Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Generates professional board meeting presentation content (board-deck.md) with executive summary, financials, product updates, GTM metrics, team/hiring, strategic decisions, and appendix. Supports early-stage, growth-stage, and pre-IPO formats. Use when preparing board meeting materials, quarterly board updates, or investor presentations.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Generate institutional-quality board-deck.md content: concise, data-driven, honest about challenges, and focused on the decisions that need board input. Output must be ready for a CEO to present directly or hand to a design team for slide conversion.
references/document-structure.md -- The required 12-section deck structure with all tables and templates.references/stage-templates.md -- Early-stage, growth-stage, and pre-IPO emphasis and per-section additions.references/style-guide.md -- Formatting rules, tone and voice, quality checklist, common mistakes.references/examples.md -- A fully worked board ask.Collect from the user. If critical inputs are missing, ask before generating. For non-critical gaps, insert bracketed placeholders like [INSERT Q3 REVENUE].
Required:
Optional: detailed P&L, product roadmap, GTM metrics, hiring plan, customer logos/NPS, competitive changes, specific board asks, prior board deck, cap table, prior-quarter OKRs.
references/stage-templates.md.[PLACEHOLDER] notation.references/document-structure.md, applying the stage-appropriate emphasis from references/stage-templates.md.references/examples.md.references/style-guide.md; run the quality checklist there before finalizing.board-deck.md in the current working directory (or a user-specified path). Confirm the file path after writing.还没有评价。欢迎分享你的使用感受。
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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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