abstract

Generate LEARNINGS.md from skill execution logs.

Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.

Harden skills against rationalization and bypass behaviors

Generate context optimization report for skill directories

Create slash commands with brainstorming and best practices

Create hooks with brainstorming and security-first design

Scaffold new Claude Code skills with brainstorming, TDD methodology, and proper frontmatter and module structure.

Manually evaluate a recent skill execution for qualitative feedback.

Evaluate all hooks in a plugin for quality and compliance

Automatically identify and implement skill improvements based on execution logs and user evaluations.

Analyze and enhance Makefiles for complete functionality coverage with auto-generation capability

Tiered plugin quality review: branch (quick gates), pr (quality scoring), release (full ecosystem audit). Detects affected plugins from git diff and reviews related plugins for side effects.

Check GitHub Discussions for highly-voted learnings and promote them to Issues.

Evaluate Claude Code rules in .claude/rules/ directories for quality

Audit skill quality, frontmatter compliance, token efficiency, and activation reliability. Recommends improvements.

Test Claude Code skills using RED/GREEN/REFACTOR TDD phases in fresh subagents to prevent priming bias.

Validate hooks for security, performance, and SDK compliance

Validate plugin structure, schema, and naming. Use for plugin creation, debugging, or verification.

Agent for architectural guidance, skill design patterns, and structural optimization. Provides consultation on modularization, token management, and dependency design.

Validates Claude Code plugin structure against official requirements - checks plugin.json schema, verifies referenced paths exist, validates kebab-case naming, and validates skill frontmatter is complete.

Agent for detailed skill quality auditing and improvement recommendations. Analyzes skill structure, content quality, token efficiency, activation reliability, and tool integration.

Facilitates human-in-loop qualitative evaluation of skill executions.

Implements skill improvements based on observability data from LEARNINGS.md. Prioritizes by frequency × impact / ease, generates proposals, validates changes. Enhanced with Hyperagents patterns: consults PerformanceTracker for trend data and ImprovementMemory for causal hypotheses before proposing changes.

Assess whether to escalate models. Use when evaluating reasoning depth.

Guide for creating Claude Code hooks with security-first design. Use for validation, logging, and policy enforcement.

Evaluate hook security, performance, and SDK compliance. Use for audits.

Analyze and improve the improvement process. Use for detecting quality regressions and refining meta-optimization.

Surface expert frameworks and methodologies. Use when: creating or evaluating skills, hooks, or agents.

Build composable skill modules with hub-and-spoke loading. Use when: token budget is tight, complexity is high.

Review plugin quality with tiered checks and dependency-aware scoping. Use for PR validation and pre-release audits.

Evaluate and validate Claude Code rules in .claude/rules/ directories. Use for frontmatter, glob patterns, and quality audits.

Reference reusable patterns for validation, error handling, and test scaffolding. Use when: ensuring consistency across skills.

Guide to creating Claude Code skills using TDD methodology and persuasion principles. Use for new skill development.

Evaluate and improve Claude skill quality through auditing. Use when reviewing, preparing for production, or auditing skills.

Test skills via RED/GREEN/REFACTOR TDD with fresh subagents. Use when: validating skill behavior, preventing priming bias.

5 hooks across 4 events