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Uses MCP Connectors to read Gmail inbound leads, score them by ICP fit, draft personalized responses, and log qualified leads to your CRM. Turns your inbox into an automated pipeline.
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Turn a Gmail inbox into a structured sales pipeline using Claude's MCP Connectors -- the Gmail connector reads and drafts email, and the Supabase connector persists CRM data. No external scripts or API keys required.
pipeline_config table. See references/configuration.md (first run, error handling) and references/crm-schema.md.references/gmail-retrieval.md.references/gmail-retrieval.md.null rather than guessing. See references/lead-extraction.md.references/scoring-model.md.references/response-templates.md.references/crm-schema.md.lead-pipeline-report.md in the working directory, then display the executive summary. See references/pipeline-report.md.Between runs, handle manual lead commands (mark contacted, move stage, disqualify, add note, schedule follow-up, query leads) and ICP/search customization. See references/configuration.md.
references/gmail-retrieval.md -- Gmail MCP tools, search queries, parsing rules, extracted field set.references/lead-extraction.md -- Lead profile JSON schema and extraction guidelines.references/scoring-model.md -- ICP, intent, urgency scoring tables; tier mapping; adjustment rules.references/response-templates.md -- Per-tier response templates, personalization rules, anti-patterns, draft creation.references/crm-schema.md -- Supabase MCP tools, table DDL, logging procedure and SQL.references/pipeline-report.md -- Report Markdown structure and reporting SQL queries.references/configuration.md -- First-run setup, customization, manual commands, error handling, privacy, invocation triggers.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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