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概要と使いどころ
Analyzes malicious PDF files using PDFiD, pdf-parser, and peepdf to identify embedded JavaScript, shellcode, exploits, and suspicious objects without opening the document. Determines the attack vector and extracts embedded payloads for further analysis. Activates for requests involving PDF malware analysis, malicious document analysis, PDF exploit investigation, or suspicious attachment triage.
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。
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr
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 indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.
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。
[BETA] AEM Cloud Service expert skill — migrate javax.inject.@Inject fields in Sling Model (@Model) classes to injector-specific annotations (@ValueMapValue / @OSGiService / @SlingObject). Self-discoverable (scan for @Model classes with field-level @Inject); the replacement is chosen deterministically from each field's declared type. Use for "fix @Inject in HeroModel.java", "modernize my Sling Models", or when scanning an AEM project for @Inject misuse. Detailed decision table, companion-annotation handling, import management, and file-level skip policy are in recipe.md. This skill is in beta. Verify all outputs before applying them to production projects.
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 content sequences and determine authoring approach (default content vs blocks). Validates block selection and section styling for import/migration to AEM Edge Delivery Services.
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。
Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.
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 an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.
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 animations in React Native and Expo, making the decisions in the order that determines whether they feel right — should it animate, which thread it runs on, which properties, spring or timing, how the gesture hands off, how it degrades. Writes the implementation with Reanimated, Gesture Handler, Expo Router and expo-haptics. Use when animating anything in an Expo app, adding gestures, sheets, screen transitions, press feedback or haptics, or fixing motion that stutters on device. For web animation use `animate`.
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 deterministic seed data for development and testing with factory functions, realistic fixtures, and database reset scripts. Use for "data seeding", "test fixtures", "database seeding", or "mock data generation".
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。
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
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。
General-purpose orchestration for Codex. Uses update_plan plus background PTY terminals to run parallel codex exec workers.
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。
Multi-channel follow-up campaign orchestrator for ShapeScale sales. Schedules and executes SMS + Email sequences with CRM logging and auto-termination on replies. Use when following up with demo leads or managing outreach campaigns.
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。
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
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。
A modern text-based browser. Renders web pages in the terminal using headless Firefox.
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。
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
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。
X Intelligence CLI — search, analyze, and engage on X/Twitter from the terminal. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "search x", "find tweets about", "monitor x for", "track followers", (2) user is working on something where recent X discourse would provide useful context (new library releases, API changes, product launches, cultural events, industry drama), (3) user wants to find what devs/experts/community thinks about a topic, (4) user needs real-time monitoring ("watch"), (5) user wants AI-powered analysis ("analyze", "sentiment", "report"). Also supports: bookmarks, likes, following (read/write), trending topics, Grok AI analysis, and cost tracking. Export as JSON, JSONL (pipeable), CSV, or Markdown. Non-goals: Not for posting tweets, not for DMs, not for enterprise features. Requires OAuth for user-context operations (bookmarks, likes, following, diff).
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。
Fast X Intelligence CLI (Rust) — search, analyze, and engage on X/Twitter from the terminal. Use when: (1) user says "x research", "search x for", "search twitter for", "what are people saying about", "what's twitter saying", "check x for", "x search", "search x", (2) user wants real-time monitoring with "watch", (3) user needs AI-powered analysis with Grok ("analyze", "sentiment"), (4) user needs intelligence reports ("report"), (5) user wants to track followers ("diff"), (6) user needs trending topics ("trends"). Also supports: bookmarks, likes, following (OAuth), x-search, collections, CSV/JSON/JSONL export. Non-goals: Not for posting tweets, not for DMs, not for enterprise features.
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。
Builds drip email campaigns for leads not ready to buy. Determines segment and buying stage, selects content per stage, and generates a complete 5-7 email sequence over 30 days with timing and 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。
Determines all follow-up actions due today across the 10-day outreach cadence. Generates content and delivers automatically via Chrome DevTools MCP. Sends Telegram confirmation after completion.
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。
Classify reply sentiment and determine next action
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 for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.
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。
Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code 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。