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概要と使いどころ
AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
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Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
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Read and analyze Xiaohongshu comments from the user's signed-in Chrome session. Use to collect, export, summarize, or classify comments for content planning, follow-up posts, audience research, reply strategy, and 引流文案。
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SOP for formal mathematical domain modeling. Trigger when: - Creating new formal models or applying mathematical lenses to analyze existing specifications. - Navigating phases: Identify, Select, Construct, Validate, Record, Connect. - Prompt contains: /form, form workflow, categorical, coalgebraic, linear logic, information-theoretic, validation.
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Smart, granular git commits following monorepo conventions. Analyzes changes, filters to session-relevant files, groups by intent, and uses conventional commit format. Use when committing code changes.
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Analyze code changes and update relevant documentation to stay in sync. Runs automatically before /commit. Assumes docs are outdated — only updates what this change touches, never over-reaches.
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FOUNDATIONAL SKILL - Analyze every prompt for underlying motivation using Golden Circle (WHY->HOW->WHAT). Runs BEFORE any other skill. No action without understanding WHY.
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Review an author's manuscript, its argument and paragraph structure, analyze reviewer comments, draft rebuttals, and verify revisions. Use for self-review, structural edit plans, simulated reviewer objections, author responses, revision closure, and submission-readiness assessment. Formal assigned peer review is outside this skill; manuscript edits and final figures/tables use their owning skills.
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Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.
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Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public knowledge-hygiene 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。
Use before build, fix, debug, review, or docs implementation; converts prompts into structured intent JSON with goal, constraints, and routing.
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Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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Analyze text and images for harmful content using Azure AI Content Safety (@azure-rest/ai-content-safety). Use when moderating user-generated content, detecting hate speech, violence, sexual content, or self-harm, or managing custom blocklists.
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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template.
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Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.
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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
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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), pr...
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AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。
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Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
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Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.
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Deploys and configures Zeek (formerly Bro) network security monitor to passively analyze network traffic, generate structured logs, detect anomalous behavior, and create custom detection scripts for threat hunting and incident response.
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。