1328 hand-tested AI prompts across image, video, and music — each with full context, variables, and a sample output. Content is authored in Traditional Chinese; the UI here is English.
Uses cc-sdd's --codex-skills install to load reusable skills (SKILL.md) into Codex CLI on demand, and replicates its /kiro-impl autonomous execution loop — a fresh implementer per task, an independent reviewer, and automatic root-cause debugging on failure — so Codex advances steadily through a task list one task at a time.
Uses codex exec's non-interactive execution plus a restricted sandbox to plug Codex CLI into CI / scripted workflows for unattended tasks like lint fixes, batch refactors, and auto-repair, guarding the safety boundary with approval_policy=never paired with a workspace-write sandbox.
An OpenAI Cookbook Codex example that turns 'fixing bugs or outdated examples' into a self-iterating closed loop: first review and list the problems, then make a targeted fix on a copy, then run validation and score it, and if it doesn't pass, feed the remaining issues back in for another round — until everything's green or a cap is hit. Includes three ready-to-use prompt templates.
Upgrades OpenAI Codex CLI from 'vibe coding' into a controllable engineering agent: layered AGENTS.md, approval and sandbox settings in .codex/config.toml, and Starlark command gating, paired with a five-stage Research → Plan → Execute → Review → Ship workflow.
A large-refactor playbook for agents: first do a read-only survey of the 'blast radius' — which files, call sites, and tests are affected — then split the change into small batches, each preserving behavior, each run through its own tests, each its own atomic commit. Puts an end to 'change 40 files at once, then the build breaks and no one knows where to start fixing it.'