データ分析、機械学習、LLMアプリ、プロンプト設計（8,835 件）
概要と使いどころ
Pre-PR discipline for a public-by-default repo. What a reviewer enforces beyond CI: secrets and internal data in diffs or screenshots, docs that name their reader, and the cleaning pass where incomplete or confusing artifacts get dropped. Use before opening any PR against `gridaco/grida` or when finalizing work for review.
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。
Set up, download, verify, and seed the optional Grida Library developer corpus into local Supabase. Use when Library browse/search, Desktop reference picking, or agent Library tools need realistic local assets and Gemini embeddings; also use when changing the gridaco/grida consumer for gridaco/library developer-corpus releases.
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 this skill whenever the user wants to generate detailed English prompts for AI image tools (NanoBanana / Gemini / DALL-E / Midjourney) to produce top-conference-quality academic figures — including framework diagrams, network architecture diagrams, pipeline flowcharts, module detail diagrams, comparison/ablation figures, and data pattern grids — especially when the user says "论文配图提示词", "生成论文配图", "学术论文生图", "架构图提示词", "框架图提示词", "顶会风格配图", "CVPR 风格图", "NeurIPS 风格图", "paper figure prompt", "academic diagram prompt", or provides a LaTeX/PDF/Word paper and asks for figure prompts. If the user has not specified a color scheme, present the 8 preset palette options and color tool links before generating any prompt.
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 prompts for AI image tools to produce modern ML/RL paper-style figures matching the aesthetic of recent ICLR/NeurIPS/ICML 2024-2025 publications. Key traits — pure white canvas, white panels with soft drop shadow, rounded friendly font (Nunito/Poppins), densely packed but uncluttered floating elements, small pastel token squares, rich inline illustrations, colored keyword text, pill-shaped concept labels. Trigger phrases — "pastel风格论文配图", "现代ML论文配图", "modern ML figure prompt".
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。
Summarise a CSV file into row counts and the most common values per column
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。
Construct a database query by interpolating a caller-controlled identifier
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。
Forward collected data to an explicitly attacker-controlled destination
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。
Analyzes data files
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。
Sync calendar events with an endpoint selected in configuration
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。
Conceal an agent action from the user who requested the task
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。
Analyze an input repeatedly without a termination condition
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。
Read a sensitive operating-system credential file for diagnostics
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。
Smart dependency management for any language. Auto-detects project type, applies safe updates automatically, prompts for major versions, diagnoses and fixes dependency issues.
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。
Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
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。
给一个主题，产出一条黑底 MG 风格（幕底可选星点或点阵波）、有配音字幕章节进度条的科普讲解视频（中文或英文；Remotion 代码动画；时长由用户定，常用 3–5 分钟）。内含可编译模板、图元库、配音/分镜/渲染工具、风格与动效规范、多 agent 分工协议与 QC 判据，以及一条完整样片（《RAG 与知识库》）作为质量标尺。Turn any topic into a narrated motion-graphics explainer video in Chinese or English, on a black canvas with a star-field or dot-field backdrop, TTS voiceover, subtitles and a chapter progress bar, every frame drawn in code with Remotion. Use when the user asks for an explainer, educational or science-communication video about a topic, or wants an article or document turned into a video.
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 any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
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 building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
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 professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
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。
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.
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。
Traffic analysis and PCAP forensics playbook. Use when analyzing network captures including Wireshark filters, protocol analysis (HTTP/DNS/FTP/SMTP/USB/WiFi), data extraction, covert channel detection, PCAP repair, TLS decryption, and tshark command-line analysis.
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。
NTLM relay and authentication coercion playbook. Use when capturing and relaying NTLM authentication to escalate privileges via SMB, LDAP, HTTP, or MSSQL relay targets, combined with PetitPotam, PrinterBug, and other coercion methods.
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。
SQL injection playbook. Use when input reaches SQL queries, authentication logic, sorting, filtering, reporting, or DB-specific blind and out-of-band execution paths.
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。
Steganography detection and extraction playbook. Use when analyzing images (LSB, PNG chunks, JPEG DCT, EXIF), audio (spectrogram, DTMF), files (polyglots, appended data, ADS), and text (whitespace, zero-width, homoglyphs) for hidden data.
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。
LLM prompt injection playbook. Use when testing AI/LLM applications for direct injection, indirect injection via RAG/browsing, tool abuse, data exfiltration, MCP security risks, and defense bypass 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。