[{"data":1,"prerenderedAt":122},["ShallowReactive",2],{"\u002Fwork\u002Fprojects\u002Fai-ops-recap-agent":3},{"id":4,"title":5,"body":6,"demo":97,"description":98,"extension":99,"featured":100,"github":97,"meta":101,"navigation":100,"ogImage":97,"order":102,"path":103,"plannedImprovements":104,"problem":109,"publishedAt":110,"seo":111,"status":112,"stem":113,"summary":114,"technologies":115,"__hash__":121},"projects\u002Fwork\u002Fprojects\u002Fai-ops-recap-agent.md","AI Operations Recap Agent",{"type":7,"value":8,"toc":85},"minimark",[9,14,24,28,31,35,40,44,47,51,58,62,65,69],[10,11,13],"h2",{"id":12},"one-sentence-explanation","One-sentence explanation",[15,16,17,18,23],"p",{},"An agent that reads Git commits, issue-tracker activity, task lists, and calendar events, and turns\nthem into a short, accurate daily or weekly operational recap — a practical, small-scale example of\nthe ",[19,20,22],"a",{"href":21},"\u002Fwork\u002Fcase-studies\u002Fai-assisted-automation","AI-assisted workflow"," I use day to day.",[10,25,27],{"id":26},"the-problem-being-solved","The problem being solved",[15,29,30],{},"Reconstructing \"what actually happened this week\" across commits, tickets, and meetings is tedious\nand easy to get wrong from memory. It's also a well-scoped, low-risk task for an AI agent: the\nsource data is concrete and verifiable, so the agent's job is synthesis and summarization, not\njudgment calls with real consequences — a good fit for AI assistance precisely because the failure\nmode of a bad summary is low-stakes and easy to spot.",[10,32,34],{"id":33},"planned-architecture","Planned architecture",[36,37],"architecture-diagram",{":steps":38,"title":39},"[{\"label\":\"Collect\",\"detail\":\"Git, issues, tasks, calendar\"},{\"label\":\"Normalize\",\"detail\":\"Common event schema\"},{\"label\":\"Summarize\",\"detail\":\"Claude API, grounded in source data\"},{\"label\":\"Deliver\",\"detail\":\"Daily\u002Fweekly recap, scheduled\"}]","Recap agent — planned shape",[10,41,43],{"id":42},"technology-stack","Technology stack",[15,45,46],{},"A scheduled job (Python or TypeScript) pulling from the GitHub API, a calendar API, and whatever\ntask tracker is in use, normalized into a common event schema, then summarized with the Claude API\nusing the source events as grounding context — not free-form generation.",[10,48,50],{"id":49},"current-status","Current status",[15,52,53,57],{},[54,55,56],"strong",{},"Concept — design complete, implementation not yet started."," This is the smallest and fastest of\nthe planned public projects to build, and a good candidate for a first real implementation. No\npublic repository exists yet.",[10,59,61],{"id":60},"what-i-expect-to-learn","What I expect to learn",[15,63,64],{},"The interesting constraint is keeping the summary grounded and verifiable — every line in the recap\nshould trace back to a specific commit, ticket, or event, not a plausible-sounding hallucination.\nThat's the same discipline described in the AI-assisted automation case study, applied to a much\nsmaller, lower-stakes tool.",[10,66,68],{"id":67},"planned-improvements","Planned improvements",[70,71,72,76,79,82],"ul",{},[73,74,75],"li",{},"Slack\u002Femail delivery",[73,77,78],{},"Configurable sources without code changes",[73,80,81],{},"A feedback loop to improve future summaries",[73,83,84],{},"Multi-project rollups",{"title":86,"searchDepth":87,"depth":87,"links":88},"",3,[89,91,92,93,94,95,96],{"id":12,"depth":90,"text":13},2,{"id":26,"depth":90,"text":27},{"id":33,"depth":90,"text":34},{"id":42,"depth":90,"text":43},{"id":49,"depth":90,"text":50},{"id":60,"depth":90,"text":61},{"id":67,"depth":90,"text":68},null,"A planned AI-assisted agent that pulls together Git activity, issue trackers, tasks, and calendar events into a concise daily or weekly operational recap.","md",true,{},4,"\u002Fwork\u002Fprojects\u002Fai-ops-recap-agent",[105,106,107,108],"Slack\u002Femail delivery of the daily or weekly recap","Configurable sources (add\u002Fremove trackers without code changes)","A feedback loop so corrections improve future summaries","Multi-project rollups for people juggling more than one codebase","Staying on top of what actually happened across a week of commits, tickets, and meetings takes real effort to reconstruct after the fact — most of that synthesis work is exactly what an AI-assisted agent is good at, if it's grounded in real operational data instead of guessing.\n","2026-04-15",{"title":5,"description":98},"concept","work\u002Fprojects\u002Fai-ops-recap-agent","A tool that turns commits, issues, tasks, and calendar events into a daily operational summary.",[116,117,118,119,120],"Python or TypeScript","Claude API","Git \u002F GitHub API","Calendar API","Scheduled jobs","4UR_qp77igU7jLguHUdyjPByQ-MiuzynGkoZLSwfhgM",1785688079643]