TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.
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Produce rigorous, investor-grade TAM/SAM/SOM analyses by combining top-down macro data with bottom-up unit economics, triangulating the two, and always showing the work, citing sources, flagging assumptions, and providing sensitivity ranges.
references/research-sources.md — source categories and search queries for every research lane.references/methodology.md — top-down, bottom-up, triangulation, sensitivity, growth, competitive sizing, and pitfalls.references/output-template.md — the full market-sizing.md document template, Mermaid charts, and quality checklist.Confirm these four inputs before proceeding. If any is missing or ambiguous, ask first.
| Parameter | Description | Example |
|---|---|---|
| Industry | The broad industry or sector | "Enterprise SaaS", "Electric Vehicles" |
| Product/Service | The specific offering being sized | "AI-powered code review tool" |
| Geography | Target market geography | "United States", "Global", "DACH region" |
| Target Segment | The specific customer segment | "Mid-market companies (100-1000 employees)" |
references/research-sources.md. Log every source URL and date as you go.references/methodology.md.references/methodology.md.market-sizing.md using the structure in references/output-template.md. Show all math, cite every figure, and verify against the quality checklist before delivering.references/methodology.md.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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