AI Company Investment Analysis Framework
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Please install @user_cf06cb04/ai-investor-harness following https://skillhub.cn/install/skillhub.md.
About this skill
Problem
When analyzing AI companies, the usual failure mode is not lack of information but weak research discipline: answers based on intuition, news treated as evidence, only bull cases examined, and missing verifiable data sources. This skill applies professional investment-research constraints to an AI agent, requiring it to identify intent, fetch data, label evidence levels, and run a red-team review before concluding.
How It Works
It routes requests to different analysis modules:
- Company Analysis Expert: decomposes revenue drivers, profit elasticity, and moats, using references/company-analyst.md.
- Risk Management Expert: challenges bullish assumptions, identifies falsification points and hard sell triggers, using references/risk-manager.md.
- Earnings Outlook Expert: focuses on expectations gap, guidance signals, and sensitive variables, using references/earnings-expert.md.
- Market Observation Expert: combines chart review, capital flow, and fundamentals consistency, using references/market-watcher.md.
- Thesis Building Expert: converges vague directions into testable investment theses, using references/thesis-builder.md.
The workflow starts with intent recognition, then selects a module, reads the matching framework, runs Preflight data collection, and produces structured output. It closes by listing missing materials and next actions. Core rules include no data no conclusion, evidence labels, and mandatory Red Team review.
Limits
This skill is useful for research support, thesis structuring, and risk checking, not as direct trading advice. It depends on available external data sources such as financial APIs, market data, news, or search. Without data, the output remains a logic framework. It does not predict prices or replace independent judgment.
Use Cases
- Review an AI company's moat by breaking down revenue sources, cost structure, and competitive barriers.
- Before earnings season, test performance assumptions and list guidance signals, expectation gaps, and sensitive variables.
- Run a red-team review on a bullish thesis to find fragile assumptions, falsification points, and sell triggers.
- Check chart patterns, capital flow, and fundamentals alignment to assess entry conditions after market review.
Best For
- Independent investors researching AI companies who want disciplined company analysis, risk checks, and earnings prep.
- Research analysts using AI assistance who need to turn vague themes into verifiable investment theses with evidence labels.
- Fundamental or quant researchers tracking AI stocks who want to align chart signals, capital flow, and fundamentals.
- Product managers building decision-support workflows who need the agent to fetch data before producing structured research.
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