「hn」の検索結果

Write or refine Chinese technical notes and articles in a concise, evidence-led voice. Use for substantive drafting or rewriting, not every Chinese reply.

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。

Single-page SEO audit: content quality under Google's E-E-A-T framework and Helpful Content guidelines, on-page factors, search intent, technical signals and readability. Pulls GSC data for the page, crawls the live HTML, checks metadata, schema, internal linking and depth, and produces a scored report with fixes. Use whenever the user wants to analyze a specific page or URL, not the whole site. Trigger on: "analyze this page", "audit this URL", "how is this page doing", "evaluate my blog post", "check this landing page", "page SEO", "content quality check", "is this page good enough", "review this page's SEO", "what's wrong with this page", "how can I improve this page", "page analysis", "single page audit", "content audit for [URL]", or any request naming a specific URL/page for SEO evaluation. If the user provides a specific URL (not just a domain), this is likely the right skill; use /seo-analysis for full-site audits instead.

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。

Implement technical plans from thoughts/shared/plans with verification

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。

Implement technical plans from thoughts/shared/plans with verification

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。

Recover deleted commits from GitHub using REST API, web interface, and git fetch. Use when you have commit SHAs and need to retrieve actual commit content, diffs, or patches. Includes techniques for accessing "deleted" commits that remain on GitHub servers.

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。

Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

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。

Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.

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。

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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。

Write research/technical reports with strong structure + figure standards. Supports Markdown/HTML/PDF outputs (Quarto optional), executive summary, methods, results, discussion, and reproducibility appendix.

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。

World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.

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。

Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.

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。

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

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。

Generate comprehensive technical design translating requirements (WHAT) into architecture (HOW) with discovery process. Use when creating architecture from requirements.

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 comprehensive technical design for a specification

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。

Interactive technical design quality review and validation. Use when reviewing design before implementation.

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。

Interactive technical design quality review and validation

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。

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step deterministic pipeline: Claude handles intelligence, Python handles all arithmetic. Year is NOT required — the engine infers the horizon. REQUIRES: Bash tool + Python 3.x. Not compatible with claude.ai web (use Soft Predict Future instead).

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。

Activate this skill for ANY future-oriented question. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", "Can [X] succeed?", "What's the future of X?", foresight analysis, scenario planning, STEEEP analysis, futures cone, prediction requests, or any question about a future outcome. Year is NOT required — the engine infers the horizon. Also activate when the user says "predict", "forecast", "what are the odds", "scenario analysis", or asks about competitive races, technology adoption, geopolitical shifts, or market dominance. REQUIRES web search to be enabled — if web search is unavailable, tell the user before proceeding.

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。

AI news tracking skill that monitors 80+ entities across 6 free sources (Reddit, HN, GitHub, HuggingFace, arXiv, X/Twitter). Generates scored daily reports with infographics and message digests. Invoke via /morning-ai.

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。

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

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。

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

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。

Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.

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。

Run or configure the daily wiki maintenance cycle: check source freshness, refresh the index and hot.md, and manage its scheduled 9 AM launchd/systemd/cron reminder. Use for daily or morning wiki refresh or scheduler setup and verification.

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。

Professional logo and brand-mark design, from brief to production files. Guides discovery and the design brief, concept generation, choosing a mark type (wordmark, monogram, letterform, pictorial, abstract, emblem, mascot, combination), building clean geometric SVG logos, optical refinement, colour and typography, testing (16 px, one-colour, reversed, shelf test), client presentation, delivery variants and brand guidelines. Includes a searchable library of 1,400+ real-world SVG logos classified by type, technique, geometry and industry, plus scripts that audit SVGs and generate test sheets, presentation boards and export variants. Use this skill whenever the user wants a logo, logotype, wordmark, monogram, brand mark, symbol, app icon or favicon designed, redesigned, refreshed, critiqued or compared; asks for logo ideas or concepts; needs a design brief, logo guidelines, lockups or an identity system; or mentions branding a new company, product, app or project — even if they don't say the word "logo".

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。