Auto-claude-code-research-in-sleep analyze-results

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says \"analyze results\", \"compare\", or needs to interpret experimental data.

install

source · Clone the upstream repo

git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/skills-codex/analyze-results" ~/.claude/skills/wanshuiyin-auto-claude-code-research-in-sleep-analyze-results-1a5f79 && rm -rf "$T"

manifest: skills/skills-codex/analyze-results/SKILL.md

source content

Analyze Experiment Results

Analyze: $ARGUMENTS

Workflow

Step 1: Locate Results

Find all relevant JSON/CSV result files:

Check
```
figures/
```
,
```
results/
```
, or project-specific output directories
Parse JSON results into structured data

Step 2: Build Comparison Table

Organize results by:

Independent variables: model type, hyperparameters, data config
Dependent variables: primary metric (e.g., perplexity, accuracy, loss), secondary metrics
Delta vs baseline: always compute relative improvement

Step 3: Statistical Analysis

If multiple seeds: report mean +/- std, check reproducibility
If sweeping a parameter: identify trends (monotonic, U-shaped, plateau)
Flag outliers or suspicious results

Step 4: Generate Insights

For each finding, structure as:

Observation: what the data shows (with numbers)
Interpretation: why this might be happening
Implication: what this means for the research question
Next step: what experiment would test the interpretation

Step 5: Update Documentation

If findings are significant:

Propose updates to project notes or experiment reports
Draft a concise finding statement (1-2 sentences)

Output Format

Always include:

Raw data table
Key findings (numbered, concise)
Suggested next experiments (if any)