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 AgenticAIPlan/AgenticAISkills --skill expert-collection-analysisgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/expert-collection-analysis)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/expert-collection-analysis"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/expert-collection-analysis/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/agenticaiplan/agenticaiskills/expert-collection-analysis"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/expert-collection-analysis.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 | $0.00041 | $0.02774 |
| Opus 5 | $0.00020 | $0.01387 |
| Sonnet 5 | $0.00008 | $0.00555 |
| Haiku 4.5 | $0.00004 | $0.00277 |
Grade A, and why
expert-collection-analysis 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.
How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
智库专家数据采集与分析 Skill
适用场景
本Skill适用于以下业务场景：
主要场景
- 智库研究机构：建立AI数据领域专家知识库，跟踪权威专家最新观点
- 企业战略部门：监控行业专家动态，识别高价值合作对象
- 政府部门：构建专家资源库，支持政策制定和咨询
- 学术会议组织：筛选高价值专家，进行精准邀请
任务特点
- 需要从多个权威信息源采集专家言论（政府、智库、媒体）
- 需要AI智能分析专家与特定领域（如AI数据）的相关度
- 需要对专家进行优先级排序，识别高价值专家
- 需要专业的数据导出格式（Excel双工作表）
- 需要与飞书多维表格等办公系统集成
输入要求
使用本Skill需要准备以下输入：
必需输入
-
采集领域关键词
- 默认：人工智能、数据要素、算力、大模型、数字经济
- 可自定义搜索关键词列表
-
时间范围
- 默认：最近24个月
- 可调整时间窗口
-
飞书配置（如需飞书集成）
- app_id：飞书应用ID
- app_secret：飞书应用密钥
- app_token：多维表格app_token
- table_id：目标数据表ID
可选输入
- 优先级评分权重调整（默认：相关度60% + 权威性40%）
- 单个信息源采集数量限制（默认：20条/源）
- 自定义信息源配置
执行步骤
步骤1：确认采集目标和范围
- 确认要采集的领域关键词
- 确认时间范围（默认24个月）
- 确认信息源配置（15个权威源）
步骤2：自动化数据采集
系统将从以下15个权威信息源采集数据：
政府部门（5个）：
- 国务院常务会议相关信息
- 国家数据局政策动态
- 工业和信息化部行业报告
- 科技部发展战略
- 中央政府AI政策
权威智库（5个）：
- 国务院发展研究中心研究报告
- 中国信息通信研究院分析报告
- 中国社会科学院学术成果
- 国家高端智库专题研究
- 清华大学AI研究进展
权威媒体（5个）：
- 人民网专家观点
- 新华网政策解读
- 央广网产业分析
- 经济日报经济趋势
- 财经网行业动态
每个信息源生成2个搜索URL（网页搜索 + 新闻搜索），共30个真实百度搜索链接。
步骤3：数据清洗与过滤
- 时间过滤：保留时间范围内的数据
- 智能去重：按专家姓名+言论内容去重
- 数据验证：确保必填字段完整
步骤4：AI智能分析
对每条专家言论进行AI分析：
-
相关度评分（0-100分）
- 分析发言内容与目标领域的相关性
- 提取关键主题和观点
-
权威性评分（0-100分）
- 评估专家身份、机构地位
- 分析影响力和学术声誉
-
综合优先级计算
综合分数 = 相关度 × 0.6 + 权威性 × 0.4 高优先级 ⭐⭐⭐：综合分≥70 且 相关度≥60 中优先级 ⭐⭐：综合分≥50 或 相关度≥40 低优先级 ⭐：其他
步骤5：专家信息聚合
- 按专家姓名聚合所有言论
- 补充专家档案信息（单位、职务、简介）
- 统计发言次数和主题分布
- 推荐适合的交流活动
步骤6：生成专业报告
- 生成Excel双工作表文件
- 工作表1：专家档案（专家基本信息、优先级、相关度）
- 工作表2：专家言论（链接、时间、内容、主题、要点）
- UTF-8 BOM编码，完美支持中文
- 可直接导入飞书多维表格
输出要求
主要输出
1. Excel专业报告
文件路径：output/专家分析报告_YYYYMMDD_HHMMSS.xlsx
工作表1：专家档案
| 列名 | 类型 | 说明 |
|---|---|---|
| 专家姓名 | 文本 | 专家完整姓名 |
| 最新单位 | 文本 | 当前工作机构 |
| 最新职务 | 文本 | 当前职务 |
| 简介 | 文本 | 专家背景介绍（≤500字） |
| 发言统计 | 数字 | 采集到的言论总数 |
| 优先级 | 文本 | 高/中/低 |
| AI数据相关度 | 数字 | 0-100分 |
工作表2：专家言论
| 列名 | 类型 | 说明 |
|---|---|---|
| 链接 | 超链接 | 百度搜索URL |
| 时间 | 日期 | 发布日期 |
| 专家姓名 | 文本 | 发言人姓名 |
| 正文 | 文本 | 言论完整内容 |
| 发言主题 | 文本 | 核心话题 |
| 发言要点 | 文本 | 关键观点摘要 |
| 活动背景 | 文本 | 发言场合/事件背景 |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 354 lines · 41 tokens per session scan A 70a8e44eec40
expert-collection-analysis is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 2,774 once invoked, about $0.0002 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 2026-08-30.
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