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 paddle-twitter-content-opsgit 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/paddle-twitter-content-ops)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/paddle-twitter-content-ops"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/paddle-twitter-content-ops/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/paddle-twitter-content-ops"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/paddle-twitter-content-ops.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 | $0.00034 | $0.01501 |
| Opus 5 | $0.00017 | $0.00750 |
| Sonnet 5 | $0.00007 | $0.00300 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
paddle-twitter-content-ops 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PaddlePaddle Twitter Content Operations
适用场景
当用户需要将中文内容素材（微信公众号文章、飞书文档、技术资料等）转换为适合发布的英文推特文案时，使用本 Skill。
与其他内容生成 Skill 的区分：
- 本 Skill 专注于 PaddlePaddle 品牌特定的 英文推特内容生成，而非通用社交媒体文案
- 强制遵循 PaddlePaddle 的品牌风格、术语规范和内容取舍原则（见参考资料）
- 包含完整的内容分类体系（model_release、website_update、partnership 等）和对应的风格模板
- 集成了图片语言识别、链接预览卡处理等社交媒体运营特定环节
- 与通用文案生成 Skill 不同，本 Skill 对发布前预览和用户确认有硬约束要求
核心约束
以下为不可逾越的硬约束，必须严格遵守：
| 约束 | 说明 |
|---|---|
| 先预览后发布 | 必须先生成完整的图文预览展示给用户，在获得用户明确确认前，不得进行任何发布操作 |
| 用户确认是发布前提 | 无论生成的文案质量如何，必须等待用户确认后才进入检查清单和后续流程 |
| 不可跳过预览环节 | 即使用户表示信任，也不得直接发布而不经过预览环节 |
输入要求
- 内容来源：微信公众号链接、飞书文档链接、文件夹路径或原始文本
- 需要明确的内容分类（模型发布、官网更新、重要公告、合作公告等）
- 可选：目标发布时间、特殊要求
外部链接解析限制说明：
- 微信公众号链接：部分链接可能因访问限制或登录墙无法直接解析，需要用户提供文章正文文本作为辅助
- 飞书文档：公开链接可直接访问，需登录的文档需要用户提供内容或登录凭证
- 其他链接：如遇到反爬虫或访问限制，将提示用户提供原始文本内容
执行步骤
- 内容获取：根据输入类型（微信链接/飞书文档/文件夹/原始文本）选择对应的解析方式
- 内容分类：根据关键词识别内容类型（model_release、website_update、major_announcement、partnership、general）
- 信息提取：提取产品名称、核心技术特点、性能数据、技术架构、对比优势、应用场景
- 文案生成：按照对应内容类型的模板生成英文推特文案
- 图片处理：识别原文图片语言，选择合适的配图方案
- 输出预览：展示完整图文预览，等待用户确认（硬约束环节）
- 检查清单：用户确认后输出发布前检查清单
内容分类与风格
| 类型 | 关键词 | 风格 |
|---|---|---|
| model_release | 发布、模型、开源、PaddleOCR | 重磅宣布 |
| website_update | 官网、更新、新版、launch | 重磅宣布 |
| major_announcement | 计划、赛事、大赛、峰会 | 重磅宣布 |
| partnership | 合作、携手、联合、共建 | 双方握手 + @合作方 |
| general | 其他 | 科技风格 |
文案结构模板
[Emoji] [包含核心信息的标题]
[简短介绍，1-2句点明核心价值]
📊 Key Highlights:
• [具体数据点1]
• [具体数据点2]
• [技术特点3]
• [对比优势4]
🔗 Read more: [链接]
#PaddlePaddle #DeepLearning #AI
Emoji 使用规范
| 位置 | Emoji | 使用场景 |
|---|---|---|
| 标题前 | 🚀 | 新模型发布、重磅功能 |
| 标题前 | 🔥 | 里程碑、排行榜 |
| 句尾 | 🌍 | 技术特点补充 |
| 句尾 | 🙌 | 感谢、宣布 |
| 句尾 | ❤️ | 社区感谢 |
图片处理策略
图片语言识别：
- 优先使用多模态模型识别图片中是否包含中文文字
- 中文图片不直接使用，需选择替代方案
配图方案优先级：
- 链接预览卡（默认推荐）
- 官网截图（paddlepaddle.org）
- 文生图（需手动）
图片识别能力限制与替代方案：
- 若图片语言识别能力不可用：直接跳过语言判断，默认采用链接预览卡方案
- 若配图不适合（如中文图片且无法替代）：提示用户手动选择配图，或使用链接预览卡作为默认方案
- 若原文无图片或图片无法获取：使用链接预览卡，或提示用户手动添加
What ships with it
5 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 · 131 lines · 34 tokens per session scan A fc7dc14ea269
paddle-twitter-content-ops is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 1,501 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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