Component Preparing

Community

Zane456

component-preparing

Phase 2.5 expert — applicability review + shortlist confirmation + asset acquisition + BOM gate. ALWAYS invoke this skill after component-selecting outputs shortlist JSON: deep-review each top pick datasheet against the circuit role (5-dimension check) before user confirms, fetch datasheets, vendor library into lib_external/, write per-MPN evidence JSON, run verify_vendoring.py + check_readiness.py to emit .bom_readiness.json sentinel + procurement BOM CSV (draw-schematic entry gate). Do not download datasheets, write to lib_external/, skip the applicability review, or skip the BOM sentinel gate by hand. Use this skill first. Does not re-evaluate shortlist verdict (component-selecting owns that) and does not draw schematics. Triggers: 落资产 / 把 shortlist 落下来 / 适用性审查 / applicability review / 抓 datasheet / vendor library / Phase 2.5 / BOM 验货 / verify BOM / library readiness / 生成订货 CSV / pre-sch-render check.

Overview

Publisher	Zane456
Repository	`PCB-Agent-Teams`
Skill name	`component-preparing`
Stars	79
Forks	20
Bundled files	16
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
16 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Zane456 on GitHub. Read the source before you install it.

Installation

Install the Component Preparing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Zane456/PCB-Agent-Teams/tree/main/.claude/skills/component-preparing
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/Zane456/PCB-Agent-Teams.git /tmp/PCB-Agent-Teams
mkdir -p .claude/skills
cp -r /tmp/PCB-Agent-Teams/.claude/skills/component-preparing .claude/skills/component-preparing

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Component Preparing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Component Preparing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Component Preparing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

component-preparing — 选品确认 + 资产获取 + BOM gate

核心理念

单一职责：把 component-selecting 已经 verdict=pass 的 MPN 落到磁盘并出 BOM 验收 sentinel，下游 draw-schematic 凭 sentinel 入场。

不重选、不二评：verdict 来自 component-selecting，本 skill 只适用性审查 + 确认 + 抓取 + 验收（⓪ 审查不重跑参数 API，只对 datasheet vs role 做深度核对）
不查库存、不算价格：API 已在 component-selecting 阶段查过
不动 .py 电路结构 / .kicad_sch：那是 draw-schematic 的事（唯一例外见 ⑥——BOM 全过后自动把已验证 MPN/Datasheet 属性补回 .py Component()，纯属性补全、不改结构）

历史职责拆分：原 bom-readiness skill 已合并入本 skill（2026-05）；原因——shortlist 落资产时 BOM 数据本就齐了，没必要等到画完 sch 再回头校。

共享元件库 lib_external/

写入 / 读取 / 命名空间 / 清理 / 冲突解决 → 见 lib_external/CONVENTIONS.md（single source of truth）。

入场判断

用户进来时的状态	进 / 不进	处理
拿着 `_artifacts/component_selecting/<role>_shortlist.json`	✅ 进	跑全工作流 ⓪–⑥
同族换值（MPN 内某段变化、supplier+series+pkg 不变）	✅ 进（family-swap 模式）	跑 `scripts/swap_family_mpn.py --old-mpn X --new-mpn Y --apply`（自动跨文件改 .py / .kicad_sch / .kicad_pcb / .net / lcsc_mapping，stamp shortlist `_v5_revision`，clean orphan PDF，重跑 sentinel）
单 MPN 跨族 swap（不同系列 / pkg / 供应商）	✅ 进（单件模式）	单点抓 datasheet → 必跑 clean_orphan_datasheets.py --apply → 后续步骤
准备 draw-schematic 但 sentinel 不存在 / stale	✅ 进（gate 模式）	跳过抓取，直接跑 ⑥ check_readiness.py 重写 sentinel
拓扑 / 锚点件还没定	❌ 退出 → `circuit-design`	—
shortlist 还没出来	❌ 退出 → `component-selecting-JP`	—
单 MPN 临时查库存 / 比价	❌ 退出 → `component-selecting-JP --mpn`	—

入场必读

Projects/<name>/CLAUDE.md §5 — 锚点件功能 spec，是 ⓪ 适用性审查的 ground truth
USER.md §0 — locale 决定抓取链路（日本 → DK dkjp + UL/SnapEDA fallback；中国 → LCSC，待建）
lib_external/CONVENTIONS.md — vendoring 写入规则
references/vendoring_field_notes.md — Cloudflare / dkjp session / DK DOM 速记

工作流概览（7 步）


读 shortlist JSON
   ↓
对每个 role，从 top-1 起：
   ⓪ 适用性审查（pre-confirm gate，详见下节）
       ├─ pass / pass_with_concerns → 带报告进下一步
       ├─ fail (swap)               → 取 shortlist[+1]，⓪ 重跑
       └─ fail (回 selecting)       → spec 错位严重，user 自跳 component-selecting
   ↓
跟 user 逐 role 确认 top pick（基于 ⓪ 报告拍板）
   ├─ accept → scripts/accept_shortlist.py 把 shortlist → per-MPN evidence
   │            （写到 datasheets/component_selecting/<safe_mpn>.json；
   │             ⓪ 报告由 LLM 补写到 evidence `applicability` 字段）
   └─ reject → 退回 component-selecting 重出 shortlist
   ↓
对每个确认的 MPN：
   ① datasheet 批量抓取（scripts/bulk_fetch_datasheets.py，原 sourcing 迁入）
       → **强制接** clean_orphan_datasheets.py --apply
       （⓪ swap 留下的孤儿 PDF 也在这里被清掉）
   ② library 分类（按 evidence library.status 7 类）
   ③ vendoring（按 USER.md §0 locale 路由）
   ④ evidence JSON 补齐（datasheet.path / library.vendored_*）
   ⑤ verify_vendoring.py 自检（强制收尾，**不允许跳**）
   ↓
全 MPN 落地后：
   ⑥ BOM gate（写 .bom_readiness.json sentinel + 采购 BOM CSV）
       → scripts/check_readiness.py
   ↓
告知用户："资产 + sentinel + 采购 CSV 已就绪，可进 draw-schematic"

⓪ 详见下节；①–⑤ 抓取 / vendoring / locale 路由 → references/vendoring_field_notes.md；⑥ BOM gate 全流程 + fidelity 三检 + sentinel 协议 → references/bom_gate.md。

⓪ 适用性审查（pre-confirm gate）

user 拍 top pick 前，先用 datasheet 把候选跟项目 §5 spec 对齐：per role 从 top-1 抓 datasheet（先不写 evidence）→ LLM 按 5 维度核查（每条带 datasheet 页码 / §5 行号支撑句）→ markdown 报告交 user 三选一（accept / swap_next / back_to_selecting）。拿不到项目 §5 → 不进 ⓪。

进 ⓪ 前读 references/applicability_review.md：gate 条件 + per-role 流程 + 5 维度细表 + verdict 三档 + evidence applicability schema + 风险接受规则 + 反例。

⑥ BOM gate（核心硬门槛）

全 MPN 落地后跑 check_readiness.py：fidelity 四检（A.MPN 一致性 / B.封装类一致性 / C.占位符伪装 / D.pin 数一致性）任一 fail → all_pass=False → draw-schematic 入场被挡。all_pass 后默认调 inject_mpn_props.py 把已验证 MPN/Datasheet/Manufacturer 补回 .py Component()（幂等、只补属性不改结构，--no-inject-mpn 关闭）。

跑 ⑥ 前读 references/bom_gate.md：四检血泪案 + 注入细节 + sentinel 字段/失效条件 + 审查模式 + evidence contract + 命令行用法。

反模式（最高频 3 条；全集 → `references/anti-patterns.md`）

❌ 重评 shortlist verdict / 跳过 user 确认：verdict 来自 component-selecting，top pick 必须 user 拍板
❌ 跳过 ⓪ 适用性审查直接抓全 BOM：spec 错位的料 vendoring 完才发现 → 浪费 quota + 误导 user 拍板；⓪ 报告必须先到 user 桌上
❌ 同族换值用 bare sed / Edit cross-file：必须走 swap_family_mpn.py，否则漏改连带字段 → sentinel 静默放行 → 物理板按错值贴片

其余（支撑句要求 / 手拷 lib / 近似 footprint / 跳 verify / role 裸编号等）展开在 references/anti-patterns.md，动手前对照自检。

边界（不做这些）

讨论拓扑 → circuit-design；longlist + verdict + buyable_gate → component-selecting-JP；临时查库存 / 比价 → component-selecting-JP --mpn；.py → .kicad_sch → draw-schematic； Gerber + 生产 BOM/CPL + 文档 + vendor 决策 + BOM 复核 → release（出货 umbrella）。

references

references/applicability_review.md — ⓪ 5 维度核查清单 + verdict 三档 + evidence applicability schema + 风险接受规则
references/vendoring_field_notes.md — ①–⑤ 实战速记（Cloudflare / dkjp / DK DOM）
references/bom_gate.md — ⑥ check_readiness.py 全流程 + fidelity 四检 + sentinel 协议
references/bom_lifecycle.md — 三类 BOM 文件生命周期（采购 vs 生产 vs CPL）
references/anti-patterns.md — 反模式全集（越界 / vendoring / 命名 / family-swap）

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Component Preparing AI skill do?

Why use Component Preparing on TypingMind?

Because you install it once and use it with any model. Component Preparing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Component Preparing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Zane456/PCB-Agent-Teams/tree/main/.claude/skills/component-preparing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Component Preparing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Component Preparing?

As many as you like. As long as a model supports skills, you can use Component Preparing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Component Preparing AI skill free?

It is published on GitHub by Zane456. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

component-preparing — 选品确认 + 资产获取 + BOM gate

核心理念

共享元件库 lib_external/

入场判断

入场必读

工作流概览（7 步）

⓪ 适用性审查（pre-confirm gate）

⑥ BOM gate（核心硬门槛）

反模式（最高频 3 条；全集 → references/anti-patterns.md）

边界（不做这些）

references

Bundled files

Frequently asked questions

Related AI Skills

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反模式（最高频 3 条；全集 → `references/anti-patterns.md`）