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Found 137 Skills
Use when converting PDFs, DOCX, HTML, scanned papers, reports, proposals, tables, or figures into Markdown, text, extracted assets, or quality reports for an academic research repository.
Literature Scout — Responsible for multi-source literature retrieval, screening, and classification, and constructing literature matrices. Activated when assigned by research supervisors to collect literature. Conduct systematic literature retrieval using tools such as Exa, ArXiv API, Semantic Scholar, etc.
USE FOR web search. Returns ranked results with snippets, URLs, thumbnails. Supports freshness filters, SafeSearch, Goggles for custom ranking, pagination. Primary search endpoint.
Compile LaTeX papers to PDF with automatic error detection, chktex style checking, and citation/reference validation. Runs the full pdflatex + bibtex pipeline. Use when the user wants to compile a paper, fix compilation errors, or debug LaTeX.
Initialize Python Project (New or Fork). Use when the user wants to create a new production-ready Python/ML project structure, or fork and enhance an existing project. Uses uv for environment management.
Build a retrospective or forward-looking work timeline from git commits, project docs, user notes, or chat records, then output a Markdown and/or HTML report with a Gantt chart or timeline visualization. Use when the user wants to review past work across one or more projects, explain time allocation to a mentor, summarize what was done in a period, or plan the next phase with a timeline.
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
Use when migrating messy academic research repositories, downloaded archives, proposal folders, ad hoc notebooks, scripts, datasets, or paper assets into the standard research project structure.
Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
Write Related Work sections that compare and contrast prior work with your approach. Organize by theme, cite broadly, and explain how your work differs. Use when writing or improving the Related Work section of a paper.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.