Research Skill

Purpose

Read a research paper and extract actionable information needed to implement the proposed method. Record the findings as a structured JSON entry.

When to Use

Phase 2 — after the current implementation has been analyzed.

Input

experiments/{research_name}/

Actions

1. Read

   {experiment_path}/research.pdf

   and extract:
   • Method Summary: 2-3 short paragraphs describing what the paper proposes, what problem it solves, and how it differs from traditional approaches.
   • Pros: each advantage the paper claims or demonstrates.
   • Cons: stated or inferred limitations, assumptions, or weaknesses.
   • Implementation Requirements:
     • Required libraries/packages (with versions if specified)
     • Required data format or preprocessing
     • Required compute resources (GPU, memory, etc.)
     • Key hyperparameters to set
   • Compatibility Analysis:
     • Can the method use the same data as the current baseline?
     • Does it need different preprocessing?
     • Does it output comparable predictions (same format)?
     • Can the same metrics be used for comparison?

2. Append a Phase 2 entry to

   {experiment_path}/log.json

   under

   phases

   :

   {
     "name": "Phase 2: Research",
     "completed_at": "2026-04-17T10:30:00Z",
     "paper": {
       "title":   "CatBoost: Unbiased Boosting with Categorical Features",
       "authors": ["Prokhorenkova et al."],
       "method_summary": "CatBoost is a gradient-boosting framework that handles categorical features natively via ordered target statistics and uses oblivious decision trees to reduce overfitting."
     },
     "pros": [
       "Native categorical handling — no manual encoding needed",
       "Reduces target leakage with ordered boosting",
       "Strong out-of-the-box performance"
     ],
     "cons": [
       "Training slower than XGBoost for small data",
       "More memory intensive"
     ],
     "requirements": {
       "new_dependencies": ["catboost>=1.2"],
       "data_format": "pandas.DataFrame with categorical columns marked",
       "compute": "CPU is sufficient; GPU optional"
     },
     "compatibility": {
       "same_data":    true,
       "same_metrics": true,
       "preprocessing_notes": "CatBoost takes raw categorical columns; do NOT pre-encode them for the new notebook."
     }
   }
   

   Do not overwrite earlier entries; append to the

   phases

   array.

Output

• {experiment_path}/log.json

  — updated with Phase 2 research entry
• No other files created or modified