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.
Two AGENTS.md templates drawn from Ischca/awesome-agents-md (CC0): "minimal" is a five-line checklist you can run with right away, and "advanced" covers five sections — environment, testing, conventions, safety, and example tasks. They let an AI coding agent know the rules the moment it enters your project.
A ready-to-paste ~/.codex/config.toml template that sets up OpenAI Codex CLI's approval policy, sandbox isolation, reasoning effort, and multiple profiles in one go — balancing automation efficiency with safety boundaries.
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.
A breakdown of the AGENTS.md actually in use in OpenAI's official codex repository — see how a project with millions of lines of code writes team conventions like formatting, testing, commits, module size limits, and API naming into an instruction file that an AI agent can understand and follow, plus a Traditional Chinese adaptation template you can apply directly to your own project.
The AGENTS.md file from the official agents.md standard project itself, demonstrating what a 'minimal but complete' agent rules file looks like — telling AI to use the dev server instead of building during development, restart after dependency changes, and which commands run lint/test/build — with a directly reusable template.
An OpenAI Cookbook Codex example that turns 'fixing bugs or outdated examples' into a self-iterating closed loop: first review and list the problems, then make a targeted fix on a copy, then run validation and score it, and if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.
Uses two Markdown templates (create-prd.md and generate-tasks.md) to turn 'dumping a pile of requirements on AI' into a controlled process: first generate a PRD, break it into a checkable task list, then complete one subtask at a time — pausing after each for your go-ahead before continuing.
Uses VoltAgent/awesome-codex-subagents (166+ subagents, 13 categories, MIT license) as a Codex-equivalent curated directory, teaching you to define subagents in .toml format under ~/.codex/agents/ and invoke Codex subagents explicitly.
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.
Have the agent interview you about your project, then produce a structured, no-fluff AGENTS.md that spells out build/test commands, code style, commit conventions, and off-limits areas in one shot — auto-loaded every time work starts.
Turns a vague requirement into a full Codex workflow: first force it to nail down a 'definition of done,' use plan mode to produce a plan for your approval, then implement in small steps, run its own tests, and finally produce a reviewable diff with verification evidence.
Once your code changes are done, have Codex read the staged diff, produce a Conventional Commits message, and write a PR description covering background, what changed, why, how it was verified, and risks — without bundling in unrelated changes, exaggerating, or fabricating tests.
Has Codex do a disciplined code review of your git diff across three tracks (correctness bugs / simplification-and-reuse / security), listing each finding with a confidence level and a pastable fix, and explicitly instructed to say so when nothing's wrong instead of manufacturing complaints.
Forces Codex through a closed loop — write a failing test that reproduces the bug, find the root cause, make the minimal fix, watch that same test go from red to green, then run the full test suite to confirm no regressions — eliminating both 'fixed it without verifying' and 'changed a bunch of unrelated stuff along the way.'
Has Codex write characterization tests for untested legacy code: it first reads and documents what the code 'actually does right now' (bugs included), systematically covers the happy path plus edge cases and error paths, and is strictly forbidden from modifying the code under test.
Has the agent automatically inventory your Cursor project (.cursorrules, .cursor/rules, .cursor/mcp.json), produce a step-by-step migration plan, and generate the corresponding CLAUDE.md or AGENTS.md, slash commands, and MCP config — so you're not stuck 'dumber than Cursor' on day one.
A Definition of Done template you can paste into any coding agent, turning 'what counts as done' into a checklist the agent must self-verify item by item: behavior meets acceptance criteria, tests/lint/typecheck/build all pass (or it states why they didn't run), risky changes come with a rollback plan, and a final 'verification story' is delivered. Puts an end to agents claiming 'done' while everything is actually broken.
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.'