工程师分享：Tag 每天完成超 50% 的 PR 和全部数据分析
有人把 Claude 接进 Slack 当团队自动化员工用，复现 bug 自动提 PR、花千万 token 挖数据，4 条提示词直接抄走。
一位工程师在 X 上分享日常用法：Claude 驱动的 Tag 每天替他写超过 50% 的 PR，数据分析约占 100%，并自动修复多数产品反馈和 bug。
搭配 Opus 5.5 与 Fable 5.
1 模型，Tag 具备主动性、记忆和连接器访问能力。他给出 4 条实际提示词，包括让 Claude 在频道里复现 bug 并提 PR、用约 100 个假设和约 1000 万 token 深挖异常数据、生成代码讲解游戏和幻灯片。
Tag writes >50% of my PRs every day. It also does ~100% of my data analysis, and fixes most of our product feedback + bugs.
It's not like a regular Slack bot. It is proactive, programmable, it has memory, it has access to your connectors. With Opus 5.5 and Fable 5.1, it has strong judgement.
Example prompts I use:
- "@Claude react to threads in this channel with ✅ when resolved" - "@Claude from now on, attempt to repro every bug in this channel end to end, running the full app. once you reproduce the bug, put up a pr to fix it and tag the right team for code review" - "@Claude brainstorm ~100 hypotheses for what could explain this weird data, use a workflow to in/validate. spend 10m tokens or so digging very deep. draw a chart with the result." - "@Claude make me an interactive game explaining how this part of the code works, then make a slide deck explaining it for others on the team"
https://t.co/gmsALM8Lvs
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Claude, Opus 5.5, Slack, 提示词工程.
Source attribution: curated from Boris Cherny (@bcherny). Cross-source corroboration: 11 independent sources have covered this development (including Claude, TestingCatalog, IT之家), confirming industry-wide signal strength.
