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概要と使いどころ
Research products via AI tools, cross-reference live stock, generate WooCommerce-ready copy, and extract marketing angles. Covers the full research-to-copy pipeline with remote_wp_connection stock checks and paper_store persistence. Use when researching products by SKU/name/category, generating WooCommerce descriptions, preparing product data for campaigns, or cross-referencing catalog files against live inventory.
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Search arXiv papers by keyword, author, category, or ID.
日本語の概要は準備中です。原文の説明を表示しています。
' - **Content:** The user provided a list of daily paper submissions with titles, authors, and some metadata. I need to extract the single most valuab
日本語の概要は準備中です。原文の説明を表示しています。
The paper 'When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation' revea
日本語の概要は準備中です。原文の説明を表示しています。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
日本語の概要は準備中です。原文の説明を表示しています。
Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories.
日本語の概要は準備中です。原文の説明を表示しています。
Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
日本語の概要は準備中です。原文の説明を表示しています。
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
日本語の概要は準備中です。原文の説明を表示しています。
Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.
日本語の概要は準備中です。原文の説明を表示しています。
Use when acting as the orchestrator agent that drives Syntropic137 - running the dogfood loop, deciding what to dispatch next, landing agent PRs, deploying a beta, triaging failed executions, or keeping the scratchpad, paper cuts and retrospectives current. Trigger phrases include "orchestrator tick", "keep the loop running", "what should we dispatch next", "land these PRs", "why are PRs stacking up", "deploy the next beta", "pit stop", "the run failed, what now", "write the retro", "is the system helping", "scale quality", "learning loop for the orchestrator". Do NOT use for deciding whether one task belongs in a workspace or locally (see `dogfooding`), for turning one incident into an eval case (see `learning-loop`), for designing a workflow A/B experiment (see `workflow-experimentation`), or for release-branch and version-bump mechanics (see `devops`).
日本語の概要は準備中です。原文の説明を表示しています。
Search arXiv papers by keyword, author, category, or ID.
日本語の概要は準備中です。原文の説明を表示しています。
変更内容を確認し、論理的な単位で複数のコミットに分けて実行します
Structure any communication artifact — documents, reports, plans, issue comments, PR descriptions, emails, presentations, slides, charts, and dashboards — using Jean-luc Doumont's "Trees, Maps, and Theorems" method. Use whenever the goal is to get a message across to a busy audience, when asked to improve clarity or structure of writing or slides, or when a deliverable will be judged by whether readers act on it, not just read it.
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Design, refine, implement, or review application UI and user journeys with minimal controls, clear actions, shared components, realistic prototypes, and polished behavior. Use for product screens, forms, onboarding, navigation, settings, and chat/task interfaces; not backend-only work or general graphic design.
日本語の概要は準備中です。原文の説明を表示しています。
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
日本語の概要は準備中です。原文の説明を表示しています。
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
日本語の概要は準備中です。原文の説明を表示しています。
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).
日本語の概要は準備中です。原文の説明を表示しています。
Fetch external documents linked in SCF submissions — Google Docs, Google Drive PDFs, GitHub, Notion, IPFS. Use when a submission links to an architecture doc, whitepaper, or technical spec hosted externally. Handles URL transformation for Google services.
日本語の概要は準備中です。原文の説明を表示しています。
Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature synthesis.
日本語の概要は準備中です。原文の説明を表示しています。
Add a new journal to the MedSci Skills profile database. Extracts metadata from author guidelines, generates write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates.
日本語の概要は準備中です。原文の説明を表示しています。
Detect articulation gaps in documents using 5W analysis and provide concrete improvement suggestions. Identifies what's missing in explanation, not what's wrong. Default runs detection + improvement; use --check-only for detection only. 旧名 check-articulation。
日本語の概要は準備中です。原文の説明を表示しています。
論文・研究概要の体裁・形式を自動チェック。LaTeX(.tex)・Word(.docx)・PDF(.pdf)に対応。表記ゆれ・参考文献体裁・相互参照整合性・未記入セクション等を検出。1〜2ページの概要・梗概・アブストラクト（固定尺）は --fixed（またはページ数で自動判定）で紙面配分（参考文献が本文を圧迫していないか等）も診断する。提出直前の最終確認に使用。卒論・研究論文・学会投稿論文・研究概要すべてに対応。旧 abstract-format-check を統合。
研究概要・中間発表予稿の「研究としての筋」を、論理の鎖(L1背景→L2関連研究と限界→L3課題(差分)→L4仮説→L5評価→L6位置づけ)として1リンクずつ採点し、さらに端点間の整合(掲げた構成概念=旗と評価指標=物差しのズレ=construct validity)を照合するスキル。各文の綺麗さ(→articulation-check)でも汎用の弱点(→weakness-analyze)でもなく「リンク間が繋がっているか／旗と物差しが一致しているか」を見る。骨子・位置づけ・仮説・課題設定・研究の一貫性を見てほしい、research framing を見て、と言われたとき。旧名 research-framing-check。