Framework for Automating Repetitive Work

Automation, AI workflow, or AI agent? To always 𝘬𝘯𝘰𝘸 𝘸𝘩𝘪𝘤𝘩 𝘰𝘯𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥, follow this 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬: Remember when I explained why many "𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴" shared on LinkedIn are actually 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise? Turns out: understanding the difference is only partially helpful. The real challenge is knowing 𝘸𝘩𝘪𝘤𝘩 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦. So I built this framework to help you decide. There are 6 key dimensions to consider - working in pairs: 𝐏𝐚𝐢𝐫 #1: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 ↔️ 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 aka. how decisions are made - and how much human intervention is required: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: You make ALL decisions upfront when designing your automation, which means that no human intervention is needed after. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: You set boundaries for the AI to operate within; humans occasionally review outputs or intervene when the system encounters edge cases. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: You set high-level goals, and AI determines its own path; this means humans need to provide ongoing feedback to ensure it makes the right decisions. 𝐏𝐚𝐢𝐫 #2: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 ↔️ 𝐀𝐝𝐚𝐩𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 a.k.a which type of data the system should process - and how adaptable it has to be: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Requires strictly predefined data formats with no deviation; breaks when encountering unexpected inputs and needs to be re-engineered when processes change. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Handles mostly structured data with some variability allowed; can adjust to parameter variations within defined parameters but needs guidance for significant changes. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Processes diverse unstructured data across multiple sources with varying formats; independently adapts to different inputs and shifting environments without reprogramming. 𝐏𝐚𝐢𝐫 #3: 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 ↔️ 𝐑𝐢𝐬𝐤 𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐜𝐞 a.k.a how predictable the outcomes must be - and what level of risk is acceptable: → 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯: Delivers highly consistent, predictable results every time; ideal for mission-critical processes where errors cannot be tolerated and predictability is essential. → 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸: Produces mostly reliable outcomes with occasional variations in edge cases; balances flexibility with guardrails to prevent major errors while allowing some adaptability. → 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵: Creates outcomes that can vary significantly between iterations; optimized for scenarios where discovering novel approaches and adaptability outweigh the need for consistent results. How to use this framework: Always 𝘴𝘵𝘢𝘳𝘵 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘭𝘦𝘧𝘵 and move right only when necessary. 1. Start with automation 2. Move to AI workflows when you need more flexibility within guardrails  3. Only move to agents when you need high adaptability Don’t fall for the AI agent hype - most processes can be automated without agents.

Claude Skills turns AI into a teammate that already knows how you work. Here’s how to use it to save 30–40 minutes on recurring tasks. Most people still use AI like a search engine. Prompt → answer → context disappears → repeat. Which means every task starts from zero. You re-explain: ↳ your role ↳ your tone ↳ your formatting preferences ↳ your constraints ↳ your workflow Claude Skills removes that friction. Instead of re-briefing the model every time, you encode the instructions once  and Claude loads them automatically when the task appears again. That turns AI from a tool into something closer to operational infrastructure. Here’s the simple structure behind every Skill: Role Who Claude is in this context. Rules What to do, what to avoid, tone, formatting, standards. Trigger The phrase or task that automatically loads the Skill. Once written well, the model already understands the job before you even start prompting. A practical way to start: Build Skills for the tasks you repeat every week. Examples: Writing Skill Role: LinkedIn editor Rules: concise, structured, actionable, no fluff Trigger: “Write a LinkedIn post” Research Skill Role: industry analyst Rules: summarize trends, cite sources, highlight implications Trigger: “Research / analyze” Meeting Skill Role: chief of staff Rules: extract decisions, action items, owners, risks Trigger: “Summarize meeting” Each one eliminates repeated prompting. Setup takes about 10 minutes. But you can reuse the Skill hundreds of times. The important shift is this: The AI advantage does not come from better prompts. It comes from designing reusable thinking systems. ↳ writing frameworks ↳ research workflows ↳ analysis checklists Once encoded properly, they become persistent intelligence. That’s how individuals begin building what companies are trying to build at scale: an AI operating layer around their work. Prompting helps. But workflow design creates the real leverage. 🔁 Repost if this helped clarify how to turn AI into a system, not just a tool. ➕ Follow Gabriel Millien for practical insights on AI execution and building real leverage with AI. Image credit: Chris Donnelly

agents that learn your workflows > agents that relearn you every day. I’m sharing a standout research report: Log2Plan, an adaptive GUI automation framework powered by task mining. It learns from real interaction logs, builds a reusable plan, and then adapts each step to the live screen. Think: global plan + local grounding, so agents get more reliable the longer you use them. ↳ Why this matters for UX/UI: ➤ Personalization without hero prompts, the system internalizes how you work (file paths, naming, exception paths). ➤ Recoverable runs, step-level checks and quick human-assist beats brittle macro replays. ➤ Transparent actions, structured plans you can read, audit, and improve. ➤ Resilience to UI drift, intent stays stable even when buttons and layouts move. ↳ What’s actually new here: ➤ Task mining turns messy click/keystroke logs into reusable “Task Groups” (ENV / ACT / Title / Description). ➤ Retrieval-augmented planning pulls the right pieces for a new goal, then the local planner fits them to the current screen. ➤ A clear separation of plan vs. interaction that reduces token bloat and flaky screenshot reasoning. ↳ Try this week (operator’s cut): ➤ Pick one high-volume desktop flow (e.g., monthly report collation). ➤ Curate 2–3 clean traces into “Task Groups.” ➤ Define success metrics (success rate, sub-task completion, time per task, assist rate). ➤ Add human-assist checkpoints for sensitive steps and ship a small pilot. Follow for more UX/UI & AI implementations. Re-share with your network.

Let's get straight to the point: if you want your AI or your entire workflow to deliver real, repeatable value, it's time to go beyond crafting clever prompts. To achieve scalable results, lasting creativity, and genuine peace of mind, you must build the scaffolding, not just issue instructions and hope for the best. Here's how to upgrade your approach: 1. Build frameworks, not just prompts. Think about the system you want, not just the answer you hope for. A reliable framework provides your agents (and your team) with a clear reasoning path to follow, time after time. 2. Apply heuristic principles. What are the rules, values, or questions that should guide every decision? Define them up front. This helps your work stay adaptable, responsible, and a whole lot smarter. 3. Design for consistency and growth. Set up your workflows so that improvement is built in. A simple feedback loop or a transparent review process turns one-off wins into repeatable habits. 4. Make ethics and transparency non-negotiable. Only trust systems you can explain. Build in ways to check your decisions, catch biases, and show your work as you go. Attached, you'll find a small section of Signal & Cipher's Creative Generalist framework, which defines creative patterns. These patterns govern how any AI agent responds to a request, providing consistent and predictable context every time. This is just one of over 50 frameworks our agents have access to for operating within our organizational AI OS. So, next time you're tempted to hack your way forward with another fancy prompt, hit pause. Choose one process you control and outline a fundamental framework for it. You'll be amazed by how much more predictable and robust your outcomes become.

Most QA engineers use AI like a search bar. Type a question, copy the answer, move on. Then wonder why the output does not fit their framework. The fix is giving it persistent context instead of re-explaining your project every session. Here is the setup I use: ▪️ A project instructions file ☑ Your framework and language, so it stops suggesting the wrong one ☑ Your locator strategy, so it stops writing brittle CSS chains ☑ Your folder structure, so new files land in the right place ☑ Your naming conventions for tests and fixtures ▪️ Rules worth writing down once ☑ Never use hardcoded waits, use web-first assertions ☑ Prefer user-facing locators over test ids over CSS ☑ Every test cleans up its own data ☑ No logic in page objects, only locators and actions ▪️ Where it actually saves time ☑ Turning a ticket into a first-draft test file ☑ Reading a failure trace and ranking likely causes ☑ Generating test data with boundary and invalid values ☑ Reviewing a pull request for testability problems ☑ Converting a manual test case into an automated one ▪️ Where it does not ☑ Deciding what is worth testing ☑ Knowing which bug actually matters to the business ☑ Owning the call on whether you ship The difference between a useful AI workflow and a frustrating one is almost never the model. It is whether you gave it your context or made it guess. Write the rules once. Stop repeating yourself. 📌 Save this if you are still pasting your whole framework into every chat. ♻️ Repost for the engineer who thinks AI cannot write tests that fit their codebase. What is in your AI setup that you would not give up? #AI #QAEngineer #TestAutomation #SoftwareTesting #SDET #Playwright #AITesting #Productivity #QualityAssurance #DevTools

After working with dozens of businesses across different industries… I started to notice a pattern. Most companies automate backwards. They pick a tool first. Then try to “make it fit” their process. And end up with automations that break, create more chaos, or never get fully adopted. So I built the Precision Automation Framework. A 5-step system designed to help businesses automate the right way: 🧭 Map the workflow: Understand every step before you automate anything. 🔍 Identify leverage points: Focus on the areas with the biggest ROI. 🧰 Select the right tools: Tools should fit the process, not the other way around. 📊 Automate + measure: If you can’t track it, you can’t scale it. 🚀 Iterate & scale: Automation is a process, not a one-time project. This framework is what turns scattered systems into a scalable infrastructure. 👇 I broke it down visually in a carousel. Swipe through, and make sure to save it, download it, and share it with your team or colleagues.

The most expensive thing in your business isn't your software—it's the time you spend on "Grunt Work." 💸⏳ To make this simple, I’ve developed The F.A.B. Framework—a 3-step system to help you stop being an "operator" and start being an "architect." 🏗️✨ Here is how you can use it to stay F.A.B. tonight: 🕵️♀️ F — Find the Repeats As you work today, look for anything you do more than once. If it feels like "copy-pasting" or "sorting," it’s a bottleneck. The Bottleneck: You have a messy list of 50 customer feedback emails that need to be put into a neat table. 📄➡️📊 🧠 A — Ask the AI Ask AI can you automate a task using plain-English AI or zero-code tools?" Keep iterating with the AI until you find the easiest, most straightforward solution. The Ask: "I have a messy list of text. I want you to extract the names, categorize the feedback as 'Happy' or 'Unhappy,' and give it to me in a clean table I can paste into Excel." 🤖💬 🛠️ B — Build the System This is the "Vibe Coding" part. Once you have the blueprint, ask the chatbot to write the actual code files for you. You don’t need to know how the background coding works—you just need to check if the end result matches your vibe! The Build: "This is perfect. Now, write a simple set of instructions I can use every time I have a list like this so I never have to think about it again." ✅✨ #VibeCoding #TheFABFramework #ProductivityHacks

🧵 If you’re doing bioinformatics manually, you’re wasting time and prone to make errors. 1/ Bioinformatics is full of repetitive tasks. The best bioinformaticians don’t just analyze data—they automate. Let’s break it down. 👇 2/ There are 4 levels of bioinformatics skills, from manual work to full automation. The higher you go, the faster and more efficient you become. 3/ Level 1: Manual Execution • Running each command by hand. • Copy-pasting file names, tweaking scripts line by line. • Slow, error-prone, and impossible to scale. 4/ Level 2: Automating with Bash • Using loops and scripts to process multiple files at once. • Combining awk, sed, cut for fast text manipulation. • Example: Run bwa to align multiple samples in a for loop 5/ Level 3: Automating with R/Python • Writing reusable scripts for data processing and visualization. • Using pandas, tidyverse, Bioconductor to automate analysis. • More flexibility than Bash, especially for statistical tasks. 6/ Level 4: Automating with Workflow Languages (Snakemake, Nextflow) • Scalable, reproducible pipelines for large datasets. • Handles dependencies, parallelization, and cloud computing. Example: Processing 100s of RNA-seq samples without modifying a single script. 7/ Why does this matter? • The best documentation is automation—reduce mistakes and save time. • Computers are good at repetitive tasks. Automate once, and never waste time again. 8/ Key Takeaways: • Manual work doesn’t scale. Automate early. • Bash is great for quick scripts, R/Python for analysis, workflow languages for full automation. • The more you automate, the more valuable you become. 9/ Action Items: • Find a repetitive task in your workflow—start automating it. • Learn Snakemake or Nextflow for scalable pipelines. See how I scaled bioinformatics analysis in this real example: 🔗https://lnkd.in/eaPhWxbt I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter https://lnkd.in/erw83Svn

Two patterns that come up constantly in engineering: work nobody wants to do, and work nobody should have to do. Our engineering team has been on the case, articulated in two new Engineering blogs: -- Ahmed Jolani and Abhishek C. tackled the first. They developed an autonomous coding tool to handle well-defined, repetitive tasks. Engineers describe the change in plain English, it writes the code and opens the PR. It now accounts for over 7% of PRs across our repos, and at some point the team started using it to maintain itself. -- Nitin Rai and Chen Su tackled the second. Manual field mapping was the top source of errors in Maestro workflows, and one of the most dreaded parts of setup. So they built a two-stage pipeline that reads document structure visually to infer what each field actually is, then auto-maps everything in about 20 seconds. That used to take 30 minutes. Good problems and smart solutions by the people who solved them. Both posts are in the comments. Check them out, and would love your feedback.

When it comes to operating, executing, and managing... BORING BEATS MOONSHOTS. Boring AI beats moonshots. Small, dull improvements compound into big, visible results. Minutes saved at the task level roll up into cleaner queues, fewer defects, faster decisions, and wider margins. This carousel is your field guide: 🧠 What “boring” really means ↳ Apply AI to the mundane, not the flashy. ↳ Standardize messy inputs, then structure outputs. ↳ Measure time saved, first-time-right, escaped defects. ↳ Treat reclaimed time as capacity, not cuts. 💡 Why it compounds ↳ Task level: fewer handoffs and shorter queues. ↳ Team level: fewer defects, managers coach systems. ↳ Org level: cleaner signals, faster decisions, fewer meetings. 1️⃣ The Five Patterns That Pay: ↳ Documentation and admin, convert chaos into templates. ↳ Decision support, retrieval and first-pass analysis. ↳ Quality control and compliance, stop errors at the gate. ↳ Pattern recognition, surface anomalies and recurring issues. ↳ Creative augmentation, more ideas with better polish. 2️⃣ How value scales up the ladder ↳ Individual contributors reclaim minutes through formatting, naming, routing fixes. ↳ Managers gain coaching time through queue compression and error prevention. ↳ Executives earn more strategy time through clearer signals and context. 3️⃣ Build the base or the top wobbles ↳ Start with documentation and templates. ↳ Add micro-automations at handoffs. ↳ Layer guardrails, then decision support. ↳ Close the loop with learning and updated SOPs. 🧠 12-month operator’s sequence ↳ Months 1–3: inventory five always-on processes, standardize inputs and naming, instrument time and queues. ↳ Months 4–6: track rework and escaped defects, add targeted micro-automations, publish a weekly “hours returned” scoreboard. ↳ Months 7–12: pilot retrieval and decision aids where variance is high, formalize exception paths and owners, redesign roles and incentives to fit new cycle times. 💡 What breaks most pilots ↳ Demos detached from workflows. ↳ Messy inputs and no standardization. ↳ No “hours returned” metric. ↳ Replacing humans instead of augmenting judgment. If you run operations, this is not about a model. It is about management. Document first, measure second, automate third. The compounding shows up as shorter cash cycles, cleaner audits, and margin expansion, not press releases. Start Boring. Start Here: ✅ Pick three high-volume workflows, instrument time, defects, and reruns. ✅ Standardize inputs, ship templates and naming, then add one micro-automation per handoff. ✅ Publish a weekly “hours returned” scoreboard 📌 If you'd like to delve deeper, be sure to check out my new article of the same title over on Substack (link in the comments) ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).