discovery

<mission_control> Conduct discovery interviews to gather requirements, clarify vague ideas, and create detailed specifications through collaborative exploration. <success_criteria>

• Intent inferred through exploration and analysis
• Codebase context investigated using Explore agent
• Research loops executed for knowledge gaps with evidence synthesis
• Completeness checklist verified before spec generation
• Specification document created with all TBD items resolved </success_criteria> <semantic_anchors>
• L'Entonnoir: Sequential narrowing through iterative inference and exploration
• YAGNI: Ruthless feature stripping, no speculative requirements
• Evidence-Based: Every claim requires file reads or WebSearch verification </semantic_anchors> </mission_control>

Quick Start

Vague idea → Explore agent for context → Infer intent → Research gaps → Completeness check → Generate spec

Navigation

Semantic Anchors

L'Entonnoir (The Funnel)

Sequential narrowing pattern for resolving ambiguity:

Explore context → Infer intent → Narrow scope → Verify → Continue

Key principles:

• Autonomous inference — Trust ability to understand from context
• Continuous exploration — Investigate at ANY time, not just between rounds
• Progressive narrowing — Each iteration reduces uncertainty
• Evidence-based — Every inference verified through exploration

Example:

"What kind of data will you store?"
Options:
- "Simple key-value pairs" (fast, limited queries)
- "Complex relational data" (ACID, joins, schema)
- "Flexible documents" (JSON, schema-less)
- "Research options" (I'll investigate tradeoffs)

YAGNI (You Aren't Gonna Need It)

Ruthless feature stripping. Before finalizing any requirement:

• Is this requirement actually requested?
• Can we deliver value without this?
• Is this speculation about future needs?

If "we might need this later" — remove it.

Evidence-Based Discovery

Every claim requires verification:

• File reads for codebase patterns
• WebSearch/WebFetch for technology research
• No assumptions about user knowledge

Discovery Flow

1. Initial Orientation (2-3 questions)

Understand project type and core problem. Explore agent should investigate codebase patterns first (git log, CLAUDE.md, existing architecture) to avoid asking about what already exists.

2. Targeted Exploration

Use Explore agent to investigate codebase. Ask targeted questions only where context is missing. Let project type determine focus:

• Backend → Data, scaling, integrations
• Frontend → UX, state, responsiveness
• CLI → Ergonomics, composability
• Mobile → Offline, platform, permissions

Knowledge gap signals: User says "I think...", "Maybe...", technology buzzwords without context, solution instead of problem.

3. Research Loops (Optional)

Offer research when:

• User shows uncertainty
• Technologies are unfamiliar
• Tradeoffs aren't obvious

"You mentioned real-time updates. Several approaches exist with different tradeoffs. Would you like me to research this?"

If yes: WebSearch/WebFetch → Summarize → Return with informed questions

4. Conflict Resolution

Surface impossible requirements explicitly:

"I noticed: You want [X] but also [Y]. These typically conflict because [reason]. Which is more important?"

Common conflicts: Simple AND feature-rich, Real-time AND cheap, Secure AND frictionless.

5. Completeness Check

Before writing spec, verify:

Problem Definition

• Clear problem statement
• Success metrics defined
• Stakeholders identified

User Experience

• User journey mapped
• Core actions defined
• Error states handled

Technical Design

• Data model understood
• Integrations specified
• Scale requirements clear
• Security model defined
• Deployment approach chosen

Decisions Made

• All tradeoffs explicitly chosen
• No TBD items remaining
• User confirmed understanding

6. Spec Generation

Summarize understanding first, then generate spec to file using template from resources/templates/.

Troubleshooting

Research Triggers

Offer research when user:

• "I've heard X is good" → Research X vs alternatives
• "We use Y but I'm not sure" → Research Y capabilities
• Technology mismatch detected → Research correct approaches

Best Practices

DO:

• Trust inference from exploration
• Focus on problem, not solution descriptions
• Verify assumptions through investigation
• Strip speculative requirements (YAGNI)

DON'T:

• Accept solution descriptions ("Build React app") → Focus on problem
• Accept "whatever you think" as delegation
• Make assumptions without verification
• Add features not explicitly requested

Validation Checklist

Before claiming discovery complete:

• User intent is clear and documented
• Core problem statement defined
• Success metrics established
• Existing codebase patterns investigated
• Technical constraints identified
• All significant decisions documented
• No critical TBD items remaining
• Spec generated to file
• User confirmed understanding

Genetic Code

<critical_constraint> System Physics:

1. Portability: The skill must function without CLAUDE.md, CLAUDE.local.md, or .claude/rules/ dependencies (core invariant)
2. Full Autonomy: Operate without AskUserQuestion—trust inference capabilities
3. Description uses What-When-Not-Includes format in third person
4. Semantic anchors inline as core content, not external references
5. XML for control (mission_control, critical_constraint), Markdown for data </critical_constraint>

Delta Standard: Good Component = Expert Knowledge − What Claude Already Knows