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Get Started Free →Write an AI-optimized PRD using multi-AI orchestration — use when scoping a new feature or product
.claude/skills/hashgraph-online-skill-prd/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -68% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -67% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -63% | 0% |
| case-05 | ✓→✗ | ▼ Worse | -81% | 0% |
| case-06 | ✓→✗ | ▼ Worse | -47% | 0% |
> Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. > Cross-reference commands use installed skill names in Codex rather than /octo:* slash commands. > Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. > For host tool equivalents, see skills/blocks/codex-host-adapter.md.
DO 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, Antigravity, 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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-10 | fail→fail | 20,296 | 25,867 | +27% | 1 | 1 | 0% | 3,588 | 4,451 | +24% | 0 | 0 | — |
case-21 | pass→pass | 20,248 | 32,089 | +58% | 1 | 1 | 0% | 3,190 | 6,098 | +91% | 0 | 0 | — |
case-22 | fail→fail | 7,948 | 7,299 | -8% | 1 | 1 | 0% | 1,384 | 2,167 | +57% | 0 | 0 | — |
case-01 | fail→pass | 28,299 | 8,003 | -72% | 1 | 1 | 0% | 4,034 | 1,296 | -68% | 0 | 0 | — |
case-02 | fail→pass | 22,347 | 3,728 | -83% | 1 | 1 | 0% | 4,103 | 1,366 | -67% | 0 | 0 | — |
case-03 | fail→pass | 21,399 | 7,943 | -63% | 1 | 1 | 0% | 3,691 | 1,376 | -63% | 0 | 0 | — |
case-04 | fail→fail | 27,465 | 45,119 | +64% | 1 | 1 | 0% | 3,319 | 8,286 | +150% | 0 | 0 | — |
case-05 | pass→fail | 49,096 | 4,541 | -91% | 1 | 1 | 0% | 8,223 | 1,569 | -81% | 0 | 0 | — |
case-06 | pass→fail | 17,848 | 8,845 | -50% | 1 | 1 | 0% | 3,022 | 1,599 | -47% | 0 | 0 | — |
case-07 | pass→fail | 14,940 | 2,689 | -82% | 1 | 1 | 0% | 2,776 | 1,456 | -48% | 0 | 0 | — |
case-08 | pass→pass | 22,356 | 5,487 | -75% | 1 | 1 | 0% | 2,938 | 1,884 | -36% | 0 | 0 | — |
case-09 | pass→pass | 14,137 | 14,378 | +2% | 1 | 1 | 0% | 2,282 | 2,448 | +7% | 0 | 0 | — |
case-11 | pass→pass | 22,309 | 15,512 | -30% | 1 | 1 | 0% | 2,814 | 3,473 | +23% | 0 | 0 | — |
case-12 | fail→fail | 15,188 | 7,416 | -51% | 1 | 1 | 0% | 2,358 | 2,363 | +0% | 0 | 0 | — |
case-13 | pass→fail | 18,613 | 4,605 | -75% | 1 | 1 | 0% | 3,263 | 1,709 | -48% | 0 | 0 | — |
case-14 | fail→fail | 24,683 | 8,372 | -66% | 1 | 1 | 0% | 3,411 | 2,336 | -32% | 0 | 0 | — |
case-15 | pass→fail | 12,692 | 13,072 | +3% | 1 | 1 | 0% | 2,175 | 2,287 | +5% | 0 | 0 | — |
case-16 | pass→pass | 16,412 | 8,163 | -50% | 1 | 1 | 0% | 2,569 | 2,412 | -6% | 0 | 0 | — |
case-17 | pass→pass | 12,368 | 2,814 | -77% | 1 | 1 | 0% | 1,195 | 1,361 | +14% | 0 | 0 | — |
case-18 | fail→fail | 13,067 | 8,892 | -32% | 1 | 1 | 0% | 1,309 | 1,518 | +16% | 0 | 0 | — |
case-19 | pass→fail | 17,375 | 3,668 | -79% | 1 | 1 | 0% | 2,005 | 1,513 | -25% | 0 | 0 | — |
case-20 | pass→fail | 41,223 | 3,704 | -91% | 1 | 1 | 0% | 8,218 | 1,572 | -81% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of -38 percentage points is the difference between those two pass rates over the 22 comparable cases. 9 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.