# Paw Work Shaping > Interactive pre-spec ideation utility skill. Agent-led Q&A to progressively clarify vague ideas, research codebase context, and produce structured WorkShaping.md artifact. Use when this capability is needed. - Skill: `tomevault-io/paw-work-shaping` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/paw-work-shaping` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/paw-work-shaping/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/paw-work-shaping --- # 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//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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/lossyrob) — claim your Tome and manage your conversions.