3,871 件 ・ 関連度順
概要と使いどころ
Guide rapid triage and initial response to security incidents following NIST SP 800-61 methodology. Use when the user mentions 'incident response,' 'security incident,' 'triage,' 'we've been hacked,' 'breach,' 'compromised,' 'malware detected,' 'suspicious activity,' 'IOC,' 'indicators of compromise,' or needs help handling a security event.
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。
Web page change detection, availability tracking, and RSS feed generation. Use to monitor changes, downtime, or make a feed.
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。
Behavioral classification, performance analysis, and trading style detection for Solana wallets
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 trade logging, performance review, behavioral pattern detection, and strategy attribution for systematic improvement
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。
Token holder distribution, concentration metrics, insider detection, and supply analysis for Solana tokens
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。
Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens
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。
Walk-forward validation framework for trading strategies and ML models with time-series-aware splits, overfit detection, and regime-aware 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。
MEV exposure assessment, sandwich attack detection, and protection strategies for Solana DEX trading
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。
Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging
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 project setup — detects your project, configures metaswarm, writes project-local 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。
Detect package managers and CI action pins, then discover outdated or vulnerable dependencies. Returns structured findings without upgrading. Use when the user asks to "review dependencies", "check for outdated packages", "check dependencies", "scan dependencies", "dependency review", "check for outdated GitHub Actions", or "are my workflow actions up to date".
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 performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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 toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
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。
Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.
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。
Mine lost deal notes to identify recurring objection patterns. Create objection response playbooks from won deals.
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。
Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
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。
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
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 at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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。
Automated contract verification, detection, and remediation across multiple languages using formal preconditions, postconditions, and invariants. This skill provides both reference documentation AND execution capabilities for the full PLAN -> CREATE -> VERIFY -> REMEDIATE workflow.
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。
Progressive four-tier URL content scraping with automatic fallback strategy. USE WHEN user says "scrape this URL", "fetch this page", "get content from", "can't access this site", "use Bright Data", "pull content from URL", or needs to retrieve web content that may have bot detection or access restrictions.
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。
Operational skill hub for LLM system architecture, evaluation, deployment, and optimization (modern production standards). Links to specialized skills for prompts, RAG, agents, and safety. Integrates recent advances: PEFT/LoRA fine-tuning, hybrid RAG handoff (see dedicated skill), vLLM 24x throughput, multi-layered security (90%+ bypass for single-layer), automated drift detection (18-second response), and CI/CD-aligned evaluation.
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 content in any language indistinguishable from a skilled human writer. Avoids AI-sounding phrases, varies sentence structure, creates natural prose. Detects target language and loads language-specific AI-tells, register, and orthography from `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。
Use when the user wants to humanize text, trim slop, de-AI or de-slop writing, remove AI tells, fix robotic or ChatGPT-sounding prose, or make writing sound human and natural. Also run before delivering a CV, cover letter, email, report, or essay to be sent. Removes 71 documented AI-writing patterns with a local detector, preserves numbers, names and citations, and rebuilds toward a human voice rather than a flat husk. Mode-aware, so it never fabricates voice on factual content.
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。