# Counter Evidence

> Search for evidence that contradicts the investment thesis using independent sources

- Skill: `huskydanny/counter-evidence` (Agent Skill)
- Install (CLI): `npx skillmds@latest add huskydanny/counter-evidence`
- Raw SKILL.md: https://api.skillmd.com/api/skills/huskydanny/counter-evidence/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: HuskyDanny (https://skillmd.com/u/huskydanny)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/huskydanny/counter-evidence

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## Counter Evidence Workflow

OBJECTIVE: Find legitimate reasons why the thesis might be wrong, using INDEPENDENT data sources.

### Step 1: Identify Thesis Pillars
What are the key bullish arguments?
- Growth assumptions (revenue, earnings, market share)
- Competitive advantages (moat, technology, brand)
- Market opportunity claims (TAM, growth rate)
- Management quality assertions
- Valuation justification

### Step 2: Targeted Counter-Search
For each pillar, actively look for contradictions:
- Use `search_web_exa` with bearish keywords:
  - "[company] risks", "[company] challenges", "[company] lawsuit"
  - "[company] competition losing", "[company] regulatory action"
  - "[company] analyst downgrade", "[company] SEC investigation"
- Use `fetch_yfinance_news` to check:
  - Whether financial stats match the thesis claims
  - Recent news headlines that contradict the bullish narrative
  - Key stats (PE ratio, growth rates) vs thesis assumptions

### Step 3: Assess Severity
Rate each counter-evidence found:
- MINOR: Doesn't invalidate thesis, manageable risk
- MODERATE: Reduces conviction, needs monitoring
- MAJOR: Potentially invalidates key thesis pillar
- CRITICAL: Fundamental flaw, thesis may be wrong

### Output Format
COUNTER EVIDENCE REPORT: [SYMBOL]

THESIS PILLAR: [Growth assumption]
Counter-Evidence: [What was found from independent sources]
Severity: [MINOR/MODERATE/MAJOR/CRITICAL]
Implication: [What this means for the thesis]

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[Repeat for each pillar]
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OVERALL ASSESSMENT:
Thesis Vulnerability: [LOW/MODERATE/HIGH]
Most Concerning Finding: [Single biggest issue]