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-deep-researchgit 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-deep-research)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-deep-research"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-deep-research/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-deep-research"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-deep-research.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 | $0.00263 | $0.08508 |
| Opus 5 | $0.00131 | $0.04254 |
| Sonnet 5 | $0.00053 | $0.01702 |
| Haiku 4.5 | $0.00026 | $0.00851 |
Grade A, and why
sn-deep-research 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 — 553 lines — stays where its author put it; the contents beside it link to each section on GitHub.
深度研究（多 Agent 深度研究编排）
你是深度研究总控。职责是调度专家角色完成研究、写作与渲染。
阅读地图：§1 总则 → §2 派发机制 → §3 报告目录 → §4 档位选择器（决定跑什么） → §5 阶段库（每个角色怎么派，仅一次） → §6 附录。运行时先按 §4 选定本次档位的流水线，再按流水线逐步跳转 §5 的对应条目。
1. 总则
控制器铁律：
- 只调度，不读大文件：evidence / 章节 / outline 等大文件通过绝对路径传给角色自读；你只读调度所需的小字段（见 §6）。
- 所有文件路径使用绝对路径。
- 通过文件路径传递内容，不在消息里粘贴大段正文。
- 子角色内部检查透明：各子 agent 自行完成交付检查和必要修复。
- 语言锚定（全档位、全流程硬约束）：你在首次派发前只确定一次请求级输出语言并保存为
language。用户明确指定的输出语言优先；否则使用原始 query 的主要指令语言。不要因专名、代码、引用、搜索词或来源语言改变该判断；混合语言且无显式要求时，以用户提出任务和约束所用的主要自然语言为准。 - 格式锚定（全档位、全流程硬约束）：你在首次派发前只确定一次请求级最终形式并保存为一个非空字符串
format。用户明确指定的形式优先；否则使用report。常见值如report、paper、table、memo，也允许用户自己的短名称。format只存在于本次运行上下文和角色 payload，不创建format.json、proposal 或配套 schema。 - 你的进度更新、档位/格式确认、澄清问题、错误/降级说明和最终交付回复都使用
language。 - 你的子任务 payload 都必须显式传递
language:{language}与format:{format}。 - 用户在运行中明确要求切换输出语言时，你需要更新
language，之后的派发使用新值。 - 用户在运行中明确要求切换最终形式时，你需要更新
format，之后的派发使用新值；已经生成且会进入终稿的编排或正文产物必须按新形式重做。
环境配置分级（任务开始前，你统一处理一次）：
Tier 1 — 强制能力，必须探测：文件读写、命令执行、网页搜索、网页抓取，是产出可靠研究的硬前提。探测到任一未就绪 → 暂停，提醒用户配置 / 启用，在具备前不派发任何角色。
Tier 2 / Tier 3 — 可选配置，不探测但须告知 + 确认：你在开始时一次性告知用户：下列可选项未配置会降级、影响效果，请确认是否继续（或先配置再跑）。
统一凭证配置：搜索、社媒、金融、学术与图片生成所需的 API key / token / cookie 统一建议写在仓库根目录 .env（参考 .env.example），由 runtime 或用户在执行前加载为同名环境变量。skill 与脚本只读取环境变量；不要把密钥写入 payload、命令行参数、报告正文、日志或 transcript。
| 层级 | 可选配置（环境变量） | 缺失影响 |
|---|---|---|
| Tier 2 | SN_IMAGE_GEN_API_KEY / SN_API_KEY |
无 AI 概念配图，输出无图版 |
| Tier 2 | ZHIHU_COOKIE / DOUYIN_COOKIE / BILIBILI_COOKIE |
知乎/抖音/B站的脚本检索能力受限，转通用搜索兜底；小红书/微博当前本就使用 browser-use / 公开网页兜底 |
| Tier 2 | TIKHUB_TOKEN（Twitter/X）、YOUTUBE_API_KEY |
对应平台无站内检索，转通用搜索兜底（Reddit 免认证） |
| Tier 3 | GitHub token、HF_TOKEN、SO_API_KEY、学术 API key |
仅速率受限、更慢更易限流（GitHub code 搜索无 token 则不可用；arXiv 等开放获取与金融/市场/年报等免认证来源无需配置） |
2. 子 agent 派发机制
2.1 路径与 token
先解析当前 skill 目录绝对路径。不同 runtime 暴露不同占位符，只用被替换成真实路径的那个，其余保持字面量时忽略：
${SKILL_DIR} ← Claude Code
${HERMES_SKILL_DIR} ← Hermes
{baseDir} ← OpenClaw
What ships with it
30 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.
- agents/perspective.md 6.3 KB
- agents/plan.md 21 KB
- agents/report-planner.md 9.8 KB
- agents/report-stitcher.md 7.0 KB
- agents/report-writer.md 11 KB
- agents/research.md 25 KB
- agents/review.md 16 KB
- agents/scout.md 12 KB
- agents/supplement-planner.md 11 KB
- schemas/briefing.schema.md 8.3 KB
- schemas/evidence.schema.md 8.7 KB
- schemas/outline.schema.md 12 KB
- schemas/perspective_feedback.schema.md 2.7 KB
- schemas/plan.schema.md 6.8 KB
- schemas/supplement_plan.schema.md 3.3 KB
- scripts/validate_briefing.py 41 KB runs code
- scripts/validate_evidence.py 19 KB runs code
- scripts/validate_outline.py 43 KB runs code
- scripts/validate_plan.py 13 KB runs code
- scripts/validate_supplement_plan.py 15 KB runs code
- workbench-runtime/bin/sensenova-ppt-workbench.mjs 21 KB runs code
- workbench-runtime/bin/sensenova-research-workbench.mjs 142 B runs code
- workbench-runtime/dist-server/index.mjs 1846 KB runs code
- workbench-runtime/dist/assets/index-BrQYg30H.js 546 KB runs code
- workbench-runtime/dist/assets/index-Bv_HE0iq.css 148 KB
- workbench-runtime/dist/assets/MarkdownArtifactViewer-C7xIspsK.js 108 KB runs code
- workbench-runtime/dist/favicon.svg 9.3 KB
- workbench-runtime/dist/index.html 531 B
- workbench-runtime/dist/sensetime-mark.png 24 KB
- workbench-runtime/package.json 418 B
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 · 553 lines · 263 tokens per session scan A 4339409cc459
sn-deep-research is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed today), licensed MIT. It adds 263 tokens to every session and 8,508 once invoked, about $0.0013 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
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skin-creator
Create and apply a two-asset LobsterAI visual skin from the user's style description. Use only when the AI Skin Designer kit supplies the structured skinpack workflow marker; do not use for ordinary theme or image requests.
content-planner
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music-search
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stock-analyzer
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ha-skill-creator
Create, edit, improve, or audit Hope Agent skills. Use when the user wants to: (1) create a new skill from scratch, (2) edit or improve an existing skill, (3) review or clean up a SKILL.md file, (4) run evaluations to test skill effectiveness, (5) optimize skill descriptions for better trigger accuracy. Trigger…