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概要と使いどころ
AI 駆動開発のレビュー品質を安定化させるレビュー規律。レビュー対象を分類し、要件・設計・差分・検証・報告を分けて見て、根拠に基づき approved / needs_revision / blocked / rejected を明示し、未検証事項と残リスクを隠さず memory に残す。「レビューして」「PR レビュー」「仕様/設計/差分/検証/報告をレビュー」「セキュリティ影響/データ影響を確認」「完了報告を検証」「/compact 前にレビュー記憶を作る」ときに使う。
Apply when wiring validation, error handling, or framework adapters. Concentrate guards at system boundaries (CLI, config, network, external APIs); trust internal types and keep business logic in pure functions.
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。
Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.
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 before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.
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 for any code, documentation, config, dependency, test, review, or maintenance task in the Nauthilus repository that must follow AGENTS.md, POLICY.md, focused reproducer-first debugging, strict DRY/OOP boundaries, English comments, GOEXPERIMENT=runtimesecret test discipline, Makefile-driven validation, prompt-sync checks, and dirty-tree preservation.
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。
Review FriendlyBet quality, regression risk, test plans, release readiness, version bump discipline, scoring/locking correctness, provider fallback behavior, and dirty-worktree safety. Use before shipping app code, scoring changes, migrations, sports integrations, copy updates, PWA cache changes, or any risky user-facing flow.
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。
Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.
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。
Artificial intelligence for modeling and understanding extreme weather ... — 24. Feb. 2025 · This re
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。
scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
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。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
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。
Create opinionated premium DESIGN.md guidance for Stitch, emphasizing calibrated typography, restrained color, layout discipline, motion, and anti-generic UI rules.
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 for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.
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 only when the user explicitly requests strict code-cell-only output for a Jupyter or undergraduate data-science task, or when an ITX2007 assignment requires that format. Do not activate for ordinary data analysis or explanation requests.
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。
Motor de crescimento para produtos digitais -- growth hacking, SEO, ASO, viral loops, email marketing, CRM, referral programs e aquisicao organica.
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。
Apply behavioral science and mental models to marketing decisions, prioritized using a psychological leverage and feasibility scoring system.
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。
Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML
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。
This is the core skill for the deep research and writing tool—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup skill.
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。
Performs numerical and symbolic mathematics using NumPy, SciPy, SymPy, and mpmath. Use when the user needs linear algebra, arrays, optimization, integration, ODEs, symbolic expressions, equation solving, or arbitrary-precision arithmetic.
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 before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.
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。
Statistics, probability, linear algebra, and mathematical foundations for data science
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。
Structured exposure habits for building taste through relentless, intentional consumption. Not passive scrolling. Active intake across design, architecture, film, music, food, and culture. Use when building a taste practice, advising on professional development, or diagnosing why someone's design range feels narrow.
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。
Pull design principles from architecture, fashion, film, industrial design, and physical spaces into screen-based design. Use when seeking inspiration beyond digital, expanding creative range, or building cross-disciplinary design vocabulary.
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 a tdmcp TouchDesigner-bridge vertical slice the house way — promote an op from /api/exec to a first-class REST endpoint (e.g. POST /api/connect, GET /api/logs, node_detail flags), add its typed method in src/td-client/touchDesignerClient.ts, its Zod envelope in src/td-client/validators.ts, rewire the tool(s) to prefer the endpoint with an exec fallback, and write py_compile + td/tests unittest + offline msw client tests. Use when adding/changing a bridge REST endpoint, editing td/, extending node_detail/get_bridge_logs, promoting connect/disconnect/param-modes/DAT-text off exec, or doing any exec→REST work — especially the td-depth bridge-robustness backlog. Honors probe-live discipline when TouchDesigner is offline.
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。