name: paw-work-shaping description: Interactive pre-spec ideation utility skill. Agent-led Q&A to progressively clarify vague ideas, research codebase context, and produce structured WorkShaping.md artifact.

Work Shaping

Execution Context: This skill runs directly in the PAW session (not a subagent)—interactive Q&A by design.

Interactive ideation session to transform vague ideas into structured, spec-ready work items.

When to Use

• User explicitly asks to explore, shape, or flesh out an idea
• Request has exploratory language ("what if", "maybe we could", "I'm thinking about")
• User expresses uncertainty ("I'm not sure if...", "not sure how to approach")
• Idea is too vague for direct specification

Session Flow

1. Opening

Acknowledge the idea and set expectations:

• This is an exploratory conversation to clarify the work
• You'll ask questions to understand intent, constraints, and scope
• User can end anytime; you'll signal when "complete enough"

2. Progressive Clarification

Agent-led Q&A to build understanding:

Question strategy:

• One question at a time
• Start broad (intent, value proposition), then narrow (boundaries, constraints)
• Offer recommendations when you have informed opinions
• Prefer multiple choice when options are enumerable

Topics to explore (adapt based on idea):

• Core value: What problem does this solve? Who benefits?
• Scope boundaries: What's definitely in? What's explicitly out?
• User interactions: How will users engage with this?
• Edge cases: What happens when X fails/is empty/conflicts?
• Success definition: How will we know it works?
• Constraints: Performance, security, compatibility requirements?

Codebase research: When questions arise about existing system behavior, patterns, or integration points, delegate to paw-code-research skill via subagent with specific questions. Request findings returned in chat summary rather than full artifact generation. Integrate findings into the conversation.

3. Completion Detection

Signal "complete enough" when:

• Core value proposition is clear
• Scope boundaries are defined
• Major edge cases identified
• No critical unknowns remain

Offer to:

• Continue exploring specific areas
• Generate the WorkShaping.md artifact
• Hand off to specification stage

User can also end anytime with "that's enough", "let's write it up", etc.

4. Artifact Generation

Synthesize the conversation into WorkShaping.md.

Artifact Content

WorkShaping.md should capture:

• Problem statement (who benefits, what problem is solved)
• Work breakdown (core functionality vs supporting features)
• Edge cases with expected handling
• Rough architecture (component interactions, data flow)
• Critical analysis (value assessment, build vs modify tradeoffs)
• Codebase fit (similar features, reuse opportunities)
• Risk assessment (potential negative impacts, gotchas)
• Open questions for downstream stages
• Session notes: key decisions and insights from the Q&A (e.g., scope decisions, rejected alternatives, surprising discoveries)

Use clear section headers. Omit sections that don't apply.

Artifact Location

Primary: .paw/work/<work-id>/WorkShaping.md (if work directory exists)

Fallback: Workspace root. Prompt user for alternate location if needed.

Quality Checklist

• Problem statement is clear and user-focused
• Work breakdown covers core and supporting functionality
• Edge cases enumerated with expected handling
• Architecture sketch shows component relationships
• Critical analysis includes value assessment and tradeoffs
• Codebase fit identifies reuse opportunities
• Risks and gotchas documented
• Open questions captured for downstream stages