Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Turn real customer reviews, quotes, and case-study results into polished social-proof videos built in HyperFrames -- single-testimonial spotlights, review-wall montages, and stat-driven proof reels with captions and voiceover, cut for vertical, square, and wide. The highest-converting content most businesses never make.
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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。
Nothing sells like a customer saying it. This skill takes the proof a business already has -- Google reviews, testimonial emails, case-study numbers, NPS comments -- and directs it into renderable HyperFrames video. It owns the selection and the story; for composition mechanics, captions, transitions, and TTS, invoke the hyperframes skill (hyperframes-cli for init/lint/preview/render, hyperframes-media for voiceover).
Take whatever exists: pasted reviews, a review export, customer-review-aggregator output, case-study docs, or a profile URL to pull from. For each candidate quote capture the text verbatim, the name/attribution available, the specific result mentioned, and the source.
Select for specificity: "They saved us $40K and two weeks" outperforms "Great company, highly recommend" every time. Rank candidates by concrete detail, emotional arc (skeptic-to-believer beats always-happy), and relevance to the audience the video targets.
claude-design-system-architect or design-style-installer token set if present); no purple, no emoji.Platform cuts (9:16, 1:1, 16:9), a thumbnail frame, and the caption text for the post. Note where each cut runs best: review wall for the Google Business Profile and Instagram, spotlight for the website hero and proposals, proof reel for paid.
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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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