abstract

Meta-skills infrastructure for the plugin ecosystem - skill authoring, hook development, and quality evaluation.

Overview

The abstract plugin provides tools for building, evaluating, and maintaining Claude Code plugins. It’s the toolkit for plugin developers.

Installation

/plugin install abstract@claude-night-market

Skills

Commands

Agents

Hooks

Insight Engine

The insight engine transforms raw skill execution metrics into diverse findings posted to GitHub Discussions. Four trigger points feed a pluggable lens architecture through a deduplication registry.

Architecture

Stop Hook (lightweight) ──┐
Scheduled agent (deep) ───┤
/pr-review ───────────────┤
/code-refinement ─────────┘
        │
        v
  insight_analyzer.py
  (loads lenses, runs analysis)
        │
        v
  InsightRegistry
  (content-hash dedup, 30-day expiry)
        │
        v
  post_insights_to_discussions.py
  (posts to "Insights" category)

Lenses

Four built-in lightweight lenses run on every Stop hook:

LLM-augmented lenses (BugLens, OptimizationLens, ImprovementLens) run in the scheduled agent only.

Custom lenses drop into scripts/lenses/ and auto-discover via the LENS_META + analyze() convention.

Deduplication

Findings pass through four layers before posting:

1. Content hash: deterministic SHA-256 from type, skill, and summary prevents re-posting identical findings.
2. Snapshot diff: DeltaLens compares current metrics to the last snapshot and only surfaces changes.
3. Staleness expiry: hashes expire after 30 days so persistent problems resurface with fresh data.
4. Semantic dedup: Jaccard similarity against existing Discussions links related findings or skips near-duplicates.

Insight Types

See ADR 0007 for the GitHub Discussions integration design and the palace bridge for cross-plugin knowledge flow.

Self-Adapting System

A closed-loop system that monitors skill health and auto-triggers improvements:

1. homeostatic_monitor.py checks stability gap after each Skill invocation
2. Skills with gap > 0.3 are queued in improvement_queue.py
3. After 3+ flags, the skill-improver agent runs automatically
4. skill_versioning.py tracks changes via YAML frontmatter
5. rollback_reviewer.py creates GitHub issues if regressions are detected
6. experience_library.py stores successful trajectories for future context

Cross-plugin dependency: reads stability metrics from memory-palace’s .history.json.

Usage Examples

Create a New Skill

/create-skill

# Claude will:
# 1. Use brainstorming for idea refinement
# 2. Apply TDD methodology
# 3. Generate skill scaffold
# 4. Create tests

Evaluate Skill Quality

Skill(abstract:skills-eval)

# Scores skills on:
# - Token efficiency
# - Documentation quality
# - Trigger clarity
# - Modular structure

Validate Plugin Structure

/validate-plugin /path/to/my-plugin

# Checks:
# - plugin.json structure
# - Required files present
# - Skill format compliance
# - Command syntax

Best Practices

Skill Design

1. Single Responsibility: Each skill does one thing well
2. Clear Triggers: Include “Use when…” in descriptions
3. Token Efficiency: Keep skills under 2000 tokens
4. TodoWrite Integration: Output actionable items

Hook Security

1. No Secrets: Never log sensitive data
2. Fail Safe: Default to allowing operations
3. Minimal Scope: Request only needed permissions
4. Audit Trail: Log decisions for review
5. Agent-Aware (2.1.2+): SessionStart hooks receive agent_type to customize context

Superpowers Integration

When superpowers is installed:

Related Plugins

• leyline: Infrastructure patterns abstract builds on
• imbue: Review patterns for skill evaluation