Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Analyzes a company's content to extract and codify their brand voice into a comprehensive style guide. Reads website copy, blog posts, emails, and social media to identify tone, vocabulary patterns, sentence structure, personality traits, and word preferences. Generates a brand-voice-guide.md and reviews new content against it.
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Ingest a company's existing content, reverse-engineer the voice behind it, produce a definitive brand voice guide any writer can follow, and review new content against an established guide.
references/voice-dimensions.md -- the seven-axis tone spectrum, vocabulary, sentence, personality, audience, and formatting dimensions to extractreferences/guide-template.md -- required sections, quality standards, and pre-delivery checklist for brand-voice-guide.mdreferences/review-mode.md -- process and output format for scoring content against a guidereferences/metrics.md -- quantitative and qualitative analysis techniques and citation standardsreferences/edge-cases.md -- handling for thin corpora, multiple voices, regulated industries, rebrands, plus interaction patternsreferences/voice-dimensions.md, citing source examples per the standards in references/metrics.md.brand-voice-guide.md following references/guide-template.md. Verify it against that checklist before delivery.brand-voice-guide.md.references/review-mode.md.brand-voice-guide.md.brand-voice-guide.md. For multiple brands: brand-voice-guide-[company-name].md. Deliver review output inline unless a file is requested.No reviews yet. Tell others what you thought.
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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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