Original Summary

the biggest mistake i see people make when trying to automate agency work is taking a 10 page standard operating procedure document and pasting the whole thing into claude with "follow this step by step." it works fine for the first two steps, then the AI starts skipping checks, summarizing the output, and hallucinating details halfway through. three weeks later the team gives up and goes back to doing it manually. the problem isnt that claude cant handle your process. the problem is that human SOPs are written for human brains, not AI context windows. humans rely on implicit assumptions, past experience, and visual judgment. when you hand a human SOP to an AI, those gaps get filled with guesses. heres the 4-step framework we use to convert complex, multi-page agency SOPs into production-ready claude skills that run consistently every time. strip out the human fluff and extract the core decision trees most SOPs are 70% filler. explanations of why the task matters, corporate background, screenshots of where buttons are located. AI doesn't need any of that. read through your SOP and pull out only three things: inputs: what exact raw data is needed to start (eg, brand voice doc, client UR L, raw transcript, competitor list) decision logic: the explicit rules for how choices are made (eg, "if word count is under 1,500, flag as thin content") outputs: the exact structural format required at the end (JSON schema, markdown tables, specific header hierarchy) if a step requires "gut feeling" or "creative judgment," you have to break down what actually drives that judgment into explicit rules. structure the process into isolated execution blocks dont try to make one single prompt handle an entire 8-step workflow from start to finish. the output quality drops exponentially with every additional task you stack into one instruction block. instead, split your SOP into small, self-contained sub-tasks within your skill file. for example, if you are converting a content brief SOP: step A: scrape and parse the top 10 search results step B: extract headings and calculate average word count step C: cross-reference missing semantic topics against competitor outlines step D: generate the final brief layout by forcing claude to execute and validate step A before moving to step B, you eliminate the compounding errors that ruin long-form outputs. encode explicit rubrics and negative constraints telling an AI "write a good email subject line" gives you generic marketing garbage. to get agency-grade output, your skill file needs strict scoring rubrics and explicit negative rules. in your skill markdown, write out what counts as a fail: "never use promotional hype words like game-changer, revolutionary, or skyrocket." "never use structural punctuation like colons, semicolons, or em-dashes in titles." "if the calculated word count target exceeds 2,500 words, trigger a human review flag instead of

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · Entrepreneur
  • 发布时间：2026/10/15 01:50:01