skill-improver

Skill Improver Agent

Automatically improves skills based on execution logs, user evaluations, and aggregated insights from LEARNINGS.md. Enhanced with Hyperagents (Zhang et al., 2026) patterns for data-driven improvement decisions.

Purpose

Part of Issue #69 Phase 5 - Self-Improvement Loop. This agent closes the observability loop by acting on insights gathered from:

• Phase 1: Execution logs (failure rates, duration)
• Phase 2: Qualitative evaluations (ratings, friction, suggestions)
• Phase 3: LEARNINGS.md aggregation (patterns, common issues)
• Phase 6: Hyperagents integration - PerformanceTracker trends, ImprovementMemory hypotheses, metacognitive self-modification

Inputs

• mode: all (default), skill:<name>, top:<N>, dry-run, or --metacognitive
• LEARNINGS.md path: ~/.claude/skills/LEARNINGS.md
• auto_implement: Boolean - automatically implement or prompt for confirmation

Workflow

0. Load Hyperagents data (before LEARNINGS.md)

Before loading LEARNINGS.md, consult the persistent improvement memory and performance tracker for context that should inform this improvement cycle.

from pathlib import Path

MEMORY_FILE = Path.home() / ".claude/skills/improvement_memory.json"
TRACKER_FILE = Path.home() / ".claude/skills/performance_history.json"

# Load improvement memory (if available)
improvement_context = {}
try:
    from abstract.improvement_memory import ImprovementMemory

    memory = ImprovementMemory(MEMORY_FILE)

    # Get strategies that worked and failed
    effective = memory.get_effective_strategies()
    failed = memory.get_failed_strategies()

    improvement_context = {
        "effective_strategies": effective,
        "failed_strategies": failed,
        "effectiveness_rate": (
            len(effective) / (len(effective) + len(failed))
            if (effective or failed)
            else None
        ),
    }
except ImportError:
    pass  # Module not available

# Load performance tracker (if available)
tracker_context = {}
try:
    from abstract.performance_tracker import PerformanceTracker

    tracker = PerformanceTracker(TRACKER_FILE)

    # Identify skills with degrading trends
    degrading_skills = []
    for entry in tracker.history:
        skill_ref = entry["skill_ref"]
        trend = tracker.get_improvement_trend(skill_ref)
        if trend is not None and trend < -0.05:
            degrading_skills.append(
                {
                    "skill": skill_ref,
                    "trend": trend,
                }
            )

    tracker_context = {
        "degrading_skills": degrading_skills,
        "best_performers": tracker.get_best_performers(top_k=5),
    }
except ImportError:
    pass  # Module not available