robotics-code-generator
Generates clean, runnable ROS 2, Gazebo, Isaac Sim, and VLA code for humanoid robotics
Generates clean, runnable ROS 2, Gazebo, Isaac Sim, and VLA code for humanoid robotics
Tier escalation and human override cascade protocol for AI-to-Human governance bridge.
Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.
Create new Agent Skills with the proper directory structure and SKILL.md format. Use when the user wants to create a new skill or learn how to make one.
Recognize and mitigate context failures including lost-in-middle, context poisoning, distraction, confusion, and clash.
ใณใผใใฃใณใฐ็จใใญใณใใใๆ้ฉๅใปๆทปๅใใในใญใซใ ไปฅไธใฎ็ถๆณใงไฝฟ็จ: (1) ใฆใผใถใผใใใใฎใใญใณใใใๆนๅใใฆใใใใญใณใใใๆ้ฉๅใใฆใใจไพ้ ผใใๆ (2) ใฆใผใถใผใๆ็คบ็ใซใ/coding-prompt-optimizerใใๅฎ่กใใๆ (3) AIๅใใใญใณใใใฎๅ่ณชๆนๅใๆฑใใใใๆ
Elite context engineering specialist for AI agents - token optimization, degradation patterns, compression, memory systems, multi-agent coordination, vector databases, knowledge graphs, RAG systems, and enterprise context management. Use PROACTIVELY for complex AI orchestration, agent design, debugging context failures, or building LLM pipelines.
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems.
Finetuning Foundation Models - when to finetune, LoRA, QLoRA, PEFT techniques, memory optimization, model merging. Use when adapting models to specific domains, reducing costs, or improving performance.
Craft effective prompts for Gemini image generation - MUST READ before any generate_image call
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Guide for creating or updating Agent Skills using the standardized SKILL.md specification, including naming rules, optional frontmatter fields, validation, packaging, and best practices for structuring scripts, references, and assets.
Long-term memory system for Claude Code using HelixDB graph-vector database. Store and retrieve facts, preferences, context, and relationships across sessions using semantic search, reasoning chains, and time-window filtering.
Long-term memory system for Claude Code using HelixDB graph-vector database. Store and retrieve facts, preferences, context, and relationships across sessions using semantic search, reasoning chains, and time-window filtering.
Execute the Recursive Thought Committee (RTC) protocol by generating and harmonizing inputs from 5 specialized cognitive personas.
Orchestrate gummy-agents across distributed network using 'd' command for load-balanced, multi-host AI development
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
Setup the Ralph autonomous AI coding loop - ships features while you sleep
This skill should be used when the user asks to "do something simple", "perform basic task", or needs minimal guidance. Demonstrates the simplest possible skill structure.
Apply optimization techniques to extend effective context capacity. Use when context limits constrain agent performance, optimizing for cost or latency, implementing long-running agent systems, agents exhaust memory, or when designing conversation summarization strategies.
Build production-grade agentic workflows with LangGraph using graph-based orchestration, state machines, human-in-the-loop, and advanced control flow
This skill should be used when the user asks to "generate an image", "create a banner", "make artwork", "create an illustration", "generate a logo", "make a graphic", "design a header", "AI art", "img2img", or needs AI image generation. Handles prompt rewriting and Gemini 3 Pro image generation API calls.
Understanding Foundation Models - architecture, sampling parameters, structured outputs, post-training. Use when configuring LLM generation, selecting models, or understanding model behavior.
Orchestrates a dual-AI engineering loop where Claude Code plans and implements, while Codex validates and reviews, with continuous feedback for optimal code quality