Stats

Skill richard-kim-79/archora-skills/skills/stats

Academic research agent skills for Claude Code and other Agent Skills-compatible tools. Hypothesis generation, experiment design, paper drafting, peer review simulation, and more.

Install

npx -y skills add richard-kim-79/archora-skills --skill stats

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What its author says it does

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Detects statistical errors, logical fallacies, and methodological issues in research content. Checks for p-hacking, correlation/causation confusion, underpowered samples, multiple comparisons problems, overgeneralization, and other common fallacies. Use when the user asks to validate statistics, audit quantitative claims, check methodology, or find logical errors. Returns minimal output on purely theoretical content — most useful after empirical data is present.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Statistical Validator

Detect statistical errors and methodological fallacies in research content.

Fallacy Types Detected

Type	Description
`P_HACKING`	Selective reporting, post-hoc hypothesis changes, stopped when p<0.05
`CORRELATION_CAUSATION`	Causal claims from correlational data
`SMALL_SAMPLE`	Sample size insufficient for claimed effect size
`MULTIPLE_COMPARISONS`	Multiple tests without Bonferroni/FDR correction
`OVERGENERALIZATION`	Results from specific sample applied to broader population
`CIRCULAR_REASONING`	Conclusion assumes what it claims to prove
`CHERRY_PICKING`	Selective evidence presentation
`EFFECT_SIZE_MISSING`	Statistical significance without practical effect size
`CONFOUND`	Alternative explanations not controlled for

Severity Levels

HIGH — fatal flaw; invalidates the finding
MEDIUM — significant concern; finding is weakened
LOW — minor issue; addressable in discussion

Output Format

When issues are found:

# 📐 Statistical Validation

> Found **3 issue(s)** requiring attention.

## Issues

### 🔴 HIGH — P_HACKING

**Post:** [reference to source]
**Claim:** "[exact statistical claim]"
**Issue:** [specific explanation of the problem]
**Suggestion:** [concrete fix]

---

### 🟡 MEDIUM — CORRELATION_CAUSATION
...

## Summary
[Overall assessment + priority order for fixes]

When no issues are found:

# 📐 Statistical Validation

> ✅ No statistical issues detected.

## Assessment
[Explanation: e.g., "This content is theoretical/conceptual and contains no quantitative claims to validate."]

## Proactive Checklist
When empirical data is added, watch for:
- [ ] [Domain-specific statistical concern 1]
- [ ] [Domain-specific statistical concern 2]

When content has no statistics

If the content is purely theoretical or conceptual, note this explicitly and provide a domain-appropriate proactive checklist. Do NOT generate phantom issues.