Download annual and periodic reports for China A-share and Hong Kong stocks from cninfo.com.cn and upload them to NotebookLM for AI-powered analysis with a specialized "Financial Analyst" persona.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncninfo-to-notebooklmExecute the skills CLI command in your project's root directory to begin installation:
Fetches cninfo-to-notebooklm from jarodise/cninfo2notebookllm and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate cninfo-to-notebooklm. Access via /cninfo-to-notebooklm in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Research competitors, compare features, identify gaps
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Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Download annual and periodic reports for China A-share and Hong Kong stocks from cninfo.com.cn and upload them to NotebookLM for AI-powered analysis with a specialized "Financial Analyst" persona.
| Market | Code Pattern | Examples |
|---|---|---|
| A-share | 6-digit codes (0xxxxx, 3xxxxx, 6xxxxx) | 600519 (贵州茅台), 000001 (平安银行) |
| Hong Kong | 5-digit codes (00xxx, 01xxx, 02xxx, 09xxx) | 00700 (腾讯控股), 09988 (阿里巴巴) |
User provides stock name/code
↓
1. Look up stock in database (auto-detect market)
↓
2. Download reports from cninfo:
- Last 5 years annual reports (年度报告)
- Current year: Q1, semi-annual, Q3 reports
↓
3. Create NotebookLM notebook
↓
4. Configure "Financial Analyst" persona with custom prompt
↓
5. Upload all PDFs as sources
↓
6. Return notebook ID ✅
Crucial: Before running the script, verify the environment is ready.
Check Dependencies: Verify if the dependencies are installed (specifically notebooklm and playwright).
Install: If dependencies are missing or this is the first run, execute the installation script:
chmod +x install.sh && ./install.sh
Authenticate: Ensure the user has authenticated with NotebookLM (notebooklm login). If not, ask them to do so.
Run the script from the skill directory:
python3 scripts/run.py <stock_code_or_name>
Examples:
python3 scripts/run.py 600350 - A-share stockpython3 scripts/run.py 山东高速 - A-share by namepython3 scripts/run.py 00700 - Hong Kong stock (Tencent)python3 scripts/run.py 腾讯控股 - Hong Kong by nameThis script handles everything:
assets/financial_analyst_prompt.txt.Provide:
The skill uses a custom system prompt located at:
assets/financial_analyst_prompt.txt
This prompt configures NotebookLM to act as a "Financial Report Analyst" based on "Hand-holding Financial Reporting" methodology.
| Error | Solution |
|---|---|
| Stock not found | Check if code is valid A-share or Hong Kong stock |
| NotebookLM CLI not found | Ensure notebooklm-py matches requirements.txt and is in PATH |
| Auth missing | Run notebooklm login to authenticate via browser |
| Upload failed | Check network connection and NotebookLM service status |
httpx packagenotebooklm-py packageplaywright (for authentication)| Report Type | Category Code | Period |
|---|---|---|
| Annual | category_ndbg_szsh |
Previous 5 years |
| Semi-Annual | category_bndbg_szsh |
Current year |
| Q1 Report | category_yjdbg_szsh |
Current year |
| Q3 Report | category_sjdbg_szsh |
Current year |
| Aspect | A-share | Hong Kong |
|---|---|---|
| Market code | szse |
hke |
| Categories | Uses category codes | Empty categories |
| Search key | Uses Chinese search terms | Empty search key |
| Report naming | YYYY年年度报告 |
May use Arabic/Chinese numerals |
| Search period | Following year (March-June) | Same year or following year |
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
cninfo-to-notebooklm reduced setup friction for our internal harness; good balance of opinion and flexibility.
cninfo-to-notebooklm has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in cninfo-to-notebooklm — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for cninfo-to-notebooklm matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend cninfo-to-notebooklm for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
cninfo-to-notebooklm fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
cninfo-to-notebooklm is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
cninfo-to-notebooklm reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in cninfo-to-notebooklm — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added cninfo-to-notebooklm from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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