychain as a free, deeply-integrated solution. The opportunity is weak unless you have a truly novel approach or serve a specific underserved niche." ## Tips for Effective Use 1. **Be specific in idea descriptions** - More detail = better analysis 2. **Trust the recommendation** - The agent is trained to be honest 3. **Look for patterns** - Similar apps getting "don't build" = saturated market 4. **Focus on severity + opportunity** - Both must be strong 5. **Read current_solutions** - Shows what you're competing against 6. **Save your analyses** - Build a knowledge base of validated/invalidated ideas ## Deep-Dive Skills After running discovery, use these specialized Skills for deeper analysis: ### **competitive-analysis** Skill When you need detailed competitor research: - Feature comparison matrices - Pricing analysis across competitors - SWOT for each competitor - Differentiation opportunities - Market positioning maps **Use when:** Discovery shows potential and you need to understand competition in detail. ### **market-research** Skill When you need market sizing and opportunity assessment: - TAM/SAM/SOM calculations - Growth trends and projections - Market maturity assessment - Entry barriers analysis - Revenue potential estimates **Use when:** Discovery shows potential and you need to size the opportunity. **Workflow:** ``` 1. product-agent discover → Quick validation (30 seconds) 2. If promising, use deep-dive Skills: - competitive-analysis → Understand players - market-research → Size opportunity 3. Make go/no-go decision with full data ``` ## Coming Soon Future agents that will be added: - MVP Scoping Agent - Define what to build - Positioning Agent - Craft messaging - ASO Optimization Agent - App store optimization - Launch Planning Agent - Distribution strategy This Skill will be updated when these agents are available. --- **Remember:** Product Agent is brutally honest. If it says "don't build", listen. It's saving you months of wasted effort on weak ideas.