据 AlphaMoat 统计,截至 2026-10,Deep Research 累计下载 8,357 次、获 7 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 3,328 位,在「知识管理」分类(745 个)中排名第 221 位。
列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。
| 平台 | 累计下载 | 星标 | 评论 |
|---|---|---|---|
| ClawHub ↗ | 8,357 | 7 | - |
各平台的计数口径不同,统计时间不一致。
通过 CellCog 对市场、竞争、投资、学术、尽调、金融、加密资产和新闻主题开展多来源研究与综合分析。可生成结构化研究结果;引用并非默认提供,需要明确要求并检查引用来源。提示词和上传的上下文可能发送给 CellCog,避免提交秘密或未经批准的敏感业务资料。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。 该技能有同名的其他版本,调整前的数据归属当前下载量最大的这一个。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-10 | 8,357 | — | 7 |
| 2026-09 | 7,942 | — | 7 |
| 2026-08 | 7,575 | +11.4% | 7 |
| 2026-07 | 6,800 | — | 7 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
name: deep-research-cellcog
description: "AI deep research powered by CellCog. Market research, competitive analysis, investment research, academic research, due diligence, literature reviews with citations, financial analysis, crypto research, news intelligence. Multi-source synthesis across hundreds of sources. #1 on DeepResearch Bench (Apr 2026)."
author: CellCog
homepage: https://cellcog.ai
metadata:
openclaw:
emoji: "🔬"
os: [darwin, linux, windows]
requires: bins: [python3]
env: [CELLCOG_API_KEY]
dependencies: [cellcog]
---
# Deep Research - Powered by CellCog
**#1 on DeepResearch Bench (Jul 2026 — rankings change frequently, see live leaderboard for latest).** Your AI research analyst for comprehensive, citation-backed research on any topic.
Leaderboard: https://huggingface.co/spaces/muset-ai/DeepResearch-Bench-Leaderboard
## How to Use
For your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.
**OpenClaw (fire-and-forget):**
```python
result = client.create_chat(
prompt="[your task prompt]",
notify_session_key="agent:main:main",
task_label="my-task",
chat_mode="team",
)
```
**All agents except OpenClaw (blocks until done):**
```python
from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
prompt="[your task prompt]",
task_label="my-task",
chat_mode="team",
)
print(result["message"])
```
---
## What You Can Research
### Competitive Analysis
Analyze companies against their competitors with structured insights:
- **Company vs. Competitors**: "Compare Stripe vs Square vs Adyen - market positioning, pricing, features, strengths/weaknesses"
- **SWOT Analysis**: "Create a SWOT analysis for Shopify in the e-commerce platform market"
- **Market Positioning**: "How does Notion position itself against Confluence, Coda, and Obsidian?"
- **Feature Comparison**: "Compare the AI capabilities of Salesforce, HubSpot, and Zoho CRM"
### Market Research
Understand markets, industries, and trends:
- **Industry Analysis**: "Analyze the electric vehicle market in Europe - size, growth, key players, trends"
- **Market Sizing**: "What's the TAM/SAM/SOM for AI-powered customer service tools in North America?"
- **Trend Analysis**: "What are the emerging trends in sustainable packaging for 2026?"
- **Customer Segments**: "Identify and profile the key customer segments for premium pet food"
- **Regulatory Landscape**: "Research FDA regulations for AI-powered medical devices"
### Stock & Investment Analysis
Financial research with data and analysis:
- **Company Fundamentals**: "Analyze NVIDIA's financials - revenue growth, margins, competitive moat"
- **Investment Thesis**: "Build an investment thesis for Microsoft's AI strategy"
- **Sector Analysis**: "Compare semiconductor stocks - NVDA, AMD, INTC, TSM"
- **Risk Assessment**: "What are the key risks for Tesla investors in 2026?"
- **Earnings Analysis**: "Summarize Apple's Q4 2025 earnings and forward guidance"
### Academic & Technical Research
Deep dives with proper citations:
- **Literature Review**: "Research the current state of quantum error correction techniques"
- **Technology Deep Dive**: "Explain transformer architectures and their evolution from attention mechanisms"
- **Scientific Topics**: "What's the latest research on CRISPR gene editing for cancer treatment?"
- **Historical Analysis**: "Research the history and impact of the Bretton Woods system"
### Due Diligence
Comprehensive research for decision-making:
- **Startup Due Diligence**: "Research [Company Name] - founding team, funding, product, market, competitors"
- **Vendor Evaluation**: "Compare AWS, GCP, and Azure for enterprise AI/ML workloads"
- **Partnership Analysis**: "Research potential risks and benefits of partnering with [Company]"
---
## Research Output Formats
CellCog can deliver research in multiple formats:
| Format | Best For |
|--------|----------|
| **Interactive HTML Report** | Explorable dashboards with charts, expandable sections |
| **PDF Report** | Shareable, printable professional documents |
| **Markdown** | Integration into your docs/wikis |
| **Plain Response** | Quick answers in chat |
Specify your preferred format in the prompt:
- "Create an interactive HTML report on..."
- "Generate a PDF research report analyzing..."
- "Give me a markdown summary of..."
---
## Choosing Mode & Tier
**Use `chat_mode="team"` — this is exactly what Agent Team exists for.** Deep research is THE Team use case: multi-source synthesis, cross-validation, citation verification, multiple reasoning passes.
| Scenario | Recommended |
|----------|-------------|
| Deep research, competitive analysis, market research | `chat_mode="team"` (default tier `"core"`) |
| High-stakes due diligence, institutional-grade analysis | `chat_mode="team", chat_tier="max"` (requires >=2,000 credits) |
| Quick lookups, simple summaries | `chat_mode="agent"` |
Legacy names `"agent team"` / `"agent team max"` still work forever (they map to team core / team max).
---
## Research Quality Features
### Citations (On Request)
**Citations are NOT automatic.** CellCog focuses on delivering accurate, well-researched content by default.
If you need citations:
- **Explicitly request them**: "Include citations for all factual claims with source URLs"
- **Specify format**: "Provide citations as footnotes" or "Include a references section at the end"
- **Indicate placement**: "Citations inline" vs "Citations in appendix"
Without explicit citation requests, CellCog prioritizes delivering accurate information efficiently.
### Data Accuracy
CellCog cross-references multiple sources for financial and statistical data, ensuring accuracy even without explicit citations.
### Structured Analysis
Complex research is organized with clear sections, executive summaries, and actionable insights.
### Visual Elements
Research reports can include:
- Charts and graphs
- Comparison tables
- Timeline visualizations
- Market maps
---
## Example Research Prompts
**Quick competitive intel:**
> "Compare Figma vs Sketch vs Adobe XD for enterprise UI design teams. Focus on collaboration features, pricing, and Figma's position after the Adobe acquisition failed."
**Deep market research:**
> "Create a comprehensive market research report on the AI coding assistant market. Include market size, growth projections, key players (GitHub Copilot, Cursor, Codeium, etc.), pricing models, and enterprise adoption trends. Deliver as an interactive HTML report."
**Investment analysis:**
> "Build an investment analysis for Palantir (PLTR). Cover business model, government vs commercial revenue mix, AI product strategy, valuation metrics, and key risks. Include relevant charts."
**Academic deep dive:**
> "Research the current state of nuclear fusion energy. Cover recent breakthroughs (NIF, ITER, private companies like Commonwealth Fusion), technical challenges remaining, timeline to commercial viability, and investment landscape."
---
## Tips for Better Research
1. **Be specific**: "AI market" is vague. "Enterprise AI automation market in healthcare" is better.
2. **Specify timeframe**: "Recent" is ambiguous. "2025-2026" or "last 6 months" is clearer.
3. **Define scope**: "Compare everything about X and Y" leads to bloat. "Compare X and Y on pricing, features, and market positioning" is focused.
4. **Request structure**: "Include executive summary, key findings, and recommendations" helps organize output.
5. **Mention output format**: "Deliver as PDF" or "Create interactive HTML dashboard" gets you the right format.
---
## If CellCog is not installed
**Claude Code, Cursor, Codex + 70 more agents:** `npx skills add cellcog/skills --skill cellcog`
**OpenClaw:** `openclaw skills install @cellcog/cellcog`
**CellCog plugin users:** run `/cellcog-setup` (or `/cellcog:cellcog-setup` depending on your tool)
**Manual setup:** `pip install -U cellcog` and set `CELLCOG_API_KEY`. See the **cellcog** skill for SDK reference.
通过公开搜索URL在16个搜索引擎(7个国内直连+9个国际)中做跨引擎检索、结果去重与多源交叉验证,无需API Key,内置零依赖Python脚本。Use when 用户要求“搜一下/查资料/多找几个来源/核实消息/找公众号文章/排查报错/找论文或开源项目”时。输入自然语言查询词,输出带来源链接与可信度标注的结构化结果。不适用于私有数据检索、批量抓取、需要登录后可见的内容。
通过 IMA OpenAPI 管理笔记与知识库内容。
把长文本、文档与网页内容压缩成要点和关键词的摘要工具。
通过“查一下、搜一下、搜索、找一下”等自然语言检索公开网页,整理来源并交付横版 PDF、Markdown 与标题链接表。支持用户主动开启、查看和取消技能收藏,保留原报告取回。
规定如何为网页请求选择合适工具的必读指引。
AI HOT(aihot.virxact.com)中文 AI 资讯查询 Skill,帮助用户获取今日 AI 圈动态、AI 日报、AI 热点、最新 AI 资讯以及 OpenAI、Anthropic、Google 等公司的最新发布信息。
全球12亿文献知识库(8千万中文期刊可下载)——通过 SmartLib 开放平台 API 提供中外文学术文献检索与下载能力,覆盖 8000 万篇授权中文期刊全文 + 12.28 亿条全球文献元数据(期刊 7.19 亿 / 专利 2.15 亿 / 会议 7155 万 / 学位论文 2473 万 / 标准 268 万)。 能力:中英文关键词检索、文献详情、中文期刊 PDF 全文下载、外文 OA 文献十级渠道免费下载(不消耗配额)、智能关键词扩展、核心期刊优先排序、相关性重排、引文追溯、分类号检索。 配额:首次使用自动注册,免费 100 次检索 + 10 次下载 / 月;耗尽自动弹出套餐(体验卡 / 个人版月 / 专业版月 / 单篇下载 / 下载包),可说「升级 / 充值」唤起;企业 / 机构定制联系 [email protected]。 触发:用户表达"查论文""找文献""检索学术""搜期刊""查专利""找标准""下论文""写文献综述""找参考文献""查 SCI/EI"等意图时启用;也适用"帮我找关于 XX 的论文""写文献综述""找几篇引用支撑论点"。英文:"find papers" "search literature" "write literature review" "find supporting citations"。 调用前必须先用 /consume 获取 consume_token,再凭 token 调 /search(每次计费接口调用都需一次 /consume)。
用 uvx 直接调用 markitdown,把各类文件转成 Markdown。
把研究、图文音视频与文档制作整体交给 CellCog 子 agent 的技能。
借 CellCog 做文生图、改图与成套视觉素材的图像生成技能。
由 CellCog 驱动的 AI 创意写作与故事创作技能。
CellCog Video Generation 提供提示词视频制作流程。
audio-generation-cellcog 由 CellCog 驱动 AI 音频生成与文字转语音:配音、旁白、声音克隆、数字人音色、音效、音乐、播客与对白。
数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。