Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Generates full consulting proposals from a brief. Input client name, problem description, and rough scope. Outputs proposal.md with executive summary, problem statement, proposed approach, timeline, team, pricing tiers, and terms. Researches client company for personalization. Multiple pricing models. Professional formatting matching consulting standards.
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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。
Transform a brief description into a complete, professionally formatted consulting proposal (proposal.md), researching the client company for personalization and structuring the document with proven consulting frameworks.
references/inputs.md -- Required and optional inputs, defaults, one-liner extractionreferences/research.md -- Client web research checklist and research brief templatereferences/problem-and-solution.md -- Problem decomposition, methodology selection, phase and deliverable templatesreferences/timeline.md -- Duration estimation and timeline formatreferences/team.md -- Role framework and team section formatreferences/pricing.md -- Pricing models, 3-tier framework, rate benchmarks, calibration, payment termsreferences/terms.md -- Standard terms and conditions blockreferences/output-template.md -- Full proposal document structure and delivery summaryreferences/tone-and-quality.md -- Tone variants, pre-delivery quality checklist, error handlingreferences/inputs.md.references/research.md.references/problem-and-solution.md.references/timeline.md.references/team.md.references/pricing.md.references/terms.md.references/output-template.md.references/tone-and-quality.md.references/output-template.md.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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