데이터·AI 스킬

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.

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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.

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Orchestrate the migrate-prep cascade — extract --prep → direct --prep → prototype --prep → assets prep — with confirmation gates between phases. Builds the data structure migrate consumes.

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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.

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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.

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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.

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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".

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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.

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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.

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Configure and manage Umami analytics tracking for the portfolio.

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Manage Sanity content -- schema changes, data migrations, debugging queries, batch operations, and revalidation.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Extracts structured data from LLM responses using JSON schemas, Zod validation, and function calling for reliable parsing. Use when users request "structured output", "JSON extraction", "parse LLM response", "function calling", or "typed responses".

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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.

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Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

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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".

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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".

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Implements TanStack Query (React Query) for server state management with caching, mutations, optimistic updates, and infinite queries. Use when users request "react query", "tanstack query", "data fetching", "cache management", or "server state".

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