Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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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.
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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.
# 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.
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Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
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Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
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