Ltk Core

Use when "codex", "use gpt", "gpt-5", "openai codex", "let openai", "full-auto", "autonomous code generation"

Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

Use when "nanobanana", "generate image", "create image", "edit image", "AI drawing", "Gemini image", "image generation"

Use when designing agent tools, creating tool descriptions, implementing MCP tools, or asking about "tool design", "agent tools", "tool descriptions", "MCP", "function calling", "tool consolidation"

Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"

Use when you have a spec or requirements for a multi-step task, before touching code

Use when "DSPy", "declarative prompting", "automatic prompt optimization", "Stanford NLP", or asking about "optimizing prompts", "prompt compilation", "modular LLM programming", "chain of thought", "few-shot learning"

Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Use when you have a written implementation plan to execute in a separate session with review checkpoints

Use when building "MCP server", "Model Context Protocol", creating "Claude tools", "MCP tools", or asking about "FastMCP", "MCP SDK", "tool development for LLMs", "external API integration for Claude"

Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"

Use when "LangChain", "LLM chains", "ReAct agents", "tool calling", or asking about "RAG pipelines", "conversation memory", "document QA", "agent tools", "LangSmith"

Use when creating Claude Code plugins, writing skills, building commands, developing agents, or asking about "plugin development", "create skill", "write command", "build agent", "SKILL.md", "plugin structure", "progressive disclosure"

Use when "writing prompts", "prompt optimization", "few-shot learning", "chain of thought", or asking about "RAG systems", "agent workflows", "LLM integration", "prompt templates"

Use when running /ltk:sync-submodules, updating submodules, or needing to "sync", "merge", "adapt", "learn from" other Claude Code plugins or repos

Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"

Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"

This skill should be used when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls. Implements Manus-style file-based planning with task_plan.md, findings.md, and progress.md.

Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"

Use when designing multi-agent systems, implementing supervisor patterns, coordinating multiple agents, or asking about "multi-agent", "supervisor pattern", "swarm", "agent handoffs", "orchestration", "parallel agents"

Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

Use when executing implementation plans with independent tasks in the current session