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 exam-material-recommendationgit 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/exam-material-recommendation)<a href="https://agentmods.dev/skills/tencentcloud/octop/exam-material-recommendation"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/exam-material-recommendation/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/exam-material-recommendation"><img src="https://agentmods.dev/badge/skills/tencentcloud/octop/exam-material-recommendation.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 | $0.00075 | $0.00986 |
| Opus 5 | $0.00037 | $0.00493 |
| Sonnet 5 | $0.00015 | $0.00197 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
exam-material-recommendation 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.
What it actually says
备考与岗位导向的学习材料推荐
用户反馈:助手无法根据自身情况(例如正在准备副高考试)推荐相应教材,只能给固定内容。本 skill 让学习内容由用户的目标驱动。
必读边界
- ../../references/compliance-boundary.md
- ../../references/source-policy.yaml
- ../../references/output-templates.md
采集目标(最少提问)
需要以下信息,缺失时一次性只问最关键的 1-2 项,不要连环追问:
- 考试或岗位目标:副高/正高/中级/规培结业/执业医师/专项能力培训/单纯工作复习。
- 目标时间:年月即可,不确定可写待定。
- 每日可投入时间:用于决定材料数量与颗粒度。
- 专业方向:可复用已登记的科室;未登记时直接问,不强制走完整 5 项登记。
只有需要按科室、地区、职称长期定制或开启订阅时,才转 doctor-registration 获取档案与同意。单次推荐不要求登记。
推荐材料的优先级
- 当年官方考试大纲(考办/卫健委/人事考试网正式发布)——最高优先级,是范围基准。
- 权威指南与共识——对应大纲中的疾病与主题。
- 国家或行业规范、教材——管理、质控、公共卫生类知识。
- 政策文件——涉及医保、基层服务规范时。
必须严格区分两类内容并在输出中分开标注:
- 官方大纲要求:来自正式文件的范围表述。
- 助手补充建议:你基于科室高频主题给出的补充,明确标为建议。
硬性风险边界
- 结尾必须写「以官方考试大纲为准」,并说明本推荐不代表考试范围、题型、分值或必考点。
- 不得承诺通过率、押题、划重点式断言必考。
- 不得编造教材名称、版本、书号、出版社或大纲条目。取不到权威大纲原文时,只给学习方向初稿并标注「待核验官方大纲」。
- 大纲会逐年调整;引用往年大纲时必须标注年份并提示以当年为准。
- 本 skill 只推荐学习材料,不输出任何个体诊疗内容、药物剂量或处置建议。
与学习目标、轨道的衔接
用户确认后,可把考试目标保存为学习目标,用于驱动后续轨道选材与内容深度:
python ../../scripts/clinical_profile.py learning-goal-save \
--label <考试目标名称> --kind exam \
--daily-minutes <5-240> --target-date <YYYY-MM-DD,可省略> \
--priority <0-100> --confirm true
保存前先复述目标、每日时间和目标日期并取得明确确认。保存后:
- 创建轨道时用
--goal-id绑定该目标,使每日单元围绕备考重点排序。 - 内容深度参考职称对应层级(见 BOOTSTRAP.md 的自动识别规则)。
- 用户目标变更(如考期推迟、改考方向)时更新目标,不要静默沿用旧选材。
输出与校验
使用 output-templates.md 的「备考学习材料推荐」模板。发送前运行:
python ../../scripts/validate_output.py --module exam_material_recommendation
校验失败时先修正;不得绕过。
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 9e4dffa7d453
- 10d ago First seen · 69 lines · 75 tokens per session scan A ad375d49e015
exam-material-recommendation is a skill published in the GitHub repository TencentCloud/Octop (1,513 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 986 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.…
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…
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…
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…
manim-explainer-animation
Provider-independent production workflow for creating Manim-based explainer animations. Use when an agent must plan, code, render, QA, or hand off precise math, science, data, diagram, algorithm, or concept animations with Manim, including storyboard, scene architecture, formulas, coordinate systems, voiceover timing…
yaml
Skill "yaml" from dhaupin/vant, covering yaml, when to use, what to do, 1. syntax and 2. anchors.