stock research and ranking agent
A structured research framework for screening and ranking stocks using weighted financial and technical dimensions.
$5
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
GitHub Copilot
Gemini CLI+20 moreClaude CodeCursorCodex CLIGitHub CopilotGemini CLI
OpenClaw
Manus
Grok+17 agentsstock research and ranking agent
Example session with this skill installed
Research and rank NVDA, AMD, and INTC based on their latest 10-K filings and current market context. Focus on growth vs valuation tradeoffs.
- Read your context and instructions
- Compiled the stock research
- Generated the document
Stock Research Shortlist Report
Generated: 2024-05-20 | Data source: 2023 10-K Filings & Q1 2024 Guidance | Universe: 3 tickers
1. Dimension Scores per Candidate
| Candidate | Growth (15%) | Profit (15%) | FinStr (15%) | Val (15%) | Tech (10%) | Cat (10%) | Comp (10%) | Risk (10%) |
|---|---|---|---|---|---|---|---|---|
| NVDA | 5 | 5 | 5 | 1 | 5 | 5 | 5 | 3 |
| AMD | 4 | 3 | 4 | 2 | 4 | 4 | 3 | 3 |
| INTC | 2 | 2 | 2 | 4 | 2 | 3 | 3 | 2 |
Scoring Justification (Excerpts):
NVDA Growth (5): [DATA] 126% YoY revenue growth (FY24 10-K). [INTERPRETATION] Exceptional visibility due to Blackwell cycle.
- NVDA Valuation (1): [DATA] Forward P/E >40x. [INTERPRETATION] Extreme premium vs sector median.
INTC Financial Strength (2): [DATA] Significant CapEx for Foundry build-out impacting FCF. [INTERPRETATION] Elevated leverage risk during transition.
2. Composite Scores & Ranking
| Rank | Ticker | Raw Weighted | Composite (0-100) |
|---|---|---|---|
| 1 | NVDA | 4.25 | 85 |
| 2 | AMD | 3.40 | 68 |
| 3 | INTC | 2.50 | 50 |
3. Top 3 Tradeoff Analysis
NVDA (Score: 85)
- Strength: Dominant competitive moat and best-in-class margins.
- Weakness: Valuation provides zero margin for error.
- Tradeoff: Superior growth and cash flow offset extreme valuation multiples.
- ⚠ Uncertainties: Export control changes, hyperscaler concentration.
AMD (Score: 68)
- Strength: Strong technical momentum and AI roadmap expansion.
- Weakness: Lower profitability compared to lead incumbent.
- Tradeoff: More attractive entry point than NVDA but faces uphill market share battle.
- ⚠ Uncertainties: MI300X ramp speed, PC market recovery.
4. Shortlist (Top 3)
| Rank | Ticker | Composite | Thesis | Key Risks | Uncertainties |
|---|---|---|---|---|---|
| 1 | NVDA | 85 | High-growth leader with massive FCF generation. | Valuation, Geopolitical | Supply chain constraints |
| 2 | AMD | 68 | Primary challenger in AI accelerator market. | Execution, Market share | Product yields |
| 3 | INTC | 50 | Turnaround play focused on foundry services. | Balance sheet, Technology lag | Foundry customer wins |
5. Critical Caveats
- Research only, not investment advice.
- No guarantee of future returns or price movement.
- Analytical interpretations are subjective.
- Past performance does not indicate future results.
Next steps
- Verify recent technical breakout levels for NVDA on the daily chart.
- Compare AMD's R&D spend as a % of revenue against historical medians.
- Audit INTC's quarterly interest coverage ratio to monitor financial distress risk.
stock-research-and-ranking-agent.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Manual stock screening is prone to emotional bias, inconsistent weighting, and the conflation of verified financial data with speculative market sentiment. Most traders lack a repeatable framework to normalize disparate metrics like ROIC, debt-to-equity, and technical momentum into a single objective score.
What it does
- Evaluates stocks across eight weighted dimensions including growth, profitability, valuation, and technical context.
- Calculates a normalized composite score (0-100) based on quantitative evidence and defined scoring anchors.
- Generates a ranking table that prioritizes candidates by risk-adjusted financial strength.
- Surfaces material uncertainties and separates verified data points from analytical interpretations.
- Produces a research shortlist with specific thesis statements and identified risk factors.
Why this beats prompting it yourself
Standard LLM prompts often hallucinate price targets or give vague, generic advice. This skill enforces a rigid weighted scoring system and mandates specific evidence for each dimension, ensuring every ranking is backed by a visible audit trail of data rather than just probabilistic text generation.
Use cases
- Comparing industry peers during earnings season to identify relative strength.
- Filtering a watchlist to find stocks with strong technical setups but attractive valuations.
- Conducting due diligence on potential long-term holdings by stress-testing financial strength and moats.
- Auditing a portfolio to identify high-risk positions with deteriorating growth or excessive leverage.
Known limitations
Does not provide real-time pricing data or automated data retrieval. Scores are dependent on user-provided financial filings and tickers. Explicitly excludes price targets and buy/sell recommendations.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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