Write an AI-optimized PRD using multi-AI orchestration — use when scoping a new feature or product
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tessl review fix ./.claude/skills/skill-prd/SKILL.mdDO NOT call Skill() again. DO NOT load any more skills. Execute directly.
Before writing ANY PRD content, you MUST ask the user these questions:
I need to understand your requirements before creating the PRD.
1. **Target Users**: Who will use this? (developers, end-users, admins, etc.)
2. **Core Problem**: What specific pain point does this solve? Any metrics?
3. **Success Criteria**: How will you measure if this succeeds?
4. **Constraints**: Any technical, budget, or timeline constraints?
5. **Existing Context**: Is this greenfield or integrating with existing systems?
Please answer these (even briefly) so I can create a more targeted PRD.WAIT for user response before proceeding to Phase 1.
If user says "skip" or provides the feature description inline, extract what you can and note assumptions.
Only search if topic is unfamiliar. Limit to 2 web searches max:
Do NOT over-research. 60 seconds max for this phase.
Structure:
After drafting the PRD but BEFORE self-scoring, dispatch the draft to a second provider for adversarial review. A single-model PRD has blind spots — cross-provider challenge surfaces wrong assumptions, uncovered scenarios, and contradictory requirements.
Dispatch the PRD draft to a different provider (Codex, Gemini, or Sonnet as fallback) with this prompt:
"Challenge this PRD. What assumptions are wrong? What user scenarios are missing? What requirements contradict each other? What will the first user complaint be? What risk does this PRD ignore?"
After receiving the challenge:
Adversarial review: appliedSkip with --fast or when user requests speed over thoroughness. See prd.md command for full dispatch syntax.
Score against 100-point framework:
Write to user-specified filename or generate based on feature name.
START WITH PHASE 0 CLARIFICATION QUESTIONS NOW.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.