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 boardgit 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/board)<a href="https://agentmods.dev/skills/jinchen-yang/diansai-skill/board"><img src="https://agentmods.dev/badge/skills/jinchen-yang/diansai-skill/board/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/board"><img src="https://agentmods.dev/badge/skills/jinchen-yang/diansai-skill/board.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 | $0.00080 | $0.00731 |
| Opus 5 | $0.00040 | $0.00365 |
| Sonnet 5 | $0.00016 | $0.00146 |
| Haiku 4.5 | $0.00008 | $0.00073 |
Grade A, and why
board 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 10d 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
board —— 任务板与自动交接
lane: 全部（每个 lane 会话各跑各的） · 引擎: tools/board.py（确定性，任何模型可用）
三个 lane 不能直接通信，只能靠 git 仓库 + board/ 任务文件协同。本 skill 让你：拉取 → 看派给自己的就绪任务 → 干 → 标完成（自动给下游 lane 派活）→ 推送。
先确定本机 lane
读仓库根的 .elec-lane（未跟踪文件）。若不存在，问用户本机是哪个 lane（lead/硬件/控制/算法），写入 .elec-lane（一行，如 控制）。后续都用它。
每次被调用的动作
git pull --rebase（取别人提交的进度与新派的任务）。python tools/board.py list --lane <本机lane>—— 列出派给我、依赖已全 done 的就绪任务。- 若无就绪任务：
python tools/board.py status给全局，告诉用户在等谁（阻塞依赖），结束。 - 若有：
skill类任务 → 调它的 hint 指向的 skill（如/interconnect），按那条流水线干完。manual类任务（连线/整定）→ 把 hint 指的清单/产物摊给用户去做。
- 干完一个：
python tools/board.py done <id> --by <本机lane>。这会自动按 DAG 给下游 lane 派生任务（写新 board/ 文件）。 git add board/ <你的产物目录> && git commit -m "board: done <id>" && git push。- 告诉用户：完成了什么、自动派给了哪些 lane 什么任务、本 lane 还有无下一个就绪任务。
近实时（可选）
让本 lane 会话挂轮询，自动看新派来的任务：
- Claude 原生：
/loop 5m /board（每 5 分钟 pull+看板）。 - 纯终端：
sh tools/watch.sh <本机lane> 300。
注意
- 只动自己 lane 的产物目录 + board/（目录归属见 CLAUDE.md），避免 git 冲突。
- 任务板是协作式的：它让交接显式、可审计；但跨机器是 pull 驱动，别人要
git pull才看得到你派的活（故有轮询）。 - 流水线 DAG 固定在
tools/board.py（read-problem→…→整车联调）；要改交接关系改那里（lead 维护）。
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.
- 10d ago First seen · 35 lines · 80 tokens per session scan A de6cc8e048c8
board is a skill published in the GitHub repository Jinchen-Yang/diansai-skill (9 stars, last pushed 1mo ago), licensed MIT. It adds 80 tokens to every session and 731 once invoked, about $0.0004 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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