Augment Code proposes loop engineering: designing agent loops that run from trigger to execution to validation to outcome, with agents handling the intermediate steps and humans stepping in only at checkpoints that require judgment. The article compares loop engineering with prompt engineering and context engineering as distinct layers, lays out five stages—trigger, execution, validation, outcome, and improvement—and describes four team-level loops already running in production: code review, ticket-to-PR, vulnerability remediation, and incident response.
Why it matters: Augment Code breaks loop engineering into five stages—trigger, execution, validation, outcome, and improvement—and lays out four team-level loop patterns already running in production.
Addy Osmani proposes that a software factory has three layers—loop, harness, and factory. The factory isn’t a smarter agent; it’s multiple loops with harnesses feeding into a single review gate, with humans controlling the outer loop.
Why it matters: The author breaks the software factory into three layers—loop, harness, and factory—and points out that validation, not generation, is the real bottleneck.
A QA engineer distilled six months of experience doing web testing on Claude Code into an open-source Skill package called paranoid-qa. At its core is an evidence contract: Pass/Fail can only be based on actual artifacts like screenshots, request bodies, and logs; anything unverified gets marked Not tested; anything blocked by the environment gets marked Blocked; and forms must verify the real submitted payload.
Why it matters: The author codified six months of QA experience into a testing Skill package for Claude Code, along with an evidence contract and failure checklist that can be reused directly.
AI Hero's skills repo ships v1.1, renaming /to-prd to /to-spec, merging /to-plan and /to-issues into /to-tickets, and adding new Skills like /wayfinder, /research, and /prototype.
Why it matters: The author walks through the full Skill flow from grilling to deployment and gives the migration commands for the renames, the merge, and the new /wayfinder—useful for anyone building an AI development workflow.
Why it matters: It lays out a complete workflow for systematically validating Codex Skills with evals, from defining success criteria to deterministic checks and scoring.
In the Codex Cookbook, OpenAI lays out a complete workflow for modernizing a legacy codebase with Codex CLI, using a COBOL portfolio system as the example and moving through five phases built around an ExecPlan design document.
Why it matters: Using a COBOL portfolio system as the example, it offers reusable documents and a validation workflow for modernizing legacy code in phases with Codex CLI.