Stock Research Report & Thesis Builder Skill

Converting fragmented research into a coherent investment case is time-consuming and prone to bias. This skill acts as a senior equity research analyst, transforming raw notes, filings, and financial data into a structured, evidence-led investment thesis that withstands professional scrutiny.

What it does

• Classifies evidence by categorizing inputs into company-reported facts, analyst estimates, model assumptions, and unverified claims.
• Builds thesis pillars by linking specific claims to economic mechanisms, evidence sources, and invalidation conditions.
• Generates counter-theses by constructing the strongest reasonable opposing cases to test the fragility of your assumptions.
• Maps catalysts by identifying specific milestones, their expected timing, and their potential impact on share performance.
• Creates monitoring checklists that turn research conclusions into observable checkpoints with defined warning signals.
• Audits financial data to flag earnings not supported by cash flow, rising leverage, or inconsistent reporting periods.

How it works

1. Define scope by providing the company ticker, research date, and your central investment question.
2. Input data by uploading raw notes, annual reports, earnings transcripts, or valuation models.
3. Review analysis as the agent normalizes your notes and separates facts from interpretations across business and financial sections.
4. Refine thesis by reviewing the bull and bear scenarios, risk register, and valuation sensitivities.

Frameworks & tools

This skill utilizes standard equity research methodologies, including DCF, EV/EBITDA, and sum-of-the-parts valuation frameworks. It follows principles of traceability and evidence-based reporting similar to professional buy-side and sell-side standards.

Why this beats prompting it yourself

Generic prompts often produce "balanced" but vague summaries that lack rigor. This skill enforces a strict separation between facts and forecasts, preventing the AI from manufacturing data while ensuring that counterarguments are substantive rather than token objections.

Use cases

• Drafting research reports from messy meeting notes or fragmented investor presentations.
• Stress-testing a long position by generating a high-conviction bear case based on current filings.
• Onboarding new tickers by creating a structured overview and a list of open questions for management.

Known limitations

Does not provide personalized financial advice or buy/sell instructions. Requires user-supplied or browsed data to generate valuation conclusions; it cannot invent current market prices.