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 agentmods add skills/tencentcloud/octop/output-formatnpx skills add TencentCloud/Octop --skill output-formatgit 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/output-format)<a href="https://agentmods.dev/skills/tencentcloud/octop/output-format"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/output-format.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.01654 |
| Opus 5 | $0.00036 | $0.00827 |
| Sonnet 5 | $0.00014 | $0.00331 |
| Haiku 4.5 | $0.00007 | $0.00165 |
Grade A, and why
output-format 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 yesterday.
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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
医学安全域输出格式校验
适用范围
以下模块的输出（无论对话内即时回答还是定时推送）必须经本技能校验：
guideline_learning、daily_guideline_learning、guideline_section_expansion、guideline_learning_pathway、guideline_learning_diagnosis、guideline_update_reminder、professional_update_summary、insurance_policy_summary、insurance_policy_learning、insurance_policy_retrospective、exam_material_recommendation。
普通教育性医学问答不加载本技能：由 clinical-q-and-a 使用内嵌的短模板和四项自检，避免为一段简短解释加载完整模板、写草稿文件和启动校验脚本。问题一旦涉及精确推荐/定位、药品高风险事实、版本比较、医保监管，立即退出快路径，转入对应专业 skill 与本技能。
通用任务（写作/翻译/编程/计划/数据整理等）不套用本技能：不要求模板头、不要求权威来源行、不加医学免责声明。校验时对通用输出使用 --module general_task，不要误用医学模块名。
纯信源核验不套用本技能：只判断文件是否为权威原文、是否最新有效以及修订/替代/废止关系时，由 source-verify skill 使用自己的【信源核验】模板完成，不再调用本技能或 validate_output.py。如果核验后还要生成指南学习内容，再对学习内容调用本技能。
资源路径
以下路径均从当前 skills/output-format/SKILL.md 向上两级定位专家工作区，必须直接使用，不得通过 glob、grep 或递归 ls 寻找，也不得为调用校验器而先读取脚本源码：
../../references/output-templates.md../../references/source-policy.yaml../../scripts/validate_output.py
校验器从标准输入或 --text-file 接收待校验正文。不要无正文试运行校验器，也不要通过读取/搜索源码猜参数或模板规则。
常用模块映射
| 用户请求实质 | module | 模板要求 |
|---|---|---|
| 普通概念解释、常见医学误区澄清 | clinical_q_and_a |
由 clinical-q-and-a 轻量自检，不执行本技能脚本流程 |
| 总结、展开或讲解指南中的某章、某节、筛查/随访等专题 | guideline_section_expansion |
【指南章节展开】；含依据、章节、原文定位、原文要点、学习提示、边界及“不替代原文” |
| 把整份指南整理为学习顺序或学习路径 | guideline_learning_pathway |
【指南学习路径图】；含依据、前置知识、编号学习顺序、边界 |
| 每日固定学习单元 | daily_guideline_learning |
【指南学习单元】；恰好 3 个编号要点及单元进度 |
| 学习状态或薄弱点评估 | guideline_learning_diagnosis |
【指南学习诊断】或【指南诊断标准学习】 |
命中上表后直接使用对应 module，不读取校验器源码二次判断。
四条硬格式（每次都必须）
- 模板头：首行
【{模块名}｜{主题}】，如【指南章节展开｜风险分层】。 - 来源行：结尾
来源：{文件名称}：[链接]({URL})，正例：来源：国家卫生健康委官网：[链接](https://www.nhc.gov.cn/)。给出期刊名、DOI、文件名时必须同时包成可点击链接，不允许只写文字出处。取不到权威链接时写明"未取得可核验权威来源，需人工核验"——不得省略来源行，不得伪造链接。 - 边界声明：按模块带对应一句——章节展开"不替代原文"、备考"以官方考试大纲为准"、医保"不作为报销依据"、涉患者请求"不提供个体诊疗"。
- 不得编造：页码、条目号、版本、文件名、机构名称，取不到权威依据就标"待核验"，绝不凭记忆补全。
详细模板见 ../../references/output-templates.md。
校验流程
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.
- yesterday Changed d268878e6e69
- 6d ago First seen · 81 lines · 72 tokens per session scan A e13f982525da
output-format is a skill published in the GitHub repository TencentCloud/Octop (1,454 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,654 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.
Other skills, from other repositories
manim-composer
Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization.…
retro-learn
Convert delivery findings into skill, eval, workflow, and documentation improvements.
system-design-case-catalog
Answer classic system design problems as constraint-to-solution sketches and coach interview practice: URL shortener, rate limiter, news feed, chat, notification, autocomplete, crawler, unique id. Use for interview practice or naming the closest known shape for a new problem.
documentary-montage-production
Provider-independent workflow for AI agents producing documentary-style montages, archive-driven timelines, interview-supported sequences, nonprofit or advocacy shorts, historical explainers, and hybrid generated/archive edits. Use for editorial thesis development, research/source logs, fact-checking, archive rights…
educational-animation-production
Provider-independent production workflow for AI agents creating educational animated lessons, classroom explainers, STEM visualizations, history/social-science animations, training modules, microlearning clips, whiteboard-style explainers, diagram-driven videos, and generated or assembled instructional animation…
explainer-video-production
Provider-independent explainer video production for AI agents creating educational, product, concept, nonprofit/public-service, onboarding, training, animated, mixed-media, and short social explainers. Use when an agent must turn a topic, brief, research corpus, product, process, policy, dataset, or complex idea into…