Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Audit websites for accessibility issues and WCAG compliance. Use when checking accessibility, fixing a11y issues, or ensuring WCAG compliance.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。