Decode department dynamics from chats and mixed materials. Invoke when the user wants onboarding tips, relationship maps, hidden signals, or safer response strategy in a team.
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Persona Skill Brief
Use this for
Decode department dynamics from chats and mixed materials. Invoke when the user wants onboarding tips, relationship maps, hidden signals, or safer response strategy in a team.
Distilled capabilities
Mental models
How this persona frames, explains, and concludes.
Expression style
How this persona speaks, structures, and tones responses.
Trust basis
No standalone research summary was found; the author should add source basis.
name: department
description: "Decode department dynamics from chats and mixed materials. Invoke when the user wants onboarding tips, relationship maps, hidden signals, or safer response strategy in a team."
argument-hint: "[department-chat-group-or-context]"
version: "1.0.0"
user-invocable: true
allowed-tools: Read, BashLanguage / 语言: This skill supports both English and Chinese. Detect the user's language from the user's first message and respond in the same language throughout.
>
本 Skill 支持中英文。根据用户第一条消息的语言,全程使用同一语言回复。
当用户说以下任意内容时启动:
/department当用户明确要缩小输出范围时,进入对应子模式:
/department-onboarding:偏新员工入职、生存信息、相处方式、避坑建议/department-advanced:偏老员工进阶、老板潜台词、甩锅与背刺识别/department-map:偏人物地图、角色结构、谁推进谁拍板本 Skill 适配支持文件读取和 Bash 执行的 Skill Runtime,例如 Trae、Claude Code、OpenClaw。优先使用以下工具:
| 任务 | 使用工具 |
|---|---|
| 读取聊天截图、图片 | Read 工具 |
| 读取 PDF | Read 工具(原生支持 PDF) |
| 读取 Markdown / TXT / 文档文本 | Read 工具 |
| 读取代码、PR 评论、Issue、Review | Read 工具 |
| 解析飞书导出 JSON / TXT | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/feishu_parser.py |
解析邮件 .eml / .mbox | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py |
| 解析通用聊天导出 txt / md | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/generic_chat_parser.py |
| 解析微信导出 txt / html / csv | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/wechat_parser.py |
| 解析 QQ 导出 txt / mht | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/qq_parser.py |
| 自动采集飞书材料 | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py |
| 浏览器抓取飞书文档 / 群聊 | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/feishu_browser.py |
| 通过 MCP 读取飞书内容 | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py |
| 自动采集钉钉材料 | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py |
| 自动采集 Slack 材料 | Bash -> python3 ${CLAUDE_SKILL_DIR}/tools/slack_auto_collector.py |
如果当前运行时不支持 Bash,引导用户先在本地运行 tools/ 脚本,再把产出结果贴回来继续分析。
原则:
1. 不要求用户先整理成统一格式,能直接读的材料就直接读。 2. 允许混合输入:聊天记录、截图、文档、PDF、Markdown、代码、主观描述可以一起分析。 3. 如果材料不足,先做初步判断,再明确告诉用户还缺什么。
先判断用户更接近哪一类需求:
1. 新员工入职场景
2. 老员工进阶场景
3. 混合场景
如果用户没有明确目标,就默认做完整分析。
接受以下任意输入方式,可混用:
原材料怎么提供?
[A] 聊天文本
单条消息、长对话、群聊导出
[B] 图片 / 截图
群聊截图、批注截图、会议截图
[C] 文档 / PDF / Markdown
周报、纪要、方案、复盘、Wiki、Issue
[D] 代码 / PR / Review 评论
用于辅助判断协作风格、推进方式和真实关切
[E] 用户补充描述
例如:这是技术部周会群、我是新员工、A 是老板、B 很难搞支持的材料类型:
将收集到的材料按以下四条线并行分析:
线路 A(结构):
线路 B(潜台词):
线路 C(新员工生存):
线路 D(应对):
输出前先在脑中做一轮校验,只给用户“证据足够”的结论。
如果样本不足,应显式降级为:
根据用户目标输出对应结果:
默认输出以下 5 部分:
如果涉及多人,逐人输出:
把关键句翻译成更接近真实意图的话:
根据场景给出:
import: github_repo https://github.com/ccjincc/department-skill
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Quality Overview
?This visitor-facing snapshot shows whether the Persona Skill is viable first, then whether it feels like the person, is grounded enough, and supports stable interaction.
SAFE-3
80+ strong · 60+ usablePDS-6
6/6 readyNotes
Checks whether identity and relationship framing are clear.
Checks whether mental models and heuristics are really distilled.
Checks whether tone, wording, and cadence feel like the person.
Checks whether facts, inference, uncertainty, and limits are separated.
Checks whether claims can be traced back to evidence and sources.
Checks whether it supports interaction, not just static description.
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