baidu-paddle-wenxin-operation

baidu-paddle-wenxin-operation is a skill for Claude Code from AgenticAIPlan/AgenticAISkills. It costs 106 tokens per session (1,262 once invoked), scanned A, original, MIT.

A research and reporting workflow for tracking Baidu PaddlePaddle, ERNIE models, AI competitors, Xiamen policies and events, and potential local customers.

In plain words
What is it for?
Use it for operations briefings, competitor monitoring, Xiamen AI policy and event lists, and prioritized customer-lead tables. It can produce documents, weekly reports, spreadsheets, or structured tables.
Why use it?
It gathers scattered and time-sensitive information into categorized reports with sources, dates, explanations, and items needing confirmation. PaddlePaddle is Baidu’s AI framework, while ERNIE is its model family.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agentic-ai-skills plugin — 54 skills shipped together

Good fit Use it for operations briefings, competitor monitoring, Xiamen AI policy and event lists, and prioritized customer-lead tables. It can produce documents, weekly reports, spreadsheets, or structured tables.

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Install with agentmods
npx agentmods add skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation
Install

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.

Any agent
npx skills add AgenticAIPlan/AgenticAISkills --skill baidu-paddle-wenxin-operation
Clone the repo
git clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkills

Made for: Claude Code.

Or install agentic-ai-skills, the plugin that ships this one along with the rest of its 54 skills.

Wrote 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.

agentmods badge for baidu-paddle-wenxin-operation

README.md
[![agentmods](https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation/github.svg)](https://agentmods.dev/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation)
Your own site
<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation/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.

agentmods 80×15 button for baidu-paddle-wenxin-operation

Your own site · 80×15
<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/baidu-paddle-wenxin-operation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,262 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00106 $0.01262
Opus 5 $0.00053 $0.00631
Sonnet 5 $0.00021 $0.00252
Haiku 4.5 $0.00011 $0.00126

Measured 12d ago against content hash 14ea31167612, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

baidu-paddle-wenxin-operation 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.

skills/baidu-paddle-wenxin-operation/SKILL.md · 68 lines

What it actually says

Baidu Paddle Wenxin Operation

适用场景

当用户需要围绕百度飞桨、文心大模型及厦门本地 AI 生态开展日常运营信息搜集和线索整理时,使用本 Skill。

典型场景包括:

  • 跟踪文心大模型、飞桨框架的最新发布、价格调整、活动和案例动态
  • 汇总国内外大模型竞品的版本更新、推广动作和行业趋势
  • 搜集厦门本地 AI 政策、补贴、活动和场景机会清单
  • 挖掘厦门本地与 AI、数据、数字化转型相关的潜在客户并整理优先级

输入要求

  • 业务目标:需要执行的模块范围,例如仅做资讯搜集、仅做客户挖掘,或四个模块全部执行
  • 时间范围:当日动态、近 3 日动态、周度汇总等
  • 重点关注对象:可指定企业、政策、活动、竞品或行业方向
  • 输出形式:简报文档、周报、Excel 清单或结构化表格

执行步骤

  1. 先确认本次执行范围,对齐需要覆盖的模块、时间范围和重点关注对象。
  2. 按模块联网检索信息,并优先使用 references/source-map.md 中的高优先级来源:
    • 模块一:百度飞桨与文心大模型官方动态、开发者生态、运营相关活动
    • 模块二:国内外大模型及竞品动态,重点关注版本、价格、推广和行业趋势
    • 模块三:厦门本地 AI 政策、补贴、产业园区动态、供需活动与机会清单
    • 模块四:厦门本地潜在客户,优先识别有明确 AI 需求信号的企业
  3. 对采集结果做筛选和结构化整理:
    • 保留权威、近期、与运营工作直接相关的信息,二手转载需尽量回溯原始来源
    • 为每条资讯补充核心解读和运营参考,并标注来源链接、发布日期和抓取日期
    • 为每个潜在客户补充需求方向、匹配度、优先级和明确的线索来源
  4. 输出整理结果:
    • 资讯类内容按模块分类,标注信息类型、发布时间、抓取日期、来源 URL、核心要点和运营价值
    • 潜在客户清单按优先级排序,保留企业名称、需求方向、线索来源、信号强弱和跟进建议
  5. 结果自查:
    • 去重并剔除过期或无直接相关性的内容
    • 检查资讯来源是否清晰、客户线索是否有依据,无法核实的信息必须标注为 待确认
    • 对价格、政策、报名时间、客户信号等易过期信息,明确写出“发布时间”或“抓取日期”
    • 对需要后续跟进的信息补充待确认项,不得把无来源内容写成既成事实

输出要求

  • 资讯汇总:按模块组织,至少包含资讯标题、信息类型、发布时间、抓取日期、来源名称、来源 URL、核心解读、运营参考、待确认项
  • 竞品动态:与文心和飞桨相关的竞品信息应单独标识,便于对比阅读
  • 厦门政策活动清单:标明信息类型、发布单位、来源 URL、发布时间、实施时间或活动时间、报名截止时间(如有)
  • 潜在客户清单:建议使用表格结构,至少包含企业名称、需求方向、匹配度、优先级、线索来源、来源 URL、抓取日期、待确认项
  • 周度总结:提炼本周核心动态、对运营工作的价值以及建议跟进事项,并明确哪些结论仍需补充验证

实时信息约束

  • 本 Skill 处理的是会随时间变化的运营信息,默认必须联网检索,不依赖记忆直接给出“最新动态”。
  • 任何涉及发布时间、价格、政策补贴、活动安排、竞品版本、企业线索的内容,都必须带来源 URL。
  • 无法确认原始来源、发布时间或真实性时,不得写成确定事实,应明确标注 待确认
  • 对二手媒体、转载文章、摘要号内容,优先回溯到官方公告、企业官网、原始活动页或原始报道。
  • 对潜在客户线索,至少提供一个明确信号来源,例如官网公告、招聘页、招投标信息、活动名单或政府清单。

参考资料

  • references/source-map.md:四个模块的建议信息源和筛选重点
  • references/output-template.md:资讯汇总、政策活动清单和潜在客户清单的推荐输出格式
  • evals/evals.json:建议用于校验来源、日期、线索强弱标注是否完整的最小测试集
Files

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.

Changes

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.

  1. 12d ago First seen · 68 lines · 106 tokens per session scan A 14ea31167612

Subscribe to this mod's changes

baidu-paddle-wenxin-operation is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 106 tokens to every session and 1,262 once invoked, about $0.0005 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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