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PythonReactSEOPDF

Z-M-Huang AI Skills

GitHub ↗

Explorez les skills IA publiés de Z-M-Huang et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

15

Étoiles des dépôts des skills indexés

196

Forks du dépôt

62

Moyenne des étoiles par skill indexé

13

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

15 skills

Explorer

GitHub

cancel-loop

Z-M-Huang

Cancel the active Ralph implementation loop. Use when you want to stop the iterative process.

GitHub

user-story

Z-M-Huang

Gather and clarify requirements before autonomous pipeline execution. Interactive phase for user input.

GitHub

review-opus

Z-M-Huang

Deep code/plan review for architecture, subtle bugs, and test quality. Use after sonnet review for thorough analysis.

GitHub

multi-ai

Z-M-Huang

Start the multi-AI pipeline with TDD-driven ralph loop. Plan → Review → Implement (loop until tests pass + reviews approve).

GitHub

review-sonnet

Z-M-Huang

Fast code/plan review for quality, security, and tests. Use for quick reviews before deeper analysis.

GitHub

mem-status

Z-M-Huang

Show memory system status and tracked projects. Use to check memory system health and list projects.

GitHub

mem-repair

Z-M-Huang

Repair common issues with the memory system. Cleans up orphan temp files, stale sessions, lock files, and old logs.

GitHub

mem-validate

Z-M-Huang

AI-powered validation of knowledge notes against current codebase. Detects stale content by comparing documented knowledge with actual files.

GitHub

mem-fix

Z-M-Huang

Fix issues detected by mem-audit. Supports dry-run mode to preview changes before applying them.

GitHub

mem-search

Z-M-Huang

Search the Claude Code knowledge base for errors, solutions, decisions, and patterns. Use this to find information from your memory system.

GitHub

mem-save

Z-M-Huang

Explicitly save knowledge, patterns, decisions, or learnings to the knowledge base. Use when you want to document something for future reference.

GitHub

mem-audit

Z-M-Huang

Scan the knowledge base for structural issues like broken links, orphan notes, missing indexes, and optionally AI-powered content staleness detection.

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.