Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
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Build an insurance-grade home inventory from a folder of photos and receipts -- identifies items, pulls values from receipts, estimates replacement costs, organizes by room, and outputs the documentation an insurance claim actually requires. Update mode keeps it current after new purchases.
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Build the document everyone wishes they had after the fire, flood, or break-in: a room-by-room inventory with values and proof. Input: a folder of photos (room shots, close-ups, serial-number shots) and receipts (PDF, email exports, images). Output: a structured inventory file plus a gap list of what still needs documenting.
ESTIMATE. Never present an estimate as a documented value.home-inventory.csv (or .xlsx via the xlsx skill) -- room, item, brand/model, serial, purchase date, purchase price, replacement estimate, documentation status, photo filename(s), receipt filename -- plus home-inventory-summary.md: totals by room and category, the high-value items list, and overall documented vs. estimated ratio.Re-run on the same folder after adding new photos/receipts: append new items, update matched ones, preserve manual edits to the CSV, and report what changed. As a quarterly Cowork scheduled task, sweep the receipts/email-export folder and deliver the diff.
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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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