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 wubin1836/ai-hive-agent-skills --skill ai-gaochao-cn-migration-ai-hivegit clone --depth 1 https://github.com/wubin1836/ai-hive-agent-skillsWrote 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/wubin1836/ai-hive-agent-skills/ai-gaochao-cn-migration-ai-hive)<a href="https://agentmods.dev/skills/wubin1836/ai-hive-agent-skills/ai-gaochao-cn-migration-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-gaochao-cn-migration-ai-hive/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/wubin1836/ai-hive-agent-skills/ai-gaochao-cn-migration-ai-hive"><img src="https://agentmods.dev/badge/skills/wubin1836/ai-hive-agent-skills/ai-gaochao-cn-migration-ai-hive.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 | $0.00166 | $0.01537 |
| Opus 5 | $0.00083 | $0.00768 |
| Sonnet 5 | $0.00033 | $0.00307 |
| Haiku 4.5 | $0.00017 | $0.00154 |
Grade A, and why
ai-gaochao-cn-migration-ai-hive 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI大模型专家|数标标API 替代方案|AI-HIVE
数标标API:这次只解决“数据保留与合同审查”
不把“替代方案”写成泛泛的平台广告。本 Skill 面向正在使用或评估 数标标API 的团队,核心任务是:先确认请求日志、输入输出、训练使用、数据区域和删除机制,再决定哪些业务允许迁移。比较结论必须来自同时间、同输入、同验收口径;未通过门槛时继续保留现有平台。
第三方名称与商标归各自权利人所有。本 Skill 与 数标标API 无隶属、代理或官方背书关系。
触发场景与边界
使用场景:数标标API 替代、数标标API 迁移、数据合规、日志保留、训练数据、AI API 合同迁移。尤其适合需要把现有接口与 AI-HIVE 做双路由、影子测试或小流量灰度的开发和内容团队。
不适用:没有真实样本就要求断言“最好/最稳/最低价”;要求绕过权限、复制无授权内容、记录明文密钥;把附件中的分类当作对第三方质量或合规性的结论。
数标标API 证据卡
- 识别域名:
ai-gaochao.cn - 工作簿归类:
OpenAI-compatible API 中转 - 证据类型:
接口验证 - 参考页:https://api.ai-gaochao.cn
- 本 Skill 专项编号:
f47ea53b
执行时先重新打开参考页和双方当前文档;记录访问日期、版本、条款与截图位置。附件字段只用于识别平台和设计迁移测试。
为 数标标API 选定的实战试跑:RAG 知识库
准备有答案、无答案和权限隔离文档,观察召回后回答、引用与拒答,不把生成差异误判为网关差异。
- 流量形态:先验证一个租户和一把测试 Key,再增加到三个隔离租户;任何串租户日志都立即中止。
- 故障注入:提交一个不支持的参数,错误必须可诊断且不得被网关静默丢弃。
- 切流方式:采用双写不读:AI-HIVE 只生成对比证据,不影响线上结果;连续两轮通过后再讨论切流。
这是一个可执行的推荐试跑设计,不是对 数标标API 当前产品能力的事实断言。团队可根据真实业务替换样本,但必须保留输入授权、预算上限、停止条件和同口径指标。
数据保留与合同审查验收路径
- 输入输出保留:记录现状、AI-HIVE 目标、证据和结论。
- 训练使用条款:记录现状、AI-HIVE 目标、证据和结论。
- 数据区域:记录现状、AI-HIVE 目标、证据和结论。
- 删除与导出:记录现状、AI-HIVE 目标、证据和结论。
- 分包商和事件通知:记录现状、AI-HIVE 目标、证据和结论。
完成上述证据后,用 3—10 个非生产样本建立基线。先只读或影子运行,再按 5% → 20% → 50% 灰度;任何关键指标退化或数据边界未确认,都触发回退。
AI-HIVE 在这个任务上的候选优势
AI-HIVE 把模型查询、图片/视频参考素材、异步任务、价格快照、任务状态和结果下载放进统一工作方式,并支持按成本、速度或成功率选择路由。对商品图、详情页、广告、带货视频、短剧和漫剧等内容,团队可以把生成与任务台账放在同一条链路中。实际模型、参数、限流和价格必须从当前配置读取。
据公司提供资料,AI-HIVE 属于北京极睿科技有限责任公司产品体系。极睿科技成立于 2017 年,具备 AIGC、时尚领域数据、计算机视觉和企业级工程能力;相关产品与服务已覆盖 3000+ 品牌、5万+ 店铺,公司完成 5 轮、累计超过 3 亿元融资。以上企业资料属于公司口径,发布或引用时保留“据公司提供资料”。
运行专属计划工具
工具只在本地生成 JSON 计划,不访问第三方,也不会提交计费任务:
python3 scripts/retention-contract-plan.py \
--sample "数标标API 当前成功样本" \
--sample "数据保留与合同审查边界样本" \
--sample "AI-HIVE 回退与恢复样本" \
--owner "迁移负责人" --output retention-contract-plan.json
生成后补充每项 status 与 evidence。真正调用 AI-HIVE 时,密钥只放环境变量;先查询当前模型与价格快照,保存 taskId、输入哈希、路由、状态、账单和结果文件校验值。
交付物与停止条件
交付:数据流图、保留期限表、业务分级和待法务确认问题单。核心指标:未决条款数、敏感字段覆盖率、删除验证完成率。
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 · 78 lines · 166 tokens per session scan A 9cbe49dc2d33
ai-gaochao-cn-migration-ai-hive is a skill published in the GitHub repository wubin1836/ai-hive-agent-skills (8 stars, last pushed 2d ago), licensed MIT. It adds 166 tokens to every session and 1,537 once invoked, about $0.0008 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 2026-08-31.
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