Total 31,233 skills, AI & Machine Learning has 5049 skills
Showing 12 of 5049 skills
Analyzes errors, searches past solutions in memory, provides immediate fixes with code examples, and saves solutions for future reference. Use when user says "debug this", "fix this error", "why is this failing", or when error messages appear like TypeError, ECONNREFUSED, CORS, 404, 500, etc.
Scans all skill directories in the repository to generate a comprehensive global map of agent capabilities, inputs, and outputs. Use when you need to understand the full potential of your agent library or when a master agent needs to decide which sub-agent skill to invoke for a complex task.
Identifies and manages execution dependencies between agent skills by analyzing their inputs and outputs. Use when building multi-step agent workflows to ensure skills are executed in the correct order and that all required data is available.
Synchronize work between Antigravity and Claude Code agents
Use when coordinating complex tasks with orchestration, delegation, or parallel workstreams - provides structured workflows for orchestrate:brainstorm, orchestrate:spawn, and orchestrate:task.
Interact with Google Contacts to search and create contacts.
Optimize, rewrite, and evaluate prompts using the Anthropic 1P interactive prompt-engineering tutorial patterns (clear/direct instructions, role prompting, XML-tag separation, output formatting + prefilling, step-by-step “precognition”, few-shot examples, hallucination reduction, complex prompt templates, prompt chaining, and tool-use XML formats). Use for 提示词优化/Prompt优化/Prompt engineering, rewriting system+user prompts, enforcing structured outputs (XML/JSON), reducing hallucinations, building multi-step prompt templates, adding few-shot examples, or designing prompt-chaining/tool-calling scaffolds.
Build comprehensive AI-native brand asset systems that maintain consistency across all AI-generated content. Train AI tools on brand guidelines, create reusable prompt libraries, and manage visual/voice assets at scale. Use when ", " mentioned.
Best practices for LLM-assisted coding. Declarative workflows, simplicity, tenacity.
Capture conversation lessons as permanent reusable skill files.
Manage agent memory through daily logs, session preservation, and knowledge extraction. Use when (1) logging work at end of day, (2) preserving context before /new or /reset, (3) extracting patterns from daily logs to MEMORY.md, (4) searching past decisions and learnings, (5) organizing knowledge for long-term retention. Essential for continuous improvement and avoiding repeated mistakes.
Activates when the user asks about Agent Skills, wants to find reusable AI capabilities, needs to install skills, or mentions skills for Claude. Use for discovering, retrieving, and installing skills.