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d-o-hub

episode-complete

by d-o-hub

A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.

3🍴 0📅 Jan 23, 2026

SKILL.md


name: episode-complete description: Complete and score a learning episode to extract patterns and update heuristics. Use when finalizing a task to enable pattern extraction and future learning.

Episode Complete

Complete and score a learning episode to extract patterns and update heuristics.

Purpose

Finalize an episode with outcome scoring, reflection generation, and pattern extraction for future retrieval.

Steps

  1. Gather outcome data:

    • Final verdict (success, partial_success, failure)
    • Total time spent
    • Total tokens used (if applicable)
    • Key artifacts produced
    • Errors encountered
  2. Create TaskOutcome:

    let outcome = TaskOutcome {
        verdict: Verdict::Success,
        time_ms: total_time,
        tokens: total_tokens,
        artifacts: vec![/* paths to created/modified files */],
        errors: vec![/* any errors encountered */],
    };
    
  3. Call complete_episode:

    memory.complete_episode(episode_id, outcome).await?;
    
  4. System processes:

    • Computes RewardScore based on:
      • Success/failure
      • Time efficiency
      • Code quality
    • Generates Reflection:
      • What worked well
      • What could be improved
      • Key learnings
    • Extracts Patterns:
      • Tool sequences
      • Decision points
      • Common pitfalls
  5. Update storage:

    • Store in Turso (permanent record)
    • Update redb cache
    • Index by task_type and timestamp
    • Update related patterns and heuristics
  6. Validation:

    • Verify episode was scored
    • Check patterns were extracted
    • Ensure heuristics were updated

Pattern Types Extracted

  • ToolSequence: Common tool usage patterns
  • DecisionPoint: Key decision moments and outcomes
  • ErrorPattern: Common errors and resolutions
  • PerformancePattern: Optimization opportunities

Scoring Rubric

  • Success: Task completed, tests pass, meets requirements
  • Partial Success: Task mostly complete, minor issues
  • Failure: Task incomplete, major issues, tests failing

Example

let outcome = TaskOutcome {
    verdict: Verdict::Success,
    time_ms: 45000,
    tokens: 12000,
    artifacts: vec![
        "src/storage/batch.rs".to_string(),
        "tests/integration/batch_test.rs".to_string(),
    ],
    errors: vec![],
};

memory.complete_episode(episode_id, outcome).await?;

Post-Completion

  • Patterns are now available for future retrieval
  • Heuristics updated for similar tasks
  • Episode stored for long-term learning
  • Embeddings computed (if service configured)

Score

Total Score

75/100

Based on repository quality metrics

SKILL.md

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0/15
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