skills

Look up any arxiv paper on alphaxiv.org to get a structured AI-generated overview. Use this skill whenever the user shares an arxiv URL (arxiv.org/abs/...), an arxiv paper ID (e.g. 2401.12345), an alphaxiv URL, or asks you to explain, summarize, or analyze a research paper by ID or link. This is faster and more reliable than reading a raw PDF. Trigger proactively even when the user says things like "can you read this paper", "summarize this arxiv link", or "what does this paper say".

Use this skill whenever you need to elicit context, preferences, constraints, or choices from the user before proceeding. This is the default mechanism for all user-facing elicitation. The skill picks the best available tool, choosing a rich inline form (with cards, free-text, file uploads, multi-select, dates, sliders, and multi-round flows) when the visualizer is loaded, falling back to ask_user_input_v0 for choice questions, and prose for everything else. Trigger this any time you would otherwise ask a clarifying question in prose, call ask_user_input_v0 directly, or write a numbered list of questions for the user. This includes requests where intent is underspecified, decisions between options, gathering preferences for a recommendation, confirming assumptions before producing a deliverable, or collecting form-like data. Replaces ask_user_input_v0 as the default elicitation mechanism.

Build Python workflows and agents using the Claude Agent SDK (claude-agent-sdk). Use this skill whenever the user wants to create, design, or debug agent workflows, multi-agent systems, automation scripts, or any Python code that uses the Claude Agent SDK (formerly Claude Code SDK). Trigger on mentions of: "agent sdk", "claude-agent-sdk", "claude_agent_sdk", "query()", "ClaudeSDKClient", "subagents", "AgentDefinition", "agent workflow", "multi-agent", "claude code sdk", or any request to build an autonomous agent that reads files, runs commands, edits code, or orchestrates tasks programmatically. Also trigger when the user asks about architecting agent systems, choosing between SDK patterns, or integrating custom tools/hooks/MCP servers with the Agent SDK. Even if the user just says "build me an agent" or "automate this with Claude", use this skill.

Help the user understand and internalize complex topics through a structured pedagogical approach calibrated to how they learn. Triggers on "explain X to me", "help me understand Y", "what is Z and why does it matter", "break down this concept", "teach me about W", "how does X work", "walk me through Y", "I keep hearing about X but do not really get it", "what is the intuition behind X", or any request where the user wants to build a mental model of something they do not yet understand. Also trigger when the user asks conceptual "why" questions about systems, architectures, or paradigms. Do NOT trigger on simple factual lookups ("when was X founded"), summarization requests, problem-solving or decision-making (use the solve skill), or requests where the user already understands the topic and wants information organized.

Multi-pass self-review of work Claude just produced or modified, looking for bugs, errors, inconsistencies, and confusion. Trigger whenever the user says "fresh eyes review", "read carefully over what you just wrote", "look for any obvious bugs/errors/issues/problems", "review what you just modified", or "critically examine" what was just produced. Also trigger on prompts asking Claude to review work for typos, ambiguous wording, missing context, conflicting information, output-format inconsistencies, or logical/structural bugs. Also use proactively after producing a substantial deliverable (code file, document, slide deck, skill, spec, prompt). The skill runs a structured sequence of focused passes (surface, logic, cross-reference, regression) rather than one generic pass, because LLM self-critique misses different categories of issues on different passes. Do not use for trivial outputs like a one-sentence answer or a single-line edit.