Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/wanshuiyin/auto-claude-code-research-in-sleep --skill analyze-resultsAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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)
