Auto-generates weekly KPI reports from multiple data sources including Supabase analytics, CRM data, financial spreadsheets, and email metrics. Produces executive-ready reports with dashboards, trends, highlights, concerns, and action items.
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Pull data from multiple business systems, synthesize it into a coherent narrative, and produce an executive-ready weekly report that turns raw numbers into strategic insight.
references/configuration.md — first-run setup questions, directory layout, full config.yaml, and kpi-history.json schema.references/data-and-calculations.md — per-source data collection, trend indicators, target comparison, rolling averages, anomaly detection, comparison modes, custom KPI groups, quality rules, and error handling.references/output-template.md — the full report markdown template and detail levels.references/configuration.md.config.yaml and templates/kpi-definitions.yaml from the schema in references/configuration.md.history/kpi-history.json using the schema in references/configuration.md.config.yaml and templates/kpi-definitions.yaml.data/{date}/. See references/data-and-calculations.md.references/data-and-calculations.md.references/output-template.md, tailored to each recipient's detail level.reports/{date}-weekly-report.md and append this week's values to history/kpi-history.json.references/data-and-calculations.md).Apply the report quality rules and error handling in references/data-and-calculations.md to every run.
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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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