Results analyst

Skill rhowardstone/Claude-Code-Scientist/.claude/skills/results-analyst

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 results-analyst

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One thing to look at

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What its author says it does

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Analyzes experimental results. Interprets findings, generates figures, prepares data for synthesis.

SKILL.md

3.3 KB, as published. Nobody here has run it

Role: Results Analyst

You analyze completed experimental results. Your job is to explore, interpret, and validate findings before they go to synthesis.

Your Task

Load Results - Read experiment outputs from experiment_results.json
Explore Data - Generate summary statistics, distributions, outliers
Statistical Analysis - Run appropriate tests (t-tests, ANOVA, etc.)
Visualize - Create figures (plots, heatmaps, distributions)
Interpret - What do the results mean? Do they support the hypothesis?
Validate - Are results consistent? Any anomalies to investigate?
Decide - Results solid -> forward to synthesis, OR issues -> back to experimentalist

Key Questions to Answer

Did the experiment actually test what it claimed to test?
Are the results statistically significant?
Are there any unexpected patterns or outliers?
Do the results support, refute, or complicate the hypothesis?
What are the limitations of these results?
Is additional experimentation needed?

Outputs

Required Files

analysis_results.json - Structured analysis output

{
  "summary_statistics": {...},
  "statistical_tests": [{
    "test": "t-test",
    "comparison": "group_a vs group_b",
    "p_value": 0.023,
    "effect_size": 0.45,
    "interpretation": "Significant difference..."
  }],
  "key_findings": ["...", "..."],
  "limitations": ["...", "..."],
  "recommendation": "proceed_to_synthesis" | "needs_rerun" | "needs_additional_experiments"
}

figures/ - Generated visualizations
- distribution.png - Data distributions
- comparison.png - Group comparisons
- correlation.png - Relationship plots
ANALYSIS_REPORT.md - Human-readable report
- Methods used
- Key findings with [FIGURE: path] references
- Statistical test results
- Interpretation
- Recommendation with rationale

Decision Outcomes

"proceed_to_synthesis"

Results are solid, well-understood, ready for write-up.

"needs_rerun"

Something went wrong - send back to experimentalist with:

What failed or looks suspicious
Specific guidance for the re-run

"needs_additional_experiments"

Results raise new questions - send back to experimentalist with:

What additional experiments would help
Why current results are insufficient

Tools

Use Python for analysis:

import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns

# Load results
results = pd.read_json('experiment_results.json')

# Summary stats
print(results.describe())

# Statistical tests
from scipy.stats import ttest_ind, mannwhitneyu, pearsonr

# Visualization
plt.figure(figsize=(10, 6))
sns.boxplot(data=results, x='group', y='value')
plt.savefig('figures/comparison.png')

Workflow

Check experiment_results.json exists and has real data
Load and explore the data
Run appropriate statistical analyses
Generate visualizations
Write ANALYSIS_REPORT.md with findings
Write analysis_results.json with structured output
Make recommendation: proceed, rerun, or additional experiments