データ分析、機械学習、LLMアプリ、プロンプト設計（8,727 件）
概要と使いどころ
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware investigation.
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。
Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing 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。
Captures and analyzes network packet data using Wireshark and tshark to identify malicious traffic patterns, diagnose protocol issues, extract artifacts, and support incident response investigations on authorized network segments.
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 and analyze covert communication channels used by malware including DNS tunneling, ICMP exfiltration, steganographic HTTP, and protocol abuse for C2 and data exfiltration.
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 network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware detection.
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 JSON-LD structured data for AEM Edge Delivery Services pages. Analyzes page content and metadata to determine the appropriate schema.org types, extracts relevant properties, and produces validated JSON-LD snippets ready for implementation in head.html or scripts.js. Use when adding rich results support, improving search appearance, or auditing existing structured 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。
Orchestrate the migrate-prep cascade — extract --prep → direct --prep → prototype --prep → assets prep — with confirmation gates between phases. Builds the data structure migrate consumes.
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 polished, leadership-ready performance briefing with KPI tiles, executive narrative bullets, and a driver analysis — all as a print-ready HTML document. Always use this skill when someone asks for an executive summary, performance briefing, leadership readout, stakeholder update, or business review — even if they don't say "executive" explicitly. Trigger phrases include: "write a summary of last week's performance," "create a briefing for our leadership team," "produce a monthly business review," "what should I tell executives about our metrics," "generate a performance narrative," "QBR summary," "weekly business review," "board update," "stakeholder briefing," "how did we do last week," or "give me a performance snapshot." When in doubt, use this skill — it is always better to produce a polished briefing than a raw data dump.
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 a full InDesign data merge from a CSV/TSV and an .indd template (or a PDF that gets converted to .indd automatically). Use this skill whenever the user wants to merge a data file with a layout template — including visiting cards, certificates, badges, catalogs, mailers, labels, invoices, or any per-row personalisation. Triggers on: "data merge", "InDesign merge", "merge my CSV with InDesign", "batch export PDF from template", "variable data InDesign", "personalise each row", or any request combining an .indd / PDF template with a data file. Use this skill even when only two of the three inputs (template, data, images) are mentioned — and even when the user never uses the phrase "data 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。
Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.
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。
Fetch up-to-date, version-specific documentation for any major library (Next.js 16, Sanity, LangGraph, React, Tailwind CSS, Vitest, Framer Motion) via the Context7 MCP server. Use when writing code against unfamiliar or recently-changed framework APIs, when training data may be stale, when resolving deprecations or version-specific syntax, or when the repo warns about framework drift (e.g. the nextjs-agent-rules block in portfolio-v1/AGENTS.md). Invokes the resolve-library-id and get-library-docs MCP tools.
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。
Master reference for Sanity CMS integration in this portfolio. Load this skill whenever working with Sanity schemas, queries, data, webhooks, media, draft mode, or any CMS operation. Covers all 20 document types, GROQ patterns, client configuration, caching, revalidation, and studio structure.
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。
Manage Sanity content -- schema changes, data migrations, debugging queries, batch operations, and revalidation.
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。
Configure and manage Umami analytics tracking for the portfolio.
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, revise, or evaluate repository Agent Skills when the user asks to write a SKILL.md, improve triggers or information hierarchy, restructure references/scripts/assets, or diagnose skill predictability; do not use for product code, AGENTS.md initialization, general prompt editing, or automatic repository-wide cleanup.
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 regression scouting routing. Use when agents, prompts, skills, model/tool routing, harness fixtures, or generated-output expectations change and need regression scenarios; do not use for ordinary product-code regressions.
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, edit, fix, or explain Mermaid diagrams for relationships, decision branches, interactions, lifecycles, and data models; also use when the parent selects a diagram to materially clarify an explanation. Do not trigger for simple status, one fact, short lists, quantitative charts, or marketing slides.
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 source data, tables, CSV/XLSX extracts, metrics, or experiment results with explicit data-authenticity checks. Use for profiling, cleaning assumptions, statistical summaries, segmentation, trend analysis, or "analyze this data"; do not use for spreadsheet file surgery, chart design only, database migrations, or claims without data provenance.
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。
Diagnose user-reported AILI/ROSE harness or workflow behavior problems without editing files; use when the user says a command, skill trigger, subagent packet, memory flow, installer, docs, or agent prompt behavior is wrong and wants to know where the issue lives and how to fix it. Do not use for normal product-code bugs; return any approved modification need to ROSE.
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。
Structure source-grounded consulting-style analysis for strategy, market, product, operations, or decision memos. Use for MECE problem framing, hypotheses, options, tradeoffs, executive recommendations, or "consulting analysis" requests; do not use for legal/financial advice, implementation, data computation, chart rendering, or unsourced business claims.
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。
Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.
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 and optimizes SQL queries using EXPLAIN plans, index recommendations, query rewrites, and performance benchmarking. Use for "query optimization", "slow queries", "database performance", or "EXPLAIN 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。
Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.
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 data table patterns with filtering, sorting, pagination, row actions, column configuration, server/client rendering modes, and empty/loading states. Use when building "data tables", "list views", "admin tables", or "data grids".
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。