𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝘀𝗸𝗶𝗹𝗹𝘀? 𝗧𝗿𝘆 𝗼𝘂𝗿 ...

Milvus on X: "𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝘀𝗸𝗶𝗹𝗹𝘀? 𝗧𝗿𝘆 𝗼𝘂𝗿 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹 𝗺𝗲𝗺𝘀𝗲𝗿𝗮𝗰𝗵 𝘁𝗵𝗮𝘁'𝘀 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝘁𝗵𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲, 𝗺𝗲𝗺𝗼𝗿𝘆-𝘁𝗼-𝘀𝗸𝗶𝗹𝗹. 𝗔𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝘁𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝘀 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀: 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗸𝗻𝗼𝘄𝘀, 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗵𝗮𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗶𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝗻𝗴𝘀. Developers used to keep those in three separate systems: knowledge went into RAG, experience piled up in an agent memory layer, and procedures were written down as skill files. But those memories — debug sessions, API integrations, releases, user corrections — are essentially part of the agent's skills too. Each one carries the scenario, the feedback, and the outcome. Plenty of reusable workflows are already hiding in there. 𝗦𝗼 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗠𝗲𝗺𝗼𝗿𝘆-𝘁𝗼-𝗦𝗸𝗶𝗹𝗹 𝗶𝗻𝘁𝗼 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵, 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝘁𝗵𝗶𝘀 𝘆𝗲𝗮𝗿. An opt-in background pass mines your history for the workflows that keep recurring and are worth capturing, then automatically distills them into candidate skills. If a release workflow keeps showing up, the system recognizes it can be abstracted into a skill and writes it up as a candidate file. When the workflow changes later, the candidate evolves with it. 𝗕𝘂𝘁 𝗻𝗼 𝗮𝗴𝗲𝗻𝘁 𝗲𝘃𝗲𝗿 𝗮𝘂𝘁𝗼-𝗹𝗼𝗮𝗱𝘀 𝗮 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝘀𝗸𝗶𝗹𝗹. It stays a draft. You review it, edit it, confirm it, and then install it into the skill directory of Claude Code, Codex, or other agents you use. 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵 𝗶𝘀 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗮𝗹𝘀𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗼𝗿 𝗰𝗿𝗼𝘀𝘀-𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗴𝗶𝘃𝗲 𝗶𝘁 𝗮 𝘁𝗿𝘆: https://t.co/eGAavvLonV" / X

Milvus

@milvusio

𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝘄𝗶𝘁𝗵 𝘀𝗸𝗶𝗹𝗹𝘀? 𝗧𝗿𝘆 𝗼𝘂𝗿 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗼𝗼𝗹 𝗺𝗲𝗺𝘀𝗲𝗿𝗮𝗰𝗵 𝘁𝗵𝗮𝘁'𝘀 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝘁𝗵𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲, 𝗺𝗲𝗺𝗼𝗿𝘆-𝘁𝗼-𝘀𝗸𝗶𝗹𝗹. 𝗔𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝘁𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝘀 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀: 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗸𝗻𝗼𝘄𝘀, 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗵𝗮𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝗱, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗶𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝗻𝗴𝘀. Developers used to keep those in three separate systems: knowledge went into RAG, experience piled up in an agent memory layer, and procedures were written down as skill files. But those memories — debug sessions, API integrations, releases, user corrections — are essentially part of the agent's skills too. Each one carries the scenario, the feedback, and the outcome. Plenty of reusable workflows are already hiding in there. 𝗦𝗼 𝘄𝗲 𝗯𝘂𝗶𝗹𝘁 𝗠𝗲𝗺𝗼𝗿𝘆-𝘁𝗼-𝗦𝗸𝗶𝗹𝗹 𝗶𝗻𝘁𝗼 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵, 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝘄𝗲 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝗲𝗮𝗿𝗹𝗶𝗲𝗿 𝘁𝗵𝗶𝘀 𝘆𝗲𝗮𝗿. An opt-in background pass mines your history for the workflows that keep recurring and are worth capturing, then automatically distills them into candidate skills. If a release workflow keeps showing up, the system recognizes it can be abstracted into a skill and writes it up as a candidate file. When the workflow changes later, the candidate evolves with it. 𝗕𝘂𝘁 𝗻𝗼 𝗮𝗴𝗲𝗻𝘁 𝗲𝘃𝗲𝗿 𝗮𝘂𝘁𝗼-𝗹𝗼𝗮𝗱𝘀 𝗮 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝘀𝗸𝗶𝗹𝗹. It stays a draft. You review it, edit it, confirm it, and then install it into the skill directory of Claude Code, Codex, or other agents you use. 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵 𝗶𝘀 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲. 𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗮𝗹𝘀𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗮𝗴𝗲𝗻𝘁 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗼𝗿 𝗰𝗿𝗼𝘀𝘀-𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗴𝗶𝘃𝗲 𝗶𝘁 𝗮 𝘁𝗿𝘆:

github.com/zilliztech/mem…

3:30 PM · Jul 20, 2026

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