Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Takes a manual business workflow description and designs the automated version. Maps current steps, handoffs, decision points, and bottlenecks. Designs automated flow with triggers, conditions, actions, and error handling. Outputs workflow-automation.md with before/after Mermaid diagrams, tool recommendations, implementation steps, and time savings estimate.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Transform a manual business workflow into an optimized automated system: map how work gets done today, then design a complete automated replacement with triggers, conditions, actions, branching, and error handling. Deliver a comprehensive workflow-automation.md.
references/intake.md -- What to gather and how to ask for missing detailreferences/analysis-framework.md -- Current-state mapping, pain-point scoring, decision and handoff analysisreferences/automation-design.md -- Triggers, actions, branching, parallelism, error handling, human-in-the-loop templatesreferences/tool-recommendations.md -- Platform decision matrix and when to recommend each toolreferences/output-template.md -- Full workflow-automation.md structure to fill inreferences/diagram-and-estimation-standards.md -- Mermaid conventions and time/ROI estimation rulesreferences/intake.md.references/analysis-framework.md.references/analysis-framework.md.references/automation-design.md.references/tool-recommendations.md.references/diagram-and-estimation-standards.md.workflow-automation.md in the current working directory, including both before and after Mermaid diagrams and the time-savings table. See references/output-template.md and references/diagram-and-estimation-standards.md.Before delivering, verify:
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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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