loop-build-path

A four-stage path for turning a manual AI task into a repeatable automated loop. It moves from doing the task by hand, to saving the instructions as a skill, to adding a trigger, and finally to adding verification and stored state.

In plain words
What is it for?
Use it when you have decided to automate a recurring task but need a starting point, a reusable instruction file, a schedule or event trigger, or checks and state tracking.
Why use it?
It helps teams automate gradually and confirm that the task works before adding schedules or events. It also explains why automation becomes a true loop only when it can verify results and remember prior state.

Skill for Claude CodeCodex

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 skills/kangarooking/loop-engineering-skill/loop-build-path
Any agent
npx skills add kangarooking/loop-engineering-skill --skill loop-build-path
Clone the repo
git clone --depth 1 https://github.com/kangarooking/loop-engineering-skill

Made for: Claude Code, Codex.

Per session 103 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,491 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00103 $0.01491
Opus 5 $0.00051 $0.00745
Sonnet 5 $0.00021 $0.00298
Haiku 4.5 $0.00010 $0.00149

Measured 2d ago against content hash 56bac4cd9e09, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loop-build-path 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.

loop-build-path/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Source Metadata

Original cangjie-skill frontmatter from the distillation run:

name: loop-build-path
description: |
  从"手动做事"到"系统自动做事"的四步渐进构建路径。
  当用户已经决定要做 loop、但不知道从何入手时;或团队正在将手工流程自动化时使用。
  不适用于: 尚未决定要不要做 loop 的任务、或已经稳定运行的 loop 优化。
  关键 trigger: "怎么开始做 loop"、"自动化第一步"、"从手动到自动"。
source_book: "Loop Engineering 视频合集"
source_chapter: 视频4 (Idoos Money) / 视频2 (Boris Cherny)
tags: [build-path, incremental, automation, getting-started]
related_skills: [loop-worthiness-test, loop-three-elements, goal-verification]

渐进式 Loop 构建路径 — 从手动到自动的四步

R — Reading (原文)

"The very first thing... do it manually, right? So, confirm the AI can even do the task at all by hand. Next, everyone is turn it into a skill... Next, everyone is the trigger. Now, it runs on a schedule or an event. It is still not a loop yet. It is just an automation. The loop starts, everyone, when you are adding the verification plus state." — Idoos Money (视频4)

I — Interpretation (自述)

构建循环系统不要一步到位,而是按四个阶段渐进:

  1. 手动验证: 先手动让 AI 完成任务,确认它能做到。不要在你都没验证过的任务上搭建自动化。
  2. 封装成 Skill: 把指令固化为 SKILL.md,让每次执行一致。此时是"可重复的手动"。
  3. 加触发器: 加入定时 (cron) 或事件触发。此时是"自动化",但还不是 loop — 因为没有验证和迭代。
  4. 加验证 + 状态: 加入自动验证环节和持久化状态记录。此时才成为真正的 Loop

关键洞察: 自动化 ≠ Loop。自动化是"按时间表执行",Loop 是"执行→验证→调整→再执行"的闭环。

A1 — Past Application (书中案例)

案例1: 选题收件箱 (视频3)

  • Step 1: 手动用 research 工具拉取资讯 → 确认可行
  • Step 2: 把指令写成 skill (research + topic-score)
  • Step 3: 加 cron 触发 (每天早上 8 点)
  • Step 4: 加 inbox.md 作为持久化状态 + 评级作为验证

案例2: Boris 的多 Loop 系统 (视频2)

  • 先手动跑 support 确认 AI 能处理 → 封装成 skill → 加 30 分钟触发 → 加 signals 文件夹作为状态

A2 — Future Trigger (未来触发)

  1. 开始做第一个 loop 时: "我想自动化 X,从哪里开始?"
  2. Loop 搭建失败时: "做了个 loop 但产出很差" → 检查是否跳过了 Step 1
  3. 团队推广 loop 时: 用这个路径作为"入门指南"
  4. 从自动化升级到 loop 时: "我的 cron job 已经跑很久了,怎么升级成 loop?"

语言信号: "怎么开始做 loop"、"自动化第一步"、"从手动到自动"、"我的 cron 怎么升级"

与相邻 skill 的区别:

  • loop-worthiness-test: 判断要不要做 (本 skill 是决定后如何做)
  • loop-three-elements: 静态结构分析 (本 skill 是动态构建过程)
  • goal-verification: 关注验证设计 (本 skill 关注整体路径)

Read the full file on GitHub · 107 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 107 lines · 103 tokens per session scan A 56bac4cd9e09

Subscribe to this mod's changes

loop-build-path is a skill published in the GitHub repository kangarooking/loop-engineering-skill (23 stars, last pushed 2mo ago), licensed MIT. It adds 103 tokens to every session and 1,491 once invoked, about $0.0005 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.

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