Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Run a go-to-market war room for a launch, repositioning, price change, or new product. Convenes opposing expert and customer-persona viewpoints to debate the plan, surfaces the risks that kill launches, and returns a go/no-go call plus a phased rollout plan with owners, sequencing, and kill criteria. Use before any launch you can't easily walk back.
.claude/skills/onewave-ai-product-launch-war-room/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -33% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 14% | 0% |
A launch fails for boring reasons: nobody owned the objection, the price change leaked, the segment that mattered wasn't in the room. This skill runs the war room that catches those reasons before launch day — adversarial by design, decision-oriented, and grounded in your real customers when data is available.
It composes the panel skills (customer-panel-of-experts, prospect-panel-simulator) into a single GTM decision and a rollout plan you can execute.
pricing-change-strategist)Lock these before any debate:
Seat both sides. Use icp-deep-scanner personas where the call is customer-facing; add functional experts for execution risk:
personas/; if absent and tools are connected, run icp-deep-scanner read-only; otherwise mark PROVISIONAL).For a thorough war room, dispatch parallel sub-agents (one per role) via /agent-army, then synthesize. Connections are read-only; no external sends; no real PII; secrets in env vars only.
markdown# Launch War Room — {Launch} Generated: {timestamp} · Room: {roles/personas} · Reversibility: {…} · Grounding: {data / PROVISIONAL} ## Call: {GO / GO WITH CHANGES / DELAY / NO-GO} The reasoning in one paragraph. ## Top risks (ranked) | Risk | Likelihood | Blast radius | Owner | Mitigation | Pre-launch or live? | ## Required changes before launch - The non-negotiables surfaced by the room. ## Phased rollout - Phase 0 — prep & internal enablement (sales, support scripts, FAQ) - Phase 1 — soft launch / segment / beta + what we watch - Phase 2 — full launch + channels + sequencing - Phase 3 — post-launch monitoring window ## Kill criteria & rollback - The specific metric thresholds that trigger pause or rollback, and how to walk it back. ## Owners & timeline | Workstream | Owner | Deadline | Dependency | ## Comms kit to produce next - Customer email, sales talk track, support FAQ, objection handling, public page.
Offer to generate the comms kit (cold-email-sequence-generator, landing-page-copywriter, company-announcement-writer), route a price change to pricing-change-strategist, build the launch video with hyperframes-ad-director, or re-run the room against the revised plan.
Adversarial by default — the room's job is to find the failure, not bless the plan · rigor scales with reversibility · kill criteria are mandatory, not optional · read-only connections, no sends, no real PII · provisional grounding is labeled.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→fail | 29,937 | 23,355 | -22% | 1 | 1 | 0% | 5,384 | 5,026 | -7% | 0 | 0 | — |
case-01 | fail→pass | 38,182 | 34,847 | -9% | 1 | 1 | 0% | 6,269 | 4,225 | -33% | 0 | 0 | — |
case-02 | fail→fail | 33,951 | 19,813 | -42% | 1 | 1 | 0% | 5,838 | 4,279 | -27% | 0 | 0 | — |
case-04 | pass→pass | 17,320 | 19,211 | +11% | 1 | 1 | 0% | 3,034 | 4,149 | +37% | 0 | 0 | — |
case-05 | fail→pass | 23,400 | 22,921 | -2% | 1 | 1 | 0% | 2,937 | 4,955 | +69% | 0 | 0 | — |
case-06 | fail→pass | 17,446 | 14,840 | -15% | 1 | 1 | 0% | 2,879 | 3,575 | +24% | 0 | 0 | — |
case-07 | fail→pass | 17,028 | 22,109 | +30% | 1 | 1 | 0% | 2,958 | 4,258 | +44% | 0 | 0 | — |
case-08 | fail→pass | 22,078 | 18,706 | -15% | 1 | 1 | 0% | 3,345 | 3,799 | +14% | 0 | 0 | — |
case-09 | fail→pass | 17,958 | 21,732 | +21% | 1 | 1 | 0% | 3,227 | 4,529 | +40% | 0 | 0 | — |
case-10 | fail→fail | 38,461 | 33,878 | -12% | 1 | 1 | 0% | 6,180 | 6,616 | +7% | 0 | 0 | — |
case-11 | fail→fail | 19,656 | 26,640 | +36% | 1 | 1 | 0% | 2,919 | 5,021 | +72% | 0 | 0 | — |
case-12 | fail→fail | 14,433 | 19,544 | +35% | 1 | 1 | 0% | 2,341 | 3,859 | +65% | 0 | 0 | — |
case-13 | fail→fail | 19,336 | 18,449 | -5% | 1 | 1 | 0% | 3,273 | 4,104 | +25% | 0 | 0 | — |
case-14 | fail→pass | 14,440 | 13,716 | -5% | 1 | 1 | 0% | 2,417 | 3,274 | +35% | 0 | 0 | — |
case-15 | pass→pass | 8,127 | 12,583 | +55% | 1 | 1 | 0% | 1,306 | 3,090 | +137% | 0 | 0 | — |
case-16 | fail→pass | 11,211 | 18,854 | +68% | 1 | 1 | 0% | 1,904 | 4,109 | +116% | 0 | 0 | — |
case-17 | fail→fail | 14,857 | 9,301 | -37% | 1 | 1 | 0% | 2,363 | 2,203 | -7% | 0 | 0 | — |
case-18 | pass→pass | 15,517 | 16,082 | +4% | 1 | 1 | 0% | 2,526 | 3,736 | +48% | 0 | 0 | — |
case-19 | fail→fail | 18,848 | 18,761 | -0% | 1 | 1 | 0% | 2,961 | 4,113 | +39% | 0 | 0 | — |
case-20 | pass→pass | 13,942 | 12,348 | -11% | 1 | 1 | 0% | 2,375 | 2,902 | +22% | 0 | 0 | — |
case-21 | pass→pass | 4,707 | 4,622 | -2% | 1 | 1 | 0% | 970 | 1,868 | +93% | 0 | 0 | — |
case-22 | pass→pass | 13,922 | 10,428 | -25% | 1 | 1 | 0% | 2,409 | 2,791 | +16% | 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 +36 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.