playbook/skills/thirdparty/pathfinding/references/questions.md

Question Format

Anatomy of a Good Question

Components:

1. Q{N}: Question number (for tracking)
2. Question: Clear, specific, focused on one decision
3. Why it matters: One sentence explaining impact
4. Options: 2–4 meaningful choices
5. Nuance: Brief context for each option
6. ★ Recommendation (optional): Your lean with reasoning

Delivery via EnterPlanMode

Use EnterPlanMode for each question — enables keyboard navigation.

Structure:

• Prose above tool: context, reasoning, ★ recommendation
• Inside tool: options only (concise, scannable)

Don't bury recommendations inside the tool — keep them visible in prose.

Crafting Options

Option Count Guidelines

2 options: Use when choices are binary or you want to keep it simple

• Good: "Web app or mobile app?"
• Avoid: Forcing false dichotomy when more options exist

3 options: Sweet spot for most questions

• Good: Covers main approaches plus one alternative
• Avoid: Making options too similar

4 options: Use when you need a combination or "other"

• Good: Three distinct approaches + a hybrid option
• Avoid: Analysis paralysis with too many choices

Option Quality

Good options:

• Mutually exclusive (can pick only one)
• Collectively exhaustive (covers reasonable space)
• Clearly differentiated (not subtle variations)
• Actionable (leads to concrete next steps)

Bad options:

• Overlapping: "Option 1: Use React. Option 2: Use modern framework."
• Too similar: "Option 1: 100ms timeout. Option 2: 150ms timeout."
• Vague: "Option 1: Do it the normal way."
• Open-ended: "Option 1: Whatever you think is best."

Why It Matters

The one-sentence explanation serves multiple purposes:

1. Context: Helps user understand why you're asking
2. Priority: Shows this isn't arbitrary
3. Decision framing: Clarifies what depends on this choice
4. Respect: Demonstrates you're not just asking for the sake of asking

Good examples:

• "Why it matters — determines database schema design"
• "Why it matters — affects performance characteristics and scaling strategy"
• "Why it matters — impacts user experience for first-time visitors"

Weak examples:

• "Why it matters — I need to know"
• "Why it matters — this is important"
• "Why it matters — because"

Adding Nuance

Each option should include helpful context:

Good nuance:

• Trade-offs: "Faster to implement but less flexible long-term"
• Implications: "Requires HTTPS and external dependency"
• Prerequisites: "Need existing user database"
• Typical use case: "Best for high-traffic applications"

Weak nuance:

• Restating the obvious: "Uses OAuth" (when option says OAuth)
• Generic statements: "Good option"
• No information: Just the option name with no context

Recommendations (★)

Use recommendations when:

• You have genuine expertise or insight
• One option clearly fits better for typical cases
• User seems uncertain or asks for guidance

Don't recommend when:

• Purely user preference (e.g., color scheme)
• Not enough context yet
• All options equally valid

Good: 1. React [★] — mature ecosystem *best starting point for most teams*

Weak:

• ★ I like this one
• ★ Most popular
• Recommendation buried in prose above options

User Replies

Number is a shorthand, not a constraint:

• 2 → selects option 2
• 2, but with caching → selection + modification
• 2 and 3 → combo
• What's the difference? → clarification request

All valid.

Adaptive Cadence

Baseline (~80% of questions):

• Clear question + one-sentence "why"
• 2–4 options with brief nuance
• Inline [★] on recommended option
• Optional: [★] { expanded reasoning } in prose above if helpful

Expand when:

• High ambiguity or risk
• User uncertain or asks for detail
• Technical complexity needs explanation

Simplify when:

• Straightforward question
• User shows expertise
• Question 6+ in session
• User wants to move faster