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.
npx skills add Jinchen-Yang/diansai-skill --skill select-partsgit clone --depth 1 https://github.com/Jinchen-Yang/diansai-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jinchen-yang/diansai-skill/select-parts)<a href="https://agentmods.dev/skills/jinchen-yang/diansai-skill/select-parts"><img src="https://agentmods.dev/badge/skills/jinchen-yang/diansai-skill/select-parts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jinchen-yang/diansai-skill/select-parts"><img src="https://agentmods.dev/badge/skills/jinchen-yang/diansai-skill/select-parts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.00933 |
| Opus 5 | $0.00060 | $0.00466 |
| Sonnet 5 | $0.00024 | $0.00187 |
| Haiku 4.5 | $0.00012 | $0.00093 |
Grade A, and why
select-parts 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 12d 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.
What it actually says
select-parts —— 选材(流水线 ④)
lane: lead · needs: multimodal(解析配件表 PDF → 只 lead/Claude lane 跑) · 门: 人工批 BOM
候选器件来自主办方当年配件表(不是凭空选、也不是固定库存)。从中按需求挑,金奖经验加持。
前置
design/solution.md(需求表)。没有先跑/plan-solution。inputs/partslist/里有配件表(PDF/文本)。没有就提醒投喂,停。
步骤
- 解析配件表:Read
inputs/partslist/文件(PDF 多模态直接读),列出候选器件表:类别 / 型号 / 关键参数 / 数量(主办方给的可用项)。 - 逐子系统选型:对
solution.md子系统清单每一项,从候选表里挑满足需求的件。判据按序:- 满足需求表的硬指标(电压/电流/接口/电平/数量);
- 查 KB
03/04/05的引脚说明 + 选型理由 + 金奖经验(Read),优先 KB 里点名好用、队伍熟悉的; - 满足供电(电机驱动撑得住堵转、稳压撑得住负载——交
power-design复核)。
- 缺口回填:配件表里没有满足需求的,用
python tools/jlc_search.py "<规格>"现查在库件作候选,标注"需额外采购",并记 LCSC/价格/库存。 - 写
design/bom.csv(列见下)。每件标来源(配件表/需采购)与对应子系统、对应 lib/modules 模块块(供 interconnect 用)。 - 人工门:把 BOM 摊给用户批准(尤其"需额外采购"项与关键件额定)。批准后更新
STATUS.md,提示可并行/power-design与/interconnect。
design/bom.csv 列
subsystem,part,model,lcsc,qty,key_spec,source,module_block,note
电机驱动,TB6612FNG,TB6612FNG,C141517,1,VM≤13.5V/1.2A,配件表,tb6612fng,
主控,MSPM0G3507,MSPM0G3507,,1,M0+80MHz,配件表,mspm0g3507-min,
稳压(逻辑),AMS1117-3.3,AMS1117-3.3,C347222,1,3.3V/1A,需采购,,jlc查得
...
(module_block 对应 lib/modules/<name>.yaml,没有对应块就留空,interconnect 时按需补。)
注意
- 绝不相信凭记忆的型号/参数:关键件额定(Vmax/Imax/封装/引脚)以配件表/数据手册为准;拿不准的标"待核手册"(读手册=多模态=lead)。
- 电源轨能不能撑由
power-design用power_budget.py复核,这里先保证选到候选。 - 候选器件优先复用
lib/modules/已有的 known-good 接法块。
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.
- 12d ago First seen · 40 lines · 119 tokens per session scan A ba1d2fd235c7
select-parts is a skill published in the GitHub repository Jinchen-Yang/diansai-skill (9 stars, last pushed 1mo ago), licensed MIT. It adds 119 tokens to every session and 933 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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