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 baidu-ecosystem-ai-product-reviewgit 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/baidu-ecosystem-ai-product-review)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/baidu-ecosystem-ai-product-review"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/baidu-ecosystem-ai-product-review/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/baidu-ecosystem-ai-product-review"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/baidu-ecosystem-ai-product-review.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 | $0.00085 | $0.01926 |
| Opus 5 | $0.00043 | $0.00963 |
| Sonnet 5 | $0.00017 | $0.00385 |
| Haiku 4.5 | $0.00009 | $0.00193 |
Grade A, and why
baidu-ecosystem-ai-product-review 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.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
百度生态 AI 产品材料审核
适用场景
当用户需要审核百度生态体系“生态AI产品”申报材料时使用本 Skill，覆盖：
- 文心生态 AI 产品应用说明
- 飞桨生态 AI 产品应用说明
.doc、.docx、普通 PDF、扫描 PDF
该 Skill 适合用于：
- 初审合作伙伴提交材料
- 复核修改后的材料
- 统一审核口径，减少人工漏看截图、漏看 OCR 页面、前后逻辑不一致等问题
输入要求
- 待审核文档路径
- 若用户有额外审核口径，以用户最新口径为准
- 如有参考样例、旧版问题说明、保密说明，也应纳入判断
核心要求
1. 不能只看正文文本
- 对
.docx，必须同时读取正文和内嵌截图 - 对 PDF，必须同时检查正文、代码截图、调用量截图
- 对扫描 PDF，必须先做 OCR；OCR 低质量页要单独告警
- 如果当前环境无法提取内嵌图、OCR 质量不足或关键页无法辨认，应输出
需澄清或“建议补更清晰材料”，不要拿不完整证据继续硬判
2. 输出必须分层
输出纯文本，分成：
- 审核结论：通过 / 不通过 / 需澄清
- 不通过问题
- 告警
- 需提报人澄清
每一项都要写清：
- 位置
- 问题
- 修改建议或补充建议
3. 审核重点是“功能价值占比”，不是机械代码行数
- 模板虽然写“代码占比”，但审核时要优先看百度技术在产品核心能力中的作用
- 若百度技术是产品核心能力，即使调用代码不多，也可以接受较高占比
- 若百度技术只是大产品中的边缘子模块，却填写很高占比，应判不合理或至少告警
- 只写一个百分比数字即可；在与截图、正文、模型信息等其他证据没有明显冲突时，不需要额外要求说明评估口径或剩余部分构成
审核流程
-
识别文档类型和审核模板
- 标题含“文心生态AI产品应用说明”时按文心规则审核
- 标题含“飞桨生态AI产品应用说明”时按飞桨规则审核
- 若标题被导出截断、改名或写得不标准，则根据字段结构、模型/套件痕迹做 fallback 判断
- 例如出现
ERNIE、qianfan、aistudio等文心痕迹时优先按文心规则判断；出现Paddle、PaddleOCR、PaddleX、PaddleLite等飞桨痕迹时优先按飞桨规则判断 - 若 fallback 后仍无法判断，再向用户确认，不要在规则集不确定的情况下继续硬判
-
提取全部可见信息
- 正文文本
- docx 内嵌截图
- PDF 中的代码截图
- PDF 中的调用量截图
- 扫描页 OCR 文本
-
建立字段映射
- 产品名称
- 应用单位
- 注册地址
- 产品上线时间
- 产品功能简介
- 产品形态
- 百度技术模块
- 实际应用模型/套件
- 调用方式
- 技术范式
- 部署工具
- 落地硬件
- 百度技术占比
- 百度技术调用比例
- 代码截图
- 调用量截图
- 数据版权信息
- 收益
- 售价
- 试用链接或测试账号
-
做规则判断
- 必填项是否完整
- 功能是否写成“产品功能”而非纯技术词
- 模型/套件与功能是否匹配
- 百度技术占比、调用比例是否合理
- 代码截图、调用量截图能否形成证据链
- 收益是否量化
- 文心材料中的数据内容和量级是否完整
- 售价、试用方式是否充分
- 前后字段是否一致
-
生成审核结果
- 先给结论
- 再列出不通过问题
- 再列告警
- 再列需澄清项
关键审核口径
一、通过 / 不通过 / 告警 / 澄清的边界
- 不通过：硬性卡口问题，如关键必填缺失、关键证据缺失、收益不量化、前后重大矛盾
- 告警：可先保留但需人工关注的问题，如老模型、调用量偏少、上线时间晚于当前日期
- 需澄清：无法直接放行，但也不宜机械判不通过的情况，如保密原因导致链接或价格缺失、字段可能漏写、截图提取失败、OCR 质量不足、关键页无法辨认
二、功能项必须写业务功能
通过示例：
- 合同识别
- 票据识别
- 公文生成
- 修复评估
- 学术文本生成
不推荐写法：
- OCR识别
- 大模型生成
- 智能识别
三、模型比例的理解
- 若填写
100%，默认理解为仅使用该百度技术体系 - 不要求额外补一句“仅使用文心系模型”或“仅使用飞桨系模型”
- 只有当截图或正文中出现非该体系模型时，才把它视为不一致问题
What ships with it
3 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.
- 12d ago First seen · 203 lines · 85 tokens per session scan A 954b88bf58a5
baidu-ecosystem-ai-product-review is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 85 tokens to every session and 1,926 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 2026-08-30.
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