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analyze-results

analyze-results

github.com/wanshuiyinData & databases18.2k555 installsUpdated 4d ago
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About analyze-results

A research skill for analyzing experiment results stored in JSON or CSV files. It organizes comparisons, checks statistical patterns, and turns observations into findings and suggested follow-up experiments.

Key features

  1. 01Locates JSON and CSV results
  2. 02Builds variable comparison tables
  3. 03Computes relative change from baseline
  4. 04Summarizes seed means and variation
  5. 05Identifies trends and outliers
  6. 06Suggests follow-up experiments

Use cases

  1. 01Comparing model or hyperparameter runs
  2. 02Checking reproducibility across seeds
  3. 03Investigating unexpected results

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill analyze-results

After installing, type/analyze-resultsin Claude Code to confirm it loaded.

Details

From the project README

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:

  1. Observation: what the data shows (with numbers)
  2. Interpretation: why this might be happening
  3. Implication: what this means for the research question
  4. 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:

  1. Raw data table
  2. Key findings (numbered, concise)
  3. Suggested next experiments (if any)

Comments

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