ai-coding-ok AGENTS.md

Agent instructions for ai-coding-ok, a framework that makes coding agents follow a plan, use project memory, and record completed work.

In plain words
What is it for?
They are for setting up and following a plan-do-check-act workflow with project memory, decision records, task history, and coding rules.
Why use it?
They help prevent an agent from repeating past mistakes or fixing one problem while breaking another by requiring it to read and update shared project notes.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/mark7766/ai-coding-ok/agents-md
Clone the repo
git clone --depth 1 https://github.com/Mark7766/ai-coding-ok

Made for: Codex, OpenCode.

Per session 1,310 This file is loaded in full into every session.
When invoked 1,310 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01310 $0.01310
Opus 5 $0.00655 $0.00655
Sonnet 5 $0.00262 $0.00262
Haiku 4.5 $0.00131 $0.00131

Measured 2d ago against content hash e2dbd12cf721, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ai-coding-ok AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — ai-coding-ok

⚠️ AI Agent 必读规范(每次任务必须执行)

本项目使用 ai-coding-ok 三层记忆系统。在执行任何任务之前,必须完成以下步骤:

Plan 阶段(强制,任务开始前)

  1. 读取 AGENTS.md — 本文件,架构速查
  2. 读取 .github/agent/system-prompt.md — Agent 人格、角色切换、行为边界
  3. 读取 .github/agent/workflows.md — 场景工作流(Feature/Bug/Refactor/发布)
  4. 读取 .github/agent/coding-standards.md — 编码规范
  5. 读取 .github/agent/memory/project-memory.md — 项目事实和架构约束
  6. 读取 .github/agent/memory/decisions-log.md — 历史技术决策
  7. 读取 .github/agent/memory/task-history.md — 近期任务上下文

Act 阶段(强制,任务结束后)

  1. 更新 .github/agent/memory/task-history.md — 记录本次任务摘要
  2. 如有架构决策变化 → 更新 .github/agent/memory/decisions-log.md
  3. 如有项目事实变化 → 更新 .github/agent/memory/project-memory.md
  4. 如 AGENTS.md / system-prompt.md / workflows.md / coding-standards.md 有事实性过时内容 → 同步更新对应文件

⛔ 以上步骤不可跳过。若在使用 superpowers brainstorming / writing-plans, 在调用这些 skill 之前先完成 Plan 阶段,结束后完成 Act 阶段。


项目概述

ai-coding-ok 是一个 AI 编程护栏 Skill 框架。为任意项目安装三层记忆系统(project-memory + decisions-log + task-history)并强制执行 PDCA 工作流,确保 AI Agent 在每次编码任务前后读取和更新项目记忆,解决"AI 修了 bug X 却搞坏了功能 Y"的问题。目标用户是所有使用 Claude Code、GitHub Copilot、Cursor 的开发者。

系统架构与数据流

开发者发起编码任务
      │
      ▼
CLAUDE.md (@AGENTS.md) ──→ AGENTS.md (PDCA 强制指令)
      │
      ├── Plan: 读取 7 个文件(AGENTS + 3 agent 规范 + 3 记忆文件)
      ├── Do:   编码 + 测试
      ├── Check: 验证
      └── Act:   更新记忆文件(task-history 始终 + decisions-log/project-memory 按需)

跨平台兼容层:
  Claude Code  → SKILL.md (Mode A/B/C/D) + CLAUDE.md shim
  Copilot      → .github/copilot-instructions.md (自动加载)
  Cursor       → .cursor/rules/ai-coding-ok.mdc
  Codex        → AGENTS.md (自动加载) + .codex/skills/ai-coding-ok/ (skill)
  • SKILL.md — Skill 定义文件,包含 Mode A(Install) / B(Plan) / C(Act) / D(Upgrade) 完整逻辑
  • templates/zh/ — 中文安装模板(AGENTS.md + 三层记忆文件 + agent 规范),安装时自动替换占位符。v4.1.0 起为唯一模板源
  • scripts/ — install.sh / install.py 自动化安装脚本 + verify.sh 安装验证 + customize-prompt.md / upgrade-prompt.md 手动触发提示

Read the full file on GitHub · 88 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 88 lines · 1,310 tokens per session scan A e2dbd12cf721

Subscribe to this mod's changes

ai-coding-ok AGENTS.md is an instructions file published in the GitHub repository Mark7766/ai-coding-ok (15 stars, last pushed 1mo ago), licensed MIT. It adds 1,310 tokens to every session, about $0.0066 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other instructions, from other repositories

spec-kit AGENTS.md

AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.

github/spec-kit · 7,104 tokens

vscode buildNext.instructions.md

Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).

microsoft/vscode · 6,785 tokens

codex AGENTS.md

AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.

openai/codex · 5,182 tokens

langchain AGENTS.md

AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.

langchain-ai/langchain · 4,345 tokens

vscode oss-third-party-notices.instructions.md

Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).

microsoft/vscode · 5,001 tokens

next.js AGENTS.md

Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.

vercel/next.js · 7,296 tokens