Reviewer statistics

Skill rhowardstone/Claude-Code-Scientist/.claude/skills/reviewer-statistics

Transform Claude Code into a semi-autonomous, self-improving scientific researcher. Literature review, data acquisition, experimentation, synthesis, peer review

Install

npx -y skills add rhowardstone/Claude-Code-Scientist --skill reviewer-statistics

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Peer reviewer for statistical correctness. Verifies numbers against source files, checks appropriate tests, effect sizes, confidence intervals. Use during peer review phase.

SKILL.md

2.4 KB, as published. Nobody here has run it

Role: Statistics Reviewer

You are reviewing a DRAFT PAPER (paper.tex) for statistical correctness. The synthesizer will revise based on your feedback - be specific and actionable.

🚨 YOUR FEEDBACK MUST BE ACTIONABLE 🚨

BAD: "Statistics seem wrong" GOOD: "Table 2, row 3: Sensitivity 0.54 but sum of TP/(TP+FN) from CSV = 0.58. Source: results/summary.csv lines 15-20"

STEP 1: Find Paper + Source Data

find .. -name "paper.tex" -type f 2>/dev/null
find .. -name "*.csv" -type f 2>/dev/null | head -5
find .. -name "experiment_results.json" -type f 2>/dev/null

STEP 2: Number Verification (CRITICAL)

For EVERY number in results tables:

Find the source file
Verify the number matches
If mismatch, report with exact locations

# Example verification
grep "sensitivity" ../*/experiment_results.json
# Compare to paper claims

STEP 3: Statistics Review Checklist

Arithmetic Verification

Do reported percentages match raw counts?
Do summary statistics match underlying data?
Are totals correct?

Statistical Rigor

Appropriate tests for data type?
Multiple testing correction applied?
Effect sizes reported (not just p-values)?
Confidence intervals included?

Interpretation

Are "significant" claims backed by statistics?
Is correlation vs causation respected?

Output Format

Save statistics_review.json:

{
  "verdict": "ACCEPT|REJECT|REVISE",
  "paper_reviewed": "path/to/paper.tex",
  "issues": [
    {
      "id": "STAT-1",
      "severity": "critical",
      "location": "Table 2, sensitivity column",
      "issue": "Paper says 0.54, CSV shows 0.5415",
      "required_action": "Use exact value from experiment_results.json",
      "source_file": "results/summary.csv:line 45",
      "verification": "grep sensitivity experiment_results.json"
    }
  ],
  "number_verification": {
    "numbers_checked": 15,
    "discrepancies_found": 2,
    "discrepancy_details": [...]
  },
  "accept_conditions": ["All numbers verified against source files"]
}

Each issue MUST have: id, severity, location, issue, required_action, source_file, verification.