Night Market Skill — ported from claude-night-market/abstract. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Metacognitive Self-Modification

Analyze the effectiveness of past skill improvements and refine the improvement process itself. This is the core innovation from the Hyperagents paper: not just improving skills, but improving HOW skills are improved.

Context Triggers (auto-invocation)

This skill should be invoked automatically when:

1. Regression detected: The homeostatic monitor finds a skill's evaluation window ended in pending_rollback_review status. The improvement made things worse -- we need to understand why.

2. Low effectiveness rate: When ImprovementMemory.get_effective_strategies() vs get_failed_strategies() shows effectiveness below 50%, the improvement process itself needs refinement.

3. Degradation despite improvements: When PerformanceTracker.get_improvement_trend() returns negative for a skill that was recently improved.

4. Periodic check: After every 10 improvement cycles (tracked via outcome count in ImprovementMemory).

Hook integration

The homeostatic monitor emits "improvement_triggered": true when a skill crosses the flag threshold. At that point, before dispatching the skill-improver, check if metacognitive analysis is warranted:

from abstract.improvement_memory import ImprovementMemory
from pathlib import Path

memory = ImprovementMemory(
    Path.home() / ".claude/skills/improvement_memory.json"
)

# Check if metacognitive analysis is warranted
effective = memory.get_effective_strategies()
failed = memory.get_failed_strategies()
total = len(effective) + len(failed)

needs_metacognition = False

# Trigger 1: Low effectiveness rate
if total >= 5 and len(effective) / total < 0.5:
    needs_metacognition = True

# Trigger 2: Periodic check (every 10 outcomes)
if total > 0 and total % 10 == 0:
    needs_metacognition = True

# Trigger 3: Recent regression
if failed and failed[-1].get("outcome_type") == "failure":
    needs_metacognition = True

if needs_metacognition:
    # Run metacognitive analysis before next improvement
    pass  # Skill(abstract:metacognitive-self-mod)

When To Use (Manual)

• After a batch of skill improvements to assess what worked
• When improvement outcomes show regressions
• Periodically (monthly) to refine improvement strategy
• When the skill-improver agent seems ineffective

When NOT To Use

• Routine skill improvements (use skill-improver directly)
• First-time skill creation (use skill-authoring)

Workflow

Step 1: Load improvement data

Read improvement memory and performance tracker data:

# Check for improvement memory
MEMORY_FILE=~/.claude/skills/improvement_memory.json
TRACKER_FILE=~/.claude/skills/performance_history.json