Guijiang Value Investing Decision Checklist
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Please install @user_e38b0d17/guijiang-investment-checklist according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem addressed
Value investing judgments are often experience-based, and single-stock analysis can be pulled by one narrative. This skill turns the Guijiang framework into a six-dimension, 19-question checklist, converting moats, cash flow, valuation, governance, and sentiment into a repeatable pre-purchase self-check.
How it works
It runs a fixed pipeline:
- Timestamp: uses PowerShell to capture the local time for the report date.
- Report generation: calls scripts/generate_checklist_html.py and writes scripts/output/Guijiang_Value_Investing_Decision_Checklist_YYYYMMDD.html.
- Email delivery: calls scripts/send_checklist_email.py to send the HTML body to hncjit@qq.com, with a date-formatted subject.
- Presentation: uses a conservative email-friendly palette, max width 900px, and a readable layout for mobile.
Caveats
This is a checklist and file-delivery workflow, not real-time market analysis, financial statement parsing, or investment advice. It depends on local Python scripts, a saved email_config.py, and QQ SMTP settings; paths, SMTP authorization, or recipient changes require environment updates.
Use Cases
- Before buying, compare moat, cash flow, valuation, and governance against a reviewable checklist.
- Compile the six-dimension Guijiang questions into a formal HTML report and email it for records.
- During post-trade review, test whether the buy thesis is independent of market consensus.
- Read the dark blue and gold HTML report on mobile and scan the 19 questions with ideal answers.
Best For
- Individual investors who prefer value investing and need structured pre-purchase self-checks.
- Independent researchers who need to document investment judgments into reviewable reports.
- Operations staff or assistants who must email a fixed checklist to a set recipient.
- Investors who prefer reading HTML email reports instead of opening long local documents.
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