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CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture transforms scattered sessions into persistent, cross-agent learnings with confidence decay, anti-pattern learning,...
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CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture transforms scattered sessions into persistent, cross-agent learnings with confidence decay, anti-pattern learning,...
Background Agent Pings
Reference guide for Agentica multi-agent infrastructure APIs
Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.
Deterministic router for math cognitive stack - maps user intent to exact CLI commands
Maintain Claude Code memory hygiene by auditing, organizing, updating, and optimizing memory files in `.claude/memory/`. Use when users request memory cleanup, organization, updates, or want to reduce context p...
Refine prompts for Claude models (Opus, Sonnet, Haiku) using Anthropic's best practices. Use when preparing complex tasks for Claude.
Agent Context Isolation
Advanced DSPy modules including ReAct agents, tool calling, output refinement (BestOfN, Refine), and adapters. Use when building agent systems, integrating external tools, or implementing multi-step reasoning.
Provision, manage, and terminate RunPod GPU instances for LLM training. Use when user says "spin up GPU", "create RunPod instance", "terminate pod", "check GPU status", "provision training server", or needs clo...
MANDATORY tracking protocol for multi-model validation. Creates structured tracking tables BEFORE launching models, tracks progress during execution, and ensures complete results presentation. Use when running...
Interview-based system for creating specialized Claude Code agents. Use when users want to create a Claude Code agent, need help defining agent roles and responsibilities, mention "create an agent" or "build an...
Use when user asks where to put configuration, skills, or learnings, or discusses sharing config across projects. Provides decision criteria for the three-tier architecture (global ~/.claude, plugin, project-lo...
Automatically applies when building LLM applications. Ensures proper async patterns for LLM calls, streaming responses, token management, retry logic, and error handling.
Use when reviewing code or PRs in the llama-stack repository (llamastack/llama-stack on GitHub). Provides specialized knowledge about Llama Stack's architecture, testing patterns (recordings folders), backward...
Automatically applies when creating AI tool functions. Ensures proper schema design, input validation, error handling, context access, and comprehensive testing.
Use when the user is exploring possibilities, asking for ideas, or expressing uncertainty. Trigger cues include "ideas", "alternatives", "what could we do", "brainstorm", "different approaches", "I'm not sure y...
Use when diagnosing openai_harmony.HarmonyError or gpt-oss tool calling issues with vLLM. Identifies error sources (vLLM server vs client), maps specific error messages to known GitHub issues, and provides conf...
This skill provides knowledge about Claude tools (Prompts, Skills, Subagents, Commands, and MCPs). Use this skill when knowledge about Claude tools is needed, when the user mentions Claude tools or when the use...
Automatically applies when engineering prompts for LLMs. Ensures proper prompt structure, templates, few-shot examples, context management, and injection prevention.
Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking, and retrieval optimization.
Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.
Creates new Agent Skills with proper YAML frontmatter, progressive disclosure architecture, and best practices. Use when the user asks to create a new skill, generate a skill template, or build custom capabilit...
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.