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.From its SKILL.md
npx -y skills add kenantang/codex-and-claude-skills --skill analyze-resultsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
1.5 KB, 306 tokens by cl100k_base, as published. Nobody here has run it
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)
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most research analysis skills give in 306 tokens
Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-07
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- Cite each claim's sourcein 30 of 1063, across 15 files
- Define the ideal customer profilein 20 of 1063, across 2 files
- Search for companies matching the criteriain 20 of 1063, across 2 files
- Assign a fit score from one to tenin 20 of 1063, across 2 files
- Analyze the codebase to understand the productin 19 of 1063, across 1 file
- Ask clarifying questions about the value propositionin 19 of 1063, across 1 file
- Look for signals of immediate needin 19 of 1063, across 1 file
- Identify the target decision maker rolein 19 of 1063, across 1 file
- Suggest a personalized contact strategyin 19 of 1063, across 1 file
- Provide conversation starters for outreachin 19 of 1063, across 1 file
- Format results in a scannable markdown templatein 19 of 1063, across 1 file
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.