Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Stockouts burn revenue on the products that sell; overstock buries cash in the ones that do not. Both come from ordering by gut. Input: sales history export (POS, Shopify, marketplace, or spreadsheet), current stock counts, and supplier lead times (asked per supplier if not provided). Output: reorder points per SKU and the purchase list for this week.
WINNERS (high velocity, healthy margin -- never stock out), STEADY, SLOW, DEAD (no sales in 90+ days with stock on hand).reorder-plan.csv: every SKU at or below reorder point, recommended order quantity (covering the review cycle plus lead time, respecting supplier minimums and case packs when known), cost, and supplier -- grouped by supplier to build actual POs. Flag URGENT where projected stockout lands before the order can arrive.NEW -- judgment order, clearly separated from computed recommendations.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.