cross-task-learner

Cross-Task Learner Skill

Enable agent loops to learn from similar past tasks and share discovered patterns across multiple concurrent or sequential loops.

Research Foundation: REF-013 MetaGPT - 159% improvement with shared state

Version 2.0: Multi-loop awareness with loop_id tracking

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Overview

This skill provides two core capabilities:

1. Pattern Extraction - On loop completion, extract reusable patterns from execution history
2. Pattern Injection - On loop start, inject relevant patterns from previous loops

Benefits

Benefit	Impact
Faster resolution	Patterns eliminate redundant debugging
Higher success rates	Proven approaches applied automatically
Accumulated wisdom	System gets smarter over time
Anti-pattern detection	Failed approaches flagged and avoided

Research Basis

From REF-013 MetaGPT:

• 159% improvement with shared state across agents
• Publish-subscribe pattern enables knowledge sharing
• Structured outputs become inputs for other agents
• Memory persistence critical for cross-session learning

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Pattern Extraction (On Loop Completion)

Trigger

• Agent loop completion (success, partial, or failure)
• Manual extraction request via aiwg ralph-extract-patterns {loop_id}

Process

extraction_steps:
  1_analyze_loop_history:
    - Load loop state from .aiwg/ralph/loops/{loop_id}/state.json
    - Load iteration analytics
    - Load debug memory
    - Load reflection history

  2_identify_error_fix_pairs:
    - Scan iterations for test failures
    - Identify fixes that resolved errors
    - Extract error signature + fix approach
    - Compute initial success rate (1.0 for first occurrence)

  3_identify_successful_approaches:
    - Analyze task category (testing, debugging, refactoring, etc.)
    - Extract step sequence that led to success
    - Note tools used and iteration count
    - Identify preconditions and benefits

  4_identify_failure_patterns:
    - Detect repeated same errors (anti-patterns)
    - Note approaches that led to scope creep
    - Flag patterns that caused quality degradation
    - Record better alternatives if discovered

  5_extract_code_templates:
    - Identify successful code changes
    - Generalize with placeholders
    - Document use case and placeholders
    - Tag by language and purpose

  6_check_for_duplicates:
    - Compare against existing patterns in registry
    - Merge if >80% similar
    - Update usage count and success rate if duplicate

  7_store_in_registry:
    - Add to .aiwg/ralph/shared/patterns/{type}-patterns.json
    - Update patterns index for semantic search
    - Link to source loop_id

  8_update_effectiveness_metrics:
    - Increment pattern counts
    - Update cross-loop benefit statistics
    - Log extraction event