Us Value Investing Framework OpenClaw Plugin & Skill | ClawHub
Looking to integrate Us Value Investing Framework into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate cli utilities tasks instantly, without having to write custom tools from scratch.
What this skill does
US stock valuation model skill (English-first + 中文) based on financial report data.
Install
openclaw skills install @spyfree/us-value-investing-frameworknpx clawhub@latest install us-value-investing-frameworkFull SKILL.md
Open originalSKILL.md content below is scrollable.
name: us-value-investing-framework description: US stock valuation model skill (English-first + 中文) based on financial report data. Use when you need to apply explicit rules: ROE > 15% for 3+ years, debt ratio < 50%, free cash flow > 80% of net income, moat assessment (brand/network effect/cost advantage), then output investment rating (A/B/C/D) with reasons.
US Stock Valuation Model - Value Investing Framework (EN + 中文)
This skill is an explicit rule-based value model focused on US stocks.
Input
Company financial report data (structured JSON), including:
- 3+ years of ROE
- Debt ratio
- Free cash flow and net income
- Moat assessment: brand / network effect / cost advantage
Use the bundled template: references/input-template.json.
Decision Rules (strict)
- ROE rule: ROE > 15% for at least 3 consecutive years
- Leverage rule: Debt ratio < 50%
- Cash conversion rule: Free cash flow > 80% of net income
- Moat rule: evaluate brand/network effect/cost advantage
Output
- Investment rating: A / B / C / D
- Reasons (pass/fail explanation per rule)
- Bilingual summary (EN main + 中文摘要)
Run
python3 scripts/evaluate_company.py \
--input references/input-template.json \
--out .state/eval.json \
--markdown .state/eval.md
Rating policy
- A: all 4 rules pass
- B: 3 rules pass
- C: 2 rules pass
- D: 0-1 rule pass
Resources
scripts/evaluate_company.py: deterministic evaluatorreferences/input-template.json: input schema example