Research skills

AI Marketing Suite for Claude Code. 15 marketing skills with parallel subagents — audit any website, generate copy, email sequences, ad campaigns, content calendars, competitive intelligence, and client-ready PDF…

Summarize deep research results into markdown report, cover all fields, skip uncertain values.

Add items (research objects) to existing research outline.

Add field definitions to existing research outline.

Read research outline, launch independent agent for each item for deep research. Disable task output.

Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.

Trade Polymarket prediction markets with AI — powered by PredictScope. Paper money with any initial amount, multiple custom strategies, controlled safe trading rule boundaries, real order books, real slippage, zero…

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting…

进行市场研究、竞争分析、投资者尽职调查和行业情报,附带来源归属和决策导向的摘要。适用于用户需要市场规模、竞争对手比较、基金研究、技术扫描或为商业决策提供信息的研究时。

Explore the codebase to understand a task before planning. Write findings to .agtx/research.md and stop. This is a read-only exploration — do not modify any files.

Non-user-invocable provider/setup reference for evo backend switching, prerequisite checks, and auth/install guidance.

Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current…

turns your codebase into an autoresearch loop — discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.

Protocol that evo optimization subagents follow when dispatched from /optimize. Auto-loaded by spawned subagents via their host's skill loader. The orchestrator may also invoke this skill to understand the brief shape…

Structure and write a literature review for any research topic. Use when asked to write a literature review, systematic review summary, narrative review, or research background section. Produces a structured review with…

Write a structured research protocol or study design document. Use when asked to write a research protocol, study protocol, research plan, methodology section, or research proposal. Produces a complete protocol with…

Create a structured UX research plan for any product question or feature. Use when asked to write a research plan, design a user study, create a discussion guide, write screener questions, or plan usability testing.…

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given…

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config,…

Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint…

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry…

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing…

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR…