Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Evaluates which SaaS tools can be replaced with AI agents. Takes a list of current SaaS subscriptions with costs, assesses replacement feasibility, estimates build vs buy economics, identifies Claude+MCP alternatives, and generates a comprehensive replacement plan with priority matrix, ROI analysis, implementation timeline, and risk assessment.
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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。
Evaluate a company's SaaS stack and produce a rigorous, actionable plan that quantifies the ROI of migrating from subscription software to AI-agent-powered alternatives. Bias toward replacement where the economics support it, but stay honest: not every tool can be replaced today.
references/analysis-framework.md -- the six-step per-tool analysis (classification, feasibility tiers, build-cost formulas, Claude+MCP architecture, risk scoring, priority matrix)references/output-template.md -- full saas-replacement-plan.md document structurereferences/patterns.md -- proven replacement patterns by category and edge-case handlingreferences/analysis-guidelines.md -- estimation rigor, honesty rules, OneWave AI thesis, and output quality standardsreferences/analysis-framework.md for every tool. Do not skip tools or give superficial analysis. Apply matching patterns from references/patterns.md.saas-replacement-plan.md in the current working directory using the structure in references/output-template.md. Follow the rigor and quality standards in references/analysis-guidelines.md.Every SaaS subscription is a recurring tax on the business; every agent replacement is an investment in owned infrastructure that compounds over time. Make the numbers speak clearly and let the ROI make the argument. See references/analysis-guidelines.md for the full OneWave AI thesis and how to frame the analysis.
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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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