July 3, 2026 | Read online
The Engineering Manager's AI Toolkit: 5 Problems AI Solves Better Than Spreadsheets
───
Hey,
Issue #014. Let's get into it.
🎥 Watch on YouTube: https://youtu.be/seDg08ZTXhM
───
⚙️ AI TOOL OF THE WEEK: Claude for Engineering Management
This entire episode is about Claude as an EM/TPM tool. The model doesn't matter as much as the prompt structure. Every prompt in this issue follows the same pattern: here is the raw context, here is what I need, here is the format. That's the whole framework. Let's apply it to five real problems.
───
🚀 DEVOPS + AI: The EM/TPM Prompt Toolkit
Engineering managers spend half their time on work that doesn't require a human. Status reports. Dependency tracking. Incident timelines. Retro summaries. Risk surfaces. Here are five prompts that handle all of it.
Problem 1: The Weekly Status Report
Here is a list of Jira tickets closed this week, pull requests merged,
and incidents resolved: [paste data]
Write a concise engineering status report for non-technical stakeholders.
Use plain language. Flag anything blocked or at risk.
Format: three sections — Shipped, Blocked, Watch List.
Feed it your JIRA export. 30 seconds. Better structured than what you'd write by hand.
Problem 2: Dependency Mapping
Here is a list of project tickets with descriptions: [paste tickets]
Identify all dependencies. State which ticket blocks which.
Flag circular dependencies and critical path risks.
Finds the one blocker that would have blown up your Q3 launch.
Problem 3: Incident Timeline Reconstruction
Here are the raw Slack messages and log excerpts from our incident
on [date]: [paste data]
Construct a chronological timeline. Note who took each action and its effect.
Flag gaps where information is missing.
2 hours → 5 minutes. More accurate. Claude doesn't forget the 2:47am log entry.
Problem 4: Retrospective Summary
Here are the notes from our retrospective: [paste notes]
Summarize: What Went Well, What Didn't, Action Items.
For each action item: suggest an owner and timeline.
Be direct — skip the feel-good filler.
Paste your Miro export. Structured output with draft owners in 2 minutes.
Problem 5: Pre-Release Risk Surface
Here is the architecture of our upcoming release: [describe system]
Identify the top 10 risk areas. For each: failure mode, likely impact,
one specific mitigation action to take before release.
Systematic risk enumeration. AI does this better than humans. Not a criticism — just true.
The pattern: Raw context + what you need + format. Start with whichever costs you the most time this week.
All 5 prompts on GitHub: github.com/mttaylor/deployordiecontent
───
⛓️ CRYPTO/WEB3 SIGNAL
DAOs are distributed engineering teams with treasury management bolted on. The same TPM problems exist — dependency tracking, risk surfaces, status reporting across contributors — but with no central authority to enforce process. AI-assisted coordination tools are becoming a real category here. Worth watching.
───
🔗 3 LINKS WORTH YOUR TIME
EP014 prompt templates on GitHub — all 5 prompts, copy and adapt
EP010 — OpenClaw workflow automation — automate the whole loop, not just one prompt