loop-worthiness-test

A decision checklist for deciding whether a task is worth running as a repeating automated process. It checks how often the task happens, whether results can be checked automatically, whether the usage cost is acceptable, and whether the agent has the tools it needs.

In plain words
What is it for?
Use it to judge whether to automate a task, start a repeating agent process, or understand why an existing process is not worthwhile.
Why use it?
It helps avoid building automation for one-off or infrequent tasks that cost more time and resources than they save.

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

Made for: Claude Code, Codex.

Per session 107 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,558 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 $0.00107 $0.01558
Opus 5 $0.00053 $0.00779
Sonnet 5 $0.00021 $0.00312
Haiku 4.5 $0.00011 $0.00156

Measured 2d ago against content hash 3f681be9cc30, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loop-worthiness-test 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-worthiness-test/SKILL.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 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-worthiness-test
description: |
  判断一个任务是否值得做成 Loop 的四条决策标准。
  当用户在纠结"这件事要不要自动化"、"该不该用 loop"、或"为什么我的 loop 得不偿失"时使用。
  不适用于: 已经决定要做 loop 后的设计阶段、或一次性任务。
  关键 trigger: "这件事值得做 loop 吗"、"该不该自动化"、"loop 成本太高怎么办"。
source_book: "Loop Engineering 视频合集"
source_chapter: 视频3 (小木头引用 Iddo Money) / 视频4
tags: [decision-framework, cost-benefit, automation, checklist]
related_skills: [loop-three-elements, loop-build-path, comprehension-gap]

Loop 适用性四条件测试 — 防止过度工程化

R — Reading (原文)

"他给了4条测试条件,4条都满足做loop才划算。第一是这个活每周以上都会重复... 第二验证能够自动化... 第三你的token预算得扛得住... 第四agent手里有资深工程师那套工具。" — 小木头 (视频3)

"The majority of tasks don't need loops." — Adam Gillock (视频1)

I — Interpretation (自述)

判断一个任务是否值得搭建循环系统,需要同时满足四个条件:

  1. 高频重复: 任务至少每周做一次。一次性或低频任务不值得搭建 loop 系统。
  2. 可自动验证: 有测试/Lint/检查能自动拦截坏结果,无需人工审验每条输出。
  3. Token 预算充足: 能承受反复读取上下文和试错的成本,包括浪费的 token。
  4. 完整工具链: Agent 拥有日志、运行环境、自测能力,能自己跑代码看结果。

四条都满足才值得做 loop。 这是一个反直觉的过滤器 — 大多数人看到 loop 很酷就想用,不会先做适用性判断。

A1 — Past Application (书中案例)

案例1: 值得做 Loop — 选题收件箱 (视频3)

  • 高频: 每天 ✅ | 可验证: 有 topic-score 评级 ✅ | 预算: 小 ✅ | 工具: 有 research API ✅
  • 结论: 值得

案例2: 不值得做 Loop — 一次性脚本 (视频1 隐含)

  • 高频: 一次性 ❌ | 可验证: N/A | 预算: N/A | 工具: N/A
  • 结论: 不值得,单次提示即可

案例3: 部分满足 — 缩略图生成 (视频1)

  • 高频: 每周 ✅ | 可验证: 主观评分 ❌ | 预算: 中 ✅ | 工具: 有 ✅
  • 结论: 验证环节是瓶颈,需要引入独立评分 agent

A2 — Future Trigger (未来触发)

  1. 纠结是否自动化时: "我想让 AI 每天做 X,值得做 loop 吗?"
  2. Loop 成本过高时: "这个 loop 跑一次花太多 token" → 检查条件3和4
  3. Loop 产出质量差时: "loop 出来的东西不能用" → 检查条件2 (验证是否可靠)
  4. 团队推广 Loop 时: 用这个测试作为"要不要做"的决策门槛

语言信号: "值得做 loop 吗"、"该不该自动化"、"loop 成本太高"、"这个任务适合 loop 吗"

与相邻 skill 的区别:

  • loop-three-elements: 假设已决定要做,关注"怎么设计" (本 skill 是前置决策)
  • loop-build-path: 关注构建步骤 (本 skill 是构建前的判断)
  • comprehension-gap: 关注 loop 运行后的风险 (本 skill 是运行前的判断)

E — Execution (可执行步骤)

Step 1: 四条件检查清单

对目标任务逐条检查:

□ 高频重复: 至少每周做一次?
□ 可自动验证: 有客观标准判断好坏?
□ Token 预算: 能承受反复试错?
□ 完整工具链: agent 有日志+环境+自测能力?

Read the full file on GitHub · 114 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 · 114 lines · 107 tokens per session scan A 3f681be9cc30

Subscribe to this mod's changes

loop-worthiness-test is a skill published in the GitHub repository kangarooking/loop-engineering-skill (23 stars, last pushed 2mo ago), licensed MIT. It adds 107 tokens to every session and 1,558 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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