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Aris Analyze Results | Skills Pool

技能檔案

Aris 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.

OpenLAIR882 星標2026年4月8日

職業
分類: 實驗室工具

技能內容

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)

相關技能

快速安裝

Aris Analyze Results

npx skillvault add OpenLAIR/openlair-dr-claw-skills-aris-analyze-results-skill-md

下載 Skill 打開源碼倉庫

作者: OpenLAIR
星標: 882
更新時間: 2026年4月8日
職業

本頁內容

01Analyze Experiment Results 02

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)

Workflow

03Step 1: Locate Results

04Step 2: Build Comparison Table

05Step 3: Statistical Analysis

06Step 4: Generate Insights

07Step 5: Update Documentation

08Output Format

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