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概要と使いどころ
Condensed tika-eval pattern for charset-detector regression hunts ("A picks encoding X, B picks Y") using one build and two configs — encoding-pair flip queries, OOV/languageness/FFFD signals, per-file detector attribution.
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。
Extract text from images and scanned documents using PaddleOCR - supports 100+ languages
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。
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
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。
Qualify the exact final diff for a LoopX-managed goal. Use when goal policy enables change_quality_qualification, before a non-trivial delivery or merge, and when producing or repairing an exact-scope quality receipt. The workflow is language-neutral, permits at most one policy-authorized safe-fix pass, and never grants merge or repository authority.
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。
Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.
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。
Searches and rewrites code by AST shape across 25 languages. Use when the target is a syntax pattern (every call/class/import shaped like X, a codemod, a YAML rule) rather than literal text; for plain strings, comments, or filenames, use rg.
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 Codex needs language-server diagnostics, definitions, references, symbols, or rename safety checks in the current workspace.
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。
Configures a language server so editor/agent tooling (diagnostics, go-to-definition, references, rename) works. Use when a project needs an LSP installed or wired, or a 'no LSP server configured' error appears.
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。
Applies strict, modern language practice (typed errors, exhaustive match, tests that can fail) for Python, Rust, TypeScript, and Go. Use for work on .py, .rs, .ts, or .go 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。
Automate web browser interactions using natural language via CLI commands. Use when the user asks to browse websites, navigate web pages, extract data from websites, take screenshots, fill forms, click buttons, or interact with web applications. Supports remote Browserbase sessions with Browserbase Identity, Verified browsers, automatic CAPTCHA solving, and residential proxies — ideal for protected websites and JavaScript-heavy pages.
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 or update feature specifications from natural language descriptions. Use when starting new features or refining requirements. Generates spec.md with user stories, functional requirements, and acceptance criteria following spec-driven development methodology.
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 skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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 writing or revising scientific manuscript prose: IMRAD sections, abstracts, figure/table captions, citation-integrated paragraphs, reporting-guideline language, terminology, clarity, and journal-submission text.
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。
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
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。
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation 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。
Domain routing and boundary guidance for authorized general injection testing outside SQL injection, including NoSQL, LDAP, XPath, and expression language injection. Use when a task belongs to the general injection domain and needs scope, evidence, pivot, or exit criteria.
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 the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments, geography, language, and content fit. Helps judge sponsorship and market fit.
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 the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
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 the user wants to validate a product idea, find pain points, mine demand signals, discover objections, or gather voice-of-customer language from Reddit, social posts, video transcripts, and comments. Produces evidence-backed product research.
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。
Direct-invocation specialist for polished, responsive, self-contained HTML mockups and interactive prototypes grounded in the user's conversation, product context, and design language. Use when the user explicitly invokes html-prototype or the broad html skill routes a mockup or prototype request here. Do not activate independently from a general request. Treat a mockup as a noninteractive fidelity mode within this skill, not as a separate 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。
Send and read messages across all channels. Routes based on arguments — whatsapp, email, slack, telegram, discord, notion, or natural language like "send [msg] to [contact]".
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-powered browser automation — navigate sites, fill forms, extract structured data, log in with stored credentials, and build reusable multi-step workflows using natural language. Install: pip install skyvern && skyvern setup
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。
Automate Make (Integromat) tasks via Rube MCP (Composio): operations, enums, language and timezone lookups. Always search tools first for current 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。