text-categorization-improver

Text Categorization Algorithm Improvement Protocol

Overview

Systematic methodology for iteratively improving text categorization, scoring, or matching algorithms. Uses a 5-phase cycle: baseline measurement, qualitative LLM-as-judge evaluation, parallel experimentation across 3 git worktrees, benchmark-driven merge testing, and iteration until the target score is reached.

Prerequisites

• A text categorization/scoring algorithm with measurable output
• A benchmark dataset (gold standard or ground truth)
• A quantitative scoring script
• Git repository with worktree support

Instructions

1. Phase 1: Run quantitative benchmark to establish baseline score
2. Phase 2: Run qualitative LLM-as-judge evaluation on failure cases
3. Phase 3: Spawn 3 parallel researcher agents in separate git worktrees
4. Phase 4: Benchmark each worktree, merge the winner, verify no regressions
5. Phase 5: Repeat from Phase 1 until target score is reached

Checklist

Copy this checklist and track your progress through the protocol:

• Phase 1: Baseline Measurement
• Phase 2: Qualitative Evaluation (LLM-as-Judge)
• Phase 3: Parallel Experimentation (3 Worktrees)
• Phase 4: Benchmark & Merge Testing
• Phase 5: Next Iteration or Completion

Output

See Output, Error Handling, Examples & Resources for expected output formats.

Error Handling

See Output, Error Handling, Examples & Resources for error handling details.

Examples

Input: Scoring algorithm achieving 65% accuracy on benchmark dataset Output: After 3 improvement cycles, accuracy reaches 82%+ with documented changes per iteration

See Output, Error Handling, Examples & Resources for detailed usage examples.

Resources

See Output, Error Handling, Examples & Resources for additional resources.

Reference Documentation

• Model Requirements
  • Model tier table for cost-efficient agent roles
• Phase 1: Baseline Measurement
  • Step 1.1: Run Quantitative Benchmark
  • Step 1.2: Initialize Progress Ledger
• Phase 2: Qualitative Evaluation
  • Step 2.1: Generate Evaluation Samples
  • Step 2.2: Spawn Evaluator Agents (Opus)
  • Step 2.3: Aggregate Findings (Opus)
  • Step 2.4: Update Progress Ledger
• Phase 3: Parallel Experimentation
  • Step 3.1: Create 3 Git Worktrees
  • Step 3.2: Assign Experiment Focus
  • Step 3.3: Launch Researcher Agents (Opus)
  • Step 3.4: Collect Results
• Phase 4: Benchmark & Merge
  • Step 4.1: Identify Best Performers
  • Step 4.2: Test Additivity
  • Step 4.3: Verify No Regressions
  • Step 4.4: Update Progress Ledger
• Phase 5: Iteration or Completion
  • Decision Gate
  • Iteration History
  • Final Merge
• Anti-Patterns
  • Common experimental anti-patterns to avoid
• Output, Errors, Examples & Resources
  • Output
  • Error Handling
  • Examples
  • Resources