Axect

Generate Python matplotlib plotting scripts in the user's scienceplots lab style: with plt.style.context(["science", "nature"]), pparam = dict(...), raw-string LaTeX labels, and fig.savefig(..., dpi=300, bbox_inches='tight'). Support parquet, CSV, NPY/NPZ data and single-line, multi-line, scatter/errorbar, or subplot variants. Generate both the reproducible plotting script and the requested PNG/PDF/SVG figure by default. Use for publication-style plot scripts, science/nature figures, plot-from-data scaffolds, and Korean or English requests for matplotlib graph scripts.

Create rigorous teaching explanations as a single markdown document with equations, assumptions, derivations, plots, optional schematic prompts, and PDF export. Use when the user asks to explain a physics, math, ML, statistics, or CS concept; write lecture, tutorial, or seminar notes; derive results step by step; build a kind but rigorous walkthrough; or turn a paper section into classroom-ready material. Finished explanations are archived to ~/Dropbox/ConceptExplainer/<Topic>/<concept-slug>/ automatically, regardless of language.

Create or revise a markdown research or experiment report that makes the work UNDERSTANDABLE (why it matters, what the core idea is, what it achieved), backed by traceable evidence, integrated plots, optional literature support, plot manifest tracking, and version history, inside a single Harness without external Codex or Gemini calls. Use when you need to generate `report.md`, explain a result or method so a colleague can understand it, inventory or validate plots, create `plots/plot_manifest.json`, manage `report_versions.json`, connect report sections to supporting references, or turn an experiment or output directory into a reusable report workflow.

Produce a journal-club-style paper presentation (9 sections: TL;DR, Problem, Key Idea, How It Works, Key Results, Why It Matters, Strengths/Limitations/Open Questions, Discussion Questions, Takeaways) from an arXiv ID/URL, a PDF, raw text/markdown, or a local LaTeX source (.tex / project dir). Helps a reading group UNDERSTAND and DISCUSS the paper — not score or accept/reject it. Grounds every claim in the source, renders math as LaTeX, auto-matches the source language (Korean source -> Korean review). When LaTeX source is available (arXiv e-print or local .tex), embeds the paper's real figures with captions; optionally generates two friendly-whiteboard infographic figures via the bundled codex image_generation tool. Use when the user wants a journal-club review, paper walkthrough/presentation, or paper explainer, to "review this PDF/paper like a journal club", or 논문 저널클럽 리뷰/발표자료/논문 설명. For an OpenReview referee report use workshop-paper-review; for an adversarial pre-submission audit use adversarial-review.

Search the live web via z.ai web_search_prime (GLM Coding Plan MCP server). Use when the user wants current information, recent trends, news, blog posts, docs, or any web resource that academic-database search (reference-search) does not cover. Triggers on web search, 웹 검색, search the web, 최근 동향, 최신 트렌드, latest trends, find online, 인터넷에서 찾아, what's new, current state of X, search z.ai, GLM web search.

Generate a hand-drawn, whiteboard-sketch schematic that explains a concept, pipeline, architecture, algorithm, or mechanism at a glance, on a pure white background. Composes a friendly hand-lettered infographic prompt (wavy marker strokes, numbered panels flowing left to right, chunky chalk arrows, hand-written notes beside shapes) and by default renders it to a PNG via the bundled codex image_generation tool (ChatGPT OAuth); falls back to emitting the copy-paste prompt when codex is unavailable. Use when the user wants a hand-drawn / hand-written / whiteboard-style schematic, a sketch diagram that shows a schema, a friendly explainer figure, a doodle-style method overview, or 손그림/화이트보드 스타일 도식/개념도. For matplotlib data plots use scienceplot-py or xkcd-py; for a multi-style (editorial, blueprint, neon, poster) prompt-only composer use wide-slide-illustrator; for the paper-review method+results pair use journal-club-review.

Manage Vast.ai GPU cloud instances via the vastai CLI. Use this skill whenever the user mentions Vast.ai, GPU rentals, cloud GPU instances, searching for GPU offers, creating/destroying instances, vast.ai billing, or any task involving the vastai command-line tool. Also trigger when the user wants to rent GPUs, find cheap GPUs, deploy Docker containers on remote GPUs, manage remote training infrastructure, or transfer data to/from cloud GPU machines. Even if the user just says "spin up a GPU" or "find me an A100", this skill likely applies.

Fetch valid (still-open, deadline-not-passed) job postings from Academic Jobs Online (academicjobsonline.org, AJO) and the InspireHEP jobs board (inspirehep.net/jobs). Use when the user wants current academic openings: postdoc / faculty / PhD positions in physics, cosmology, HEP, ML, astrophysics or any field, filtered to postings whose application deadline has not passed. Searches both boards by default and merges the results. Manage field presets (keywords + position types + which boards), fetch and store valid postings, see what is new since last check, and inspect a posting's details. Triggers on: Academic Jobs Online, AJO, InspireHEP jobs, Inspire HEP 공고, academic job postings, postdoc openings, faculty positions, job listings, valid 공고, 학술 잡, 교수 공고, 포닥 공고, 채용 공고, 잡 마켓.

Migrate an existing project that uses pytorch_template to the latest version. Use this skill when the user says: "update template", "migrate", "upgrade", "apply latest changes", "sync with template", "update pytorch_template", or when the user's project is missing new features (preflight, hpo-report, data field, diagnostic callbacks).

Guide for training PyTorch models using this template's config-driven pipeline. Use this skill whenever the user wants to: train a model, create experiment configs (run.yaml / opt.yaml / best.yaml), run hyperparameter optimization (HPO) with Optuna, extract best parameters from an HPO study, set up a new experiment version, or scaffold a new model/data recipe. Triggers on: "train", "HPO", "optimize", "create config", "best params", "new experiment", "run training", "set up model", "preflight check", "HPO analysis".

Automatically convert arXiv papers to well-structured Markdown documentation. Invoke with an arXiv ID to fetch materials (LaTeX source or PDF), convert to Markdown, and generate implementation-ready reference documentation with preserved mathematics and section structure.

CLI tool (arxivterminal) for fetching, searching, and managing arXiv papers locally. Use when working with arXiv papers using the arxivterminal command - fetching new papers by category, searching the local database, viewing papers from specific dates, or managing the local paper database.

Convert Markdown reports to Typst format, compile to PDF with Korean support and publication-quality figures

Analyze disk usage with tonz

Upload files to Dropbox, download from Dropbox, and create or reuse shared links. Use when the user mentions Dropbox, asks to upload/download/share a file via Dropbox, or wants a shareable link for a file already in Dropbox.

Maintain a research log that acts as an advisor, not just a diary. Registers projects; records decisions with generalizable lesson extraction; saves session state; recalls cross-project lessons and prior solutions when stuck; checks proposed approaches against promoted anti-pattern rules; runs weekly review with lesson promotion and dashboard refresh. Use when the user asks to initialize a research-log project, record an experiment or decision, save session state, recall past lessons or how a blocker was solved before, check a planned approach against past mistakes, or review all projects.

Manage Vast.ai GPU cloud instances via the vastai CLI. Use this skill whenever the user mentions Vast.ai, GPU rentals, cloud GPU instances, searching for GPU offers, creating/destroying instances, vast.ai billing, or any task involving the vastai command-line tool. Also trigger when the user wants to rent GPUs, find cheap GPUs, deploy Docker containers on remote GPUs, manage remote training infrastructure, or transfer data to/from cloud GPU machines. Even if the user just says "spin up a GPU" or "find me an A100", this skill likely applies.

Generate publication-quality academic diagrams and statistical plots from text descriptions using the paperbanana CLI. Supports methodology diagrams, statistical plots, diagram evaluation, and iterative refinement with user feedback. Use when the user asks to: create a diagram, generate a figure, make a plot from data, evaluate diagram quality, refine/improve a generated diagram, or anything related to academic illustration generation. Triggers on: "diagram", "figure", "plot", "paperbanana", "academic illustration", "methodology diagram", "evaluate diagram", "refine diagram", "improve figure".