init

A guided project-initialization skill that interviews the user about a project and fills its AGENTS.md and documentation placeholders. AGENTS.md is a handbook that tells coding agents how to work in a repository.

In plain words
What is it for?
It is for initializing a repository template, defining its domain and scope, recording project-specific constraints, and checking that the resulting guidance is complete.
Why use it?
It replaces incomplete project assumptions with documented decisions about the project's purpose, technology, boundaries, risks, and rules.

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/yangfan-code-star/context-dev/init
Any agent
npx skills add YangFan-Code-Star/context-dev --skill init
Clone the repo
git clone --depth 1 https://github.com/YangFan-Code-Star/context-dev

Made for: Claude Code, Codex.

Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,066 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 $0.00112 $0.05066
Opus 5 $0.00056 $0.02533
Sonnet 5 $0.00022 $0.01013
Haiku 4.5 $0.00011 $0.00507

Measured yesterday against content hash 27f94f2abefb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

init 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 yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (scaffold.mjs, templates/scripts/audit-context.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/skills/init/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

初始化项目

模板本身是空的。这个技能的任务是通过提问把它变成一份准确的项目手册——问完之后,一个半年后第一次进来的 agent 应该只读 AGENTS.md 就能正确干活。

产出质量取决于问题质量。宁可多问两轮,也不要靠猜填占位符。

访谈原则

这几条决定了访谈是好用还是折磨人:

  1. 一次问 3~5 个,不要一次抛 20 个。 问完一组、得到回答、落盘,再问下一组。
  2. 每个问题都给推荐答案。 让用户能直接说"就用默认"。开放式问题("你想怎么设计?")会让用户卡住,带选项的问题("A 还是 B,我建议 A 因为……")不会。
  3. 不问能自己看出来的。 见阶段 0。问用户 package.json 里写着的东西会立刻消耗掉他的耐心。
  4. 用户说"不知道 / 你决定"时,不要卡住。 给出你的选择 + 一句理由,标注为「agent 代填,待确认」,继续往下走。
  5. 答案模糊就追问一次。 "要好用"、"性能要好"这种答案不可执行,追问:"能给个具体场景吗?比如什么情况下你会觉得它不好用?"
  6. 每阶段结束立刻落盘。 用户可能随时中断,写进文件的才算数。
  7. 只记录模型猜不到的东西。 用户的口径选择、历史包袱、被否决的方案、项目特有的陷阱。不要把"要写测试"、"注意错误处理"这类模型本来就知道的内容写进产出。
  8. 用推导式提问,少用清单式。 能从上一个答案推出来的问题,就说明推导过程再问——"你说这是单用户本地工具,那我推断没有登录鉴权;下一个问题:数据存哪?因为这决定本地文件还是嵌入式数据库。"清单式提问(把题库念一遍)让用户觉得被审问,推导式让他觉得被理解,也让误解在早期暴露。

流程

- [ ] 阶段 0:生成骨架 + 勘察现状(不问用户)
- [ ] 阶段 1:项目定位(6 个必答)
- [ ] 阶段 2:领域深挖(按形态分支)
- [ ] 阶段 3:红线与口径
- [ ] 阶段 4:范围边界
- [ ] 阶段 5:落盘、体检、复述确认

每个阶段结束跑一次收敛自检——只要下面四条都能回答,就不必按部就班走完所有阶段,直接进阶段 5 落盘:

  • 能一句话说出项目做什么、给谁用
  • 能说出可验证的成功标准
  • 能说出第一个任务的完整执行上下文(入口、数据、红线)
  • 阶段 3 的红线与口径六问都已问过(用户答"没有"也算问过;没问过就不可能"没有未决问题")

四条不全通过,就继续下一阶段。问题数量由内容决定,不由流程决定——问够了就停,没问够就继续。

轻量档:先问 3 个就开工

完整流程是给"要长期维护、别人也会用"的项目准备的。周末小工具、一次性脚本、还不确定做不做得成的试验,第一次接触就被问三十道题只会让人放弃。这种情况先走轻量档,在阶段 0 之后直接问这 3 个

  1. 一句话说清做什么、谁用(阶段 1 问题 1)
  2. 做到什么程度算成功(阶段 1 问题 2)
  3. 从零到能跑起来、以及验证改动对不对,分别跑什么命令(阶段 1 问题 5)

轻量档只保留和具体功能实现相关的问题,不额外问敏感数据与红线,也不建议换完整版。

落盘时和完整流程一样删掉 AGENTS.md 顶部的「这个仓库还没初始化」引用块,但保留同一个 TODO(init) 欠账标记:没问到的占位符改写成 TODO(init): 轻量初始化未覆盖——/ship-change 首次碰到时现场追问再补。体检脚本会把 TODO(init) 报为 warning(不是"未初始化"的硬错误),其中轻量欠账单独计数,和「完整初始化没做完」区分开:完整初始化的完成线仍是 TODO(init) 归零,轻量档则是有意保留欠账。之后每次 /ship-change 碰到与本次改动相关的 TODO(init),当场问一句补上;与本次改动无关的欠账不要顺手全补。

阶段 0:生成骨架 + 勘察现状

先生成骨架,再看现状。 本技能自包含一套项目骨架模板(templates/,与 SKILL.md 同目录)。先确认 Node 可用(脚手架和体检脚本都是零依赖 Node 脚本):

node --version

先确认技能挂载,再生成骨架。 scaffold.mjs 只生成 12 个骨架文件,不安装技能;项目根没有 .agents/skills/ 时,初始化完成后 /ship-change/maintain-context 不会被宿主发现。先用 dry-run 拿到项目根并预览:

Read the full file on GitHub · 199 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. yesterday First seen · 199 lines · 112 tokens per session scan A 27f94f2abefb

Subscribe to this mod's changes

init is a skill published in the GitHub repository YangFan-Code-Star/context-dev (2 stars, last pushed 14d ago), licensed MIT. It adds 112 tokens to every session and 5,066 once invoked, about $0.0006 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 2026-08-31.

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