Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.
Stack Honey onto Superpowers-style workflows (subagent-driven-development, dispatching-parallel-agents, executing-plans) and any orchestration that dispatches fresh subagents. Dispatched subagents run in isolated context and do NOT inherit the session's Honey hook, so they emit full-fat code and verbose reports. Use when you are about to dispatch implementer/reviewer/fixer/parallel subagents: it gives the exact Honey directive to paste into each dispatch prompt, with a worker variant and a reviewer variant, so the levers apply inside the subagents too — where the multiplied cost actually is.
Search, download, and publish agents/teams/skills on Markus Hub
Cross-domain loop orchestration for the chip design pipeline. Provides the fix_request protocol, iteration-cap logic, escalation templates, and dispatch patterns for routing verification/formal failures to the RTL orchestrator and back. Use when driving the closed-loop verification↔RTL feedback cycle.
初始化 RepoMind 业务知识库。全量重建 graphify 图谱，保守生成高置信 concepts/modules 文档，并为每个知识文件写入 name/description 元数据。执行前会自动修正旧格式；初始化结束后会询问是否继续导入 PRD 业务知识。
每次 AI 完成代码修改、问答或排查结论后，且最终答复前，必须同步做轻量 summary gate 判断；代码写完后也要自动进入 gate。只有发现可复用的新业务知识、用户纠错、手动要求记忆的经验、模块边界、排查经验或知识文档元数据变化时，才执行写入。按类型更新 concepts、modules、troubles 及每个文档自己的 name/description，避免回到集中式索引。
Define and populate agentic-commerce metafields (material, attributes, key features, specs, sizing) so AI agents can filter and match products to specific shopper requirements.
Score how findable, readable, and recommendable the store's catalog is to AI shopping agents — then route each gap to the agentic skill that fixes it.
Design and create agent skill packages — instruction-based and MCP-based skills, manifest format
Design and create AI team packages — manifest format, member structure, directory layout