Octop is a self-hosted, multi-user AI assistant that runs multiple specialized agents and connects them to chat interfaces, tools, and external services. It is for individuals, families, and teams who want a locally operated assistant with shared experts and persistent capabilities. Catalogue add-ons extend its agent and assistant workflows.
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 TencentCloud/Octop --skill insurance-policy-learninggit clone --depth 1 https://github.com/TencentCloud/OctopWrote 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/tencentcloud/octop/insurance-policy-learning)<a href="https://agentmods.dev/skills/tencentcloud/octop/insurance-policy-learning"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/insurance-policy-learning/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/tencentcloud/octop/insurance-policy-learning"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/insurance-policy-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.01348 |
| Opus 5 | $0.00043 | $0.00674 |
| Sonnet 5 | $0.00017 | $0.00270 |
| Haiku 4.5 | $0.00009 | $0.00135 |
Grade A, and why
insurance-policy-learning 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 4d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
地区医保政策学习
本 skill 有两种模式：
| 模式 | 覆盖范围 | 使用模板 | 校验模块 |
|---|---|---|---|
| 最新变化 | 最近一周／自上次推送以来 | 地区医保政策变化学习 | insurance_policy_learning |
| 长周期回顾 | 近一年、本年度、去年至今或用户指定区间 | 医保政策回顾学习（长周期） | insurance_policy_retrospective |
用户说「只看本周变化」用前者；说「近一年重点政策」「今年有哪些新政策」「回顾一下」用后者。定时推送默认用最新变化模式；用户可另外按需请求回顾。
必读文件
起草前必须先读取：
../../USER.md（脚本生成的只读摘要）../../references/compliance-boundary.md../../references/source-policy.yaml../../references/output-templates.md
使用档案
优先调用 ../../scripts/clinical_profile.py get 获取结构化档案；USER.md 只作为脚本生成的只读摘要。不得直接编辑 USER.md。
从档案获取：
- 省、市、区县
- 医院
- 科室和科室系统
- 医院能力边界
如果地区登记不完整，先提示补全地区，不生成正式医保政策学习内容。
地区匹配
只使用医生登记地区相关政策：
- 优先区县正式文件。
- 区县无正式文件时，回溯到市级正式文件。
- 市级无正式文件时，回溯到省级正式文件。
- 必须明确本地执行口径需以医保办确认为准。
信源要求
最终依据只能使用医保局、政府部门、卫健委或其他正式机构发布的原文页面或正式附件。公众号、新闻、商业网站和转载材料只能作为线索，不得作为最终依据。
涉及“新增、调出、扩大、缩小、调整”时，必须同时取得新旧正式文件并可对照；否则只能写“版本差异需人工核验”。
输出边界
只输出：
- 政策文件名称、发布机构、生效日期
- 政策原文摘要
- 需要本地医保办确认的事项
- 权威信源链接
- 不作为报销依据的提示
- 来源行必须写成
来源：{文件或页面名称}：[链接]({URL})。链接文字固定为“链接”，不得裸露长 URL。
不得输出：
- 临床行动建议
- HIS 提醒或系统规则
- 病历书写要求
- 报销结论、支付比例或支付条件承诺
- 某项目在用户所在医院一定可报销的判断
长周期回顾模式
用户要求回顾近一年或指定区间时：
- 先确认回顾区间并在输出中写明（如 2025-08-01 至 2026-07-31）。用户说「近一年」按当前日期往前推 12 个月；说「今年」按本年度 1 月 1 日起算。
- 检索范围同样遵守区县 → 市 → 省的地区匹配规则，并可纳入国家级重点政策；在输出中说明覆盖了哪些层级。
- 按发布时间排序，便于用户看到政策演进脉络。
- 必须标注每份文件的状态：现行有效 / 已废止 / 已被替代。这是长周期回顾特有的风险点——不得把失效政策当作现行有效呈现。发现被替代关系时，写明由哪份新文件替代。
- 按主题归纳学习提示（如价格调整、慢特病管理、支付方式改革），而不是简单罗列文件。
- 每份文件独立给出来源行；区间内未检索到可回溯权威原文时如实说明，不用二手平台补齐，也不用旧文件凑数。
- 数量控制：优先选择对基层医生影响最大的重点政策，避免一次列出过多文件；用户可要求继续展开。
长周期回顾的输出边界与最新变化模式完全一致——仍然只做原文摘要与学习提示，不给报销结论、支付比例或院内结算口径，结尾保留本地确认提示。
输出要求
输出语言与格式：全程使用简体中文；只输出最终正文，不输出思考、推理、检索或执行过程（禁止出现 I'll、Let me、搜索过程、检索日志等过程语句）；执行检索时静默，只在正文生成后输出。
最新变化模式使用 ../../references/output-templates.md 中的“地区医保政策变化学习”模板；长周期回顾使用“医保政策回顾学习（长周期）”模板。
发送前必须运行或按同等规则检查：
../../scripts/validate_output.py --module insurance_policy_learning- 长周期回顾用
../../scripts/validate_output.py --module insurance_policy_retrospective
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.
- 4d ago Changed · +9 lines 2a6d6ab86f4b
- 11d ago First seen · 99 lines · 86 tokens per session scan A 41e23467cf62
insurance-policy-learning is a skill published in the GitHub repository TencentCloud/Octop (1,533 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,348 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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