cancel-loop
Cancel the active Ralph implementation loop. Use when you want to stop the iterative process.
Explorez les skills IA publiés de Z-M-Huang et consultez leurs instructions source.
Skills indexés
15Étoiles des dépôts des skills indexés
196Forks du dépôt
62Moyenne des étoiles par skill indexé
13Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Cancel the active Ralph implementation loop. Use when you want to stop the iterative process.
Gather and clarify requirements before autonomous pipeline execution. Interactive phase for user input.
Deep code/plan review for architecture, subtle bugs, and test quality. Use after sonnet review for thorough analysis.
Start the multi-AI pipeline with TDD-driven ralph loop. Plan → Review → Implement (loop until tests pass + reviews approve).
Implement code using sonnet model with full main context access
Fast code/plan review for quality, security, and tests. Use for quick reviews before deeper analysis.
Final code/plan review using Codex. Use as the last review step after sonnet and opus.
Show memory system status and tracked projects. Use to check memory system health and list projects.
Repair common issues with the memory system. Cleans up orphan temp files, stale sessions, lock files, and old logs.
Two-phase cleanup - normalize long filenames, then consolidate semantic duplicates using AI
AI-powered validation of knowledge notes against current codebase. Detects stale content by comparing documented knowledge with actual files.
Fix issues detected by mem-audit. Supports dry-run mode to preview changes before applying them.
Search the Claude Code knowledge base for errors, solutions, decisions, and patterns. Use this to find information from your memory system.
Explicitly save knowledge, patterns, decisions, or learnings to the knowledge base. Use when you want to document something for future reference.
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.