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Found 126 Skills
Use Chrome DevTools Protocol to allow the AI to "ask Gemini" or "research with Gemini" directly. This uses the user's logged-in Chrome session, bypassing API limits and leveraging the web interface's reasoning capabilities.
Guide for conducting thorough and synthesized research, focusing on verification, multi-source analysis, and RAG patterns.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
Full token research workflow using Messari x402 API. Fetches asset fundamentals, price history, sentiment signals, and news, then synthesizes a research brief via Messari AI. Total cost ~$1.00–$1.50 USDC per run.
Finalize an accepted ML or AI paper for camera-ready submission after reviews, rebuttal, and acceptance. Use this skill whenever the user has an accepted paper, camera-ready deadline, final revision, acceptance email, meta-review, rebuttal promises, author-response commitments, de-anonymization tasks, supplement updates, code links, acknowledgements, final LaTeX checks, or needs to ensure the accepted paper's claims, figures, references, and artifacts are consistent before final submission.
Simulate a tough but constructive peer review of an AI research artifact. Use when the user asks for a review, critique, feedback on a paper or draft, or wants to identify weaknesses before submission.
Enables Claude to conduct comprehensive research using Gemini Deep Research for in-depth analysis and reports
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
Technical research methodology using Context7, Exa, and Sequential Thinking for documentation, best practices, and complex investigations.
Deep research with cross-verification and source tiering. Use when investigating technologies, comparing tools, fact-checking claims, evaluating architectures, or any task requiring verified information. Triggers on "조사해줘", "리서치", "research", "investigate", "fact-check", "비교 분석", "검증해줘".
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).
Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.