SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.
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 OpenSenseNova/SenseNova-Skills --skill sn-research-reportgit clone --depth 1 https://github.com/OpenSenseNova/SenseNova-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/opensensenova/sensenova-skills/sn-research-report)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-research-report"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-research-report/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/opensensenova/sensenova-skills/sn-research-report"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-research-report.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 | $0.00051 | $0.03066 |
| Opus 5 | $0.00026 | $0.01533 |
| Sonnet 5 | $0.00010 | $0.00613 |
| Haiku 4.5 | $0.00005 | $0.00307 |
Grade A, and why
sn-research-report 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 9d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sn-research-report（研究报告结构模板）
报告结构的决定因素是读者需要完成的认知任务，不是研究的信息领域。同一批信息，为做决策而读和为建立全貌而读，需要完全不同的组织方式。
模板选择
digraph select {
"研究任务" [shape=doublecircle];
"领域专属?" [shape=diamond];
"主认知任务?" [shape=diamond];
"学术综述" [shape=box];
"医疗健康" [shape=box];
"法律研究" [shape=box];
"政策分析" [shape=box];
"全景叙述" [shape=box];
"对比选型" [shape=box];
"实体调查" [shape=box];
"时序追踪" [shape=box];
"研究任务" -> "领域专属?";
"领域专属?" -> "学术综述" [label="学术综述"];
"领域专属?" -> "医疗健康" [label="医疗方案报告"];
"领域专属?" -> "法律研究" [label="法律备忘录"];
"领域专属?" -> "政策分析" [label="政策简报"];
"领域专属?" -> "主认知任务?" [label="否"];
"主认知任务?" -> "全景叙述" [label="了解全貌"];
"主认知任务?" -> "对比选型" [label="比较/选择"];
"主认知任务?" -> "实体调查" [label="深挖某对象"];
"主认知任务?" -> "时序追踪" [label="还原事件"];
}
领域专属触发条件：最终产出是学术综述、医疗方案报告、法律备忘录或政策简报，且读者对该类文档有强烈格式预期。商业/投资场景下涉及医疗或法律内容，优先按认知任务选模板，再叠加领域术语惯例。
复合意图处理：主意图决定整体框架，次意图压缩为一个章节。例如"先了解行业再评估某公司"→主意图是实体调查（论点驱动式），全景内容压缩为"行业背景"章节，不重复建立完整全景框架。
基础结构一：全景叙述型
适用：行业研究、可行性研究、技术趋势综述
读者认知任务：建立对一个空间的完整心智地图
| 章节 | 必须/可选 | 说明 |
|---|---|---|
| 摘要 | 必须 | 3-5 句，含关键结论和核心数据点 |
| 背景与现状 | 必须 | 界定研究范围，说明当前格局和发展阶段 |
| [核心维度 1-N] | 必须 | 按研究维度逐一展开，每维度自成小节 |
| 综合分析 | 必须 | 跨维度关联与洞察，不是各维度摘要 |
| 结论与展望 | 必须 | 基于证据的结论，标注确定性程度 |
| 附录 | 可选 | 数据表格、方法说明 |
可行性研究变体：在"综合分析"前插入"财务测算"章节，"结论与展望"改为"可行性判断（Go / No-Go / 有条件 Go）"，明确列出判断依据和关键假设。
基础结构二：对比选型型
适用：竞品分析、技术选型、消费决策
读者认知任务：在多个选项中做出有据可查的决策
| 章节 | 必须/可选 | 说明 |
|---|---|---|
| 摘要与推荐 | 必须 | 直接给出推荐结论，一句话说明核心理由 |
| 评估背景 | 必须 | 需求场景、约束条件、评估维度定义及权重 |
| 选项概览 | 必须 | 各选项简介，说明各自定位 |
| 对比矩阵 | 必须 | 表格：行=评估维度，列=各选项，必须有 |
| 逐维度分析 | 必须 | 矩阵无法承载的定性分析，按维度逐一展开 |
| 综合建议 | 必须 | 针对不同场景/需求的差异化建议 |
| 风险与局限 | 可选 | 推荐选项的已知缺陷，便于读者预判 |
子类差异：
| 子类 | 特有章节或要求 |
|---|---|
| 竞品分析 | 评估背景加"市场格局"（各选项市占/定位）；建议部分增加战略含义 |
| 技术选型 | 对比矩阵加"迁移成本/实施风险"行；建议部分输出 ADR（架构决策记录）格式 |
| 消费决策 | 省略战略维度；建议部分针对用户具体使用场景个性化 |
基础结构三：实体调查型
适用：尽职调查、投资研究、人物/机构背景调查
读者认知任务：全面了解某一对象，形成综合判断
根据研究目的选择三种子结构之一：
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.
- 9d ago First seen · 252 lines · 51 tokens per session scan A f2f6a28a934a
sn-research-report is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 3,066 once invoked, about $0.0003 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 2026-09-03.
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