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概要と使いどころ
Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
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。
Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.
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 provides a way to retrieve information from the AgentScope library for analysis and decision-making.
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 calculates key financial ratios and metrics from financial statement data for investment 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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M 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。
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale 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。
Use when the user explicitly asks to commit and push the current repository changes. Suitable for analyzing current changes, generating an English commit message that follows the repo's conventions, and completing the push. Not suitable for status-only checks, local-only commits, push-disallowed contexts, or any case where the user has not explicitly asked to write to the remote.
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 analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.
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。
Design, audit, and strengthen experimental validation for IEEE manuscripts using routed claim-evidence checks. Use when planning experiments, selecting baselines, designing ablations, choosing metrics, checking fairness, adding robustness tests, explaining results, analyzing complexity, building claim-to-evidence matrices, or responding to reviewer concerns about insufficient experiments.
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。
AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture. Essential when user mentions AWS costs, spending, billing, budget, pricing, CloudWatch, observability, monitoring, alerting, CloudTrail, audit, or wants to optimize AWS infrastructure costs and operational efficiency.
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 comprehensive code review capability for NestJS applications, analyzing controllers, services, modules, guards, interceptors, pipes, dependency injection, and database integration patterns. Use when reviewing NestJS code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review NestJS code", "NestJS code review", "check my NestJS controller/service".
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。
Expert Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.
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 autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.
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 a structured 8-phase workflow for resolving GitHub issues in Claude Code. Covers fetching issue details, analyzing requirements, implementing solutions, verifying correctness, performing code review, committing changes, and creating pull requests. Use when user asks to resolve, implement, work on, fix, or close a GitHub issue, or references an issue URL or number for 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。
Provides automated documentation updates by analyzing git changes between the current branch and the last release tag. Performs git diff analysis to identify modifications, then updates README.md, CHANGELOG.md following Keep a Changelog standard, and discovers documentation folders for contextual updates. Use when preparing a release, maintaining documentation sync, or before creating a pull request. Triggers on "update docs", "update changelog", "sync documentation", "update readme", "prepare release documentation".
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 targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data. Use when users need to find prospects, build lead lists, or track decision-maker movements.
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 pull requests for code quality, security issues, and best practices. Use when reviewing PRs, checking code changes, or analyzing diffs before merge.
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。
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
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 anti-sycophancy checklist to override agreement bias. Use when analyzing contested claims or avoiding socially convenient but inaccurate conclusions.
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。
Generates a Mermaid dependency graph showing import relationships between modules. Use when analyzing coupling, finding circular deps, or planning refactors.
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 analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata 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。
Use when planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.
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。
Access meme-rodeo MCP tools for searching, browsing, and analyzing memes. Use when the user asks about memes, wants to search for memes, get meme stats, or interact with the meme-rodeo platform.
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。
拆解已筛选爆款内容为什么能传播，分析受众情绪、分享动机、有效立场、首批传播者和可迁移元素。用户问一条内容为什么爆、打中了谁，或哪些机制能跨赛道借鉴时使用。 English: Explain why a selected breakout post spreads by analyzing audience emotions, sharing motives, effective positioning, early sharers, and transferable elements. Use when the user asks why content went viral, whom it resonated with, or what can be adapted across 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。