# ai-skills Published articles for ai-skills. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week DevFeed: [How 8 AI Skills Automate a Content Workflow and Save 5+ Hours per Week]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-16T14:36:57Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [Artificial Intelligence]() Tags: [ai](), [ai-skills](), [automate](), [skills](), [workflow]() ### AI overview The article describes a three-step system that turns 15 manual content-workflow steps into 8 AI skills, reportedly saving more than 5 hours per week. ### Source excerpt How I turned 15 manual steps into 8 AI skills that save me 5+ hours a week, and how you can do the same for your own work ## Designing a custom AI agent for repetitive QA workflows DevFeed: [Designing a custom AI agent for repetitive QA workflows]() Original publisher: [Read original article]() Author: Rimple Sharma Published: 2026-05-18T06:01:01Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Agent](), [Markdown](), [Parser](), [context]() Tags: [ai](), [ai-agent](), [ai-skills](), [context](), [markdown](), [parsing](), [software-testing](), [test-automation]() ### AI overview This tutorial explains how to design a custom AI agent for repetitive QA workflows. It focuses on encoding structured instructions in Markdown, including headers, rules, variables, examples, output schemas, and executable commands, and introduces the agent's processing loop. ### Source excerpt Part 2: Markdown as agent logic and the ReAct loop in action. In Part 1, I walked through the problem, repetitive QA work that resists pure scripting and the three pillars of a useful agent: input validation, context awareness, and a decision engine. I also showed why structured instructions beat plain text. This part is about how that structure gets encoded, how the loop runs, and what the impact looked like in practice. Why agents use Markdown When writing instructions for an agent, Markdown isn't just a formatting choice, it acts as a bridge between human-readable text and machine-processable logic. It's become a common format for agent communication because both humans and machines can work with it efficiently. But what most people miss... When you write agent instructions in Markdown, you're creating a weighted logic map, as well as just writing text. A few patterns that matter: 1. Semantic headers (#, ##) act as anchors for the agent's attention. Wrapping your mandatory rules under a clear header makes it far less likely the agent will overlook them mid-task. 2. Horizontal rules ( -- -) separate identity from instructions. Without this boundary, rules from one section bleed into another and the agent loses track of what governs what. 3. Bold keywords (**NEVER**, **ALWAYS**, **MANDATORY**) tend to be treated as hard constraints. Bold is a hard constraint. Italics are treated as a soft suggestion. 4. Blockquotes (>) are useful for examples. Wrapping an example in a blockquote signals to the agent that this is a reference, not a direct command. 5. {{curly_braces}} mark dynamic variables. They tell the agent that this part of the input will be filled in at runtime -- things like {{TICKET_ID}} or {{DATASET_NAME}} -- so it never substitutes a generic placeholder into a production file. 6. Fenced code blocks define output schemas. If you want the agent to always respond in a specific format, put that format in a code block. It treats it as a contract. 7. Inline code signa ## Designing a custom AI agent for repetitive QA workflows DevFeed: [Designing a custom AI agent for repetitive QA workflows]() Original publisher: [Read original article]() Author: Rimple Sharma Published: 2026-05-13T09:34:47Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Agent](), [Automation](), [test](), [test data](), [configuration](), [pull-requests](), [consistency]() Tags: [ai-agent](), [ai-skills](), [automation](), [configuration](), [consistency](), [pull-requests](), [qa](), [software-testing](), [test-automation](), [test-data]() ### AI overview This article defines a repetitive QA workflow involving configuration updates, test-data additions, mapping checks, coverage validation, and pull requests. It argues that the main bottleneck is contextual decision-making and cross-file validation, which motivates designing a custom AI agent. ### Source excerpt Part 1: Core problem definition, three pillars of an effective agent & why structured instructions matter.Source: AI generated image Engineers on QA teams spend a disproportionate share of their time on work that follows a consistent pattern: updating configurations, modifying test artifacts, executing focused validations, and managing pull requests. The tasks are well-defined but the repetition adds up fast. Individually, none of this is complicated. But taken together, these tasks are: Repetitive Error-prone Hard to repeat reliably During my time in automation, I kept running into the same problem. Every release brought the same requirements. New datasets had to be added so the test suite could cover the scenarios tied to them. It was never a one-time effort. The same work had to be repeated across different combinations, every single time. The process typically looked like this: 1. Updating multiple configuration files. 2. Adding the corresponding test data. 3. Making sure everything was mapped correctly. 4. Validating coverage and consistency. 5. Committing the changes and raising a PR. Each step on its own was straightforward. But strung together and repeated across releases, it consumed a meaningful chunk of time that could have gone elsewhere. Problem breakdownFig 2.0 Pain of manual repetitive process (Source: AI generated image) Each step is straightforward. But together, context switching between files, making manual edits, double checking mappings, it added up to 30 to 60 minutes per release cycle in our case. Miss one mapping, introduce a typo, and the build breaks. It wasn't hard work, just repetitive. And that's exactly the kind of work that makes test suites brittle over time. Even with automation in place, every iteration still required someone to identify what had changed, figure out which files were affected, and make sure everything stayed consistent across the board. The framework we had was solid. Scripts handled the heavy lifting well. But there