Formerly "Ops with Skill", now officially upgraded. Fully dedicated to AI, we share both the why (fundamental insights) and the how (practical implementation). From technical operations to breakthrough thinking, we help you understand AI's transformation and master the core abilities needed to shape the future. ShugeX: boundless exploration, skillful execution.
The article analyses OpenSpec’s default core workflow, explains why the five‑command core profile can feel limiting, and walks through a four‑step process to unlock six additional expanded commands, then compares three workflow modes that suit different development rhythms.
The article analyzes Karpathy’s three common LLM coding pitfalls, presents four concrete guidelines—Think Before Coding, Simplicity First, Surgical Changes, and Goal‑Driven Execution—implemented in a 65‑line Markdown file, and shows how to install and validate them across Claude Code and Cursor.
This article documents a step‑by‑step OpenSpec workflow that starts with requirement exploration, generates proposal, design, specs, review and task artifacts, executes them to scaffold a React 19 + TypeScript + Vite + Tailwind project, handles GitHub integration, validates the build, and archives the change while sharing practical lessons and pitfalls.
The article analyzes three rounds of lab experiments that reveal task‑granularity as the key lever for AI‑generated code quality, introduces the 2/8 rule, and details a three‑step configuration (config.yaml, schema fork with upgraded instruction, and review artifact) that raises quality scores to about 80% without modifying OpenSpec source code.
The article walks through three hands‑on labs—bare‑run, adding Rules + Explore + Validate, and customizing the schema with a Review artifact—to experimentally verify five quality‑upgrade directions for OpenSpec, comparing outputs, task granularity, rollback plans, testing coverage, and offering practical recommendations.
This article dissects OpenSpec’s four‑step AI programming workflow, exposing why completing the process often still yields buggy code, and proposes five concrete upgrades—including spec reviews, atomic task checks, runtime verification, TDD practices, and archive gate‑keeping—to close the quality gap.
This article walks through a three‑iteration, AI‑assisted TDD workflow that combines OpenSpec for spec management and Superpowers for disciplined implementation, detailing a six‑step process, concrete code snippets, performance metrics, known limitations, and practical tips for backend developers.
This guide shows how to create a custom OpenSpec schema that injects TDD discipline into an AI‑driven coding workflow using three injection points and a four‑layer overlay, demonstrates that the propose phase works as intended while the apply phase only partially enforces TDD, and provides step‑by‑step setup, code examples, comparisons, troubleshooting and best‑practice recommendations.
This article documents a complete hands‑on experiment linking OpenSpec and Superpowers, showing that while the initial spec proposal works, three critical integration points break—two fail outright and one never triggers—leaving the envisioned seamless, spec‑driven development pipeline unachievable.
This article analyses how OpenSpec’s hard‑coded specification engine and Superpowers’ LLM‑driven execution loop complement each other, presenting a detailed four‑step workflow, concrete code snippets, and a side‑by‑side comparison that shows how the combined approach resolves both definition and execution quality issues in AI‑assisted programming.
