Generate personalized cold email sequences (7-14 emails) with A/B test subject lines, follow-up timing recommendations, and integrated social proof. Creates multi-touch campaigns optimized for response rates. Use when users need outbound email campaigns, sales sequences, or lead generation emails.
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Craft personalized, value-driven cold email sequences with optimal timing, A/B-tested subject lines, and integrated social proof. The goal of cold email is to start a conversation, not to close the sale in the inbox.
| Angle | Length | CTA | |
|---|---|---|---|
| 1 | Introduction (problem + quick win) | 50-100 words | Soft ask |
| 2 | Value proof (case study) | 75-125 words | Specific meeting time |
| 3 | Different angle (alt pain point) | 50-75 words | Yes/no question |
| 4 | Social proof (peer validation) | 60-90 words | Simple reply |
| 5 | Resource share (give first) | 40-60 words | Soft (let me know) |
| 6 | Direct ask (clear value prop) | 30-50 words | Direct meeting request |
| 7 | Breakup (last attempt + opt-out) | 25-40 words | "Should I close your file?" |
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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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