stock research and ranking agent

    1

    A structured research framework for screening and ranking stocks using weighted financial and technical dimensions.

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    stock 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

    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

    CandidateGrowth (15%)Profit (15%)FinStr (15%)Val (15%)Tech (10%)Cat (10%)Comp (10%)Risk (10%)
    NVDA55515553
    AMD43424433
    INTC22242332

    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

    RankTickerRaw WeightedComposite (0-100)
    1NVDA4.2585
    2AMD3.4068
    3INTC2.5050

    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)

    RankTickerCompositeThesisKey RisksUncertainties
    1NVDA85High-growth leader with massive FCF generation.Valuation, GeopoliticalSupply chain constraints
    2AMD68Primary challenger in AI accelerator market.Execution, Market shareProduct yields
    3INTC50Turnaround play focused on foundry services.Balance sheet, Technology lagFoundry 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.

    Connects securely to your tools. The creator never sees your data.

    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.

    Requires user-provided data sources or filings. No real-time data API integration. Limited to research and ranking, not execution or advisory.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed2 months ago

    What's inside

    Frequently Asked Questions

    The agent uses a proprietary eight-dimension framework—including growth, valuation, and technical context—to assign a normalized 0-100 score to each ticker, allowing for objective side-by-side comparisons.

    This skill is a prompt-engineered framework compatible with major LLMs like GPT-4, Claude 3, and Gemini; it is delivered as a structured template that can be integrated into your preferred AI workspace.

    No, this skill is designed for deep analytical processing rather than live data scraping. You provide the tickers or financial data points, and the agent applies its weighting logic to rank them based on your input.

    You receive the complete ranking framework, the weighted scoring rubric, specific input templates for data entry, and a guide on how to interpret the resulting composite scores.

    Buyers receive lifetime access to the current version of the framework, including any documentation updates or refinement to the scoring anchors released by the creator.

    Absolutely. While the core logic is pre-set, you can easily adjust the weights of the eight dimensions (e.g., favoring growth over valuation) to align with your specific investment strategy.

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