1,390 件 ・ 関連度順
概要と使いどころ
Use when you need to install the embedded robot agents into either .cursor/agents or .claude/agents, selecting the destination interactively and copying the embedded agent definitions from project assets. This should trigger for requests such as Install embedded agents; Bootstrap .cursor/agents; Bootstrap .claude/agents; Copy robot agents. Part of cursor-rules-java 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。
Threat actor infrastructure tracking involves monitoring and mapping adversary-controlled assets including command-and-control (C2) servers, phishing domains, exploit kit hosts, bulletproof hosting, a
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。
Monitors dark web forums, marketplaces, paste sites, and ransomware leak sites for mentions of organizational assets, leaked credentials, threatened attacks, and threat actor communications to provide early warning intelligence. Use when establishing dark web monitoring coverage, investigating specific data breach claims, or enriching incident investigations with dark web context. Activates for requests involving dark web OSINT, leak site monitoring, credential exposure, Recorded Future dark web, or Tor hidden service intelligence.
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。
Plan and validate PFC 5.0 asphalt fluid, pore-pressure, buoyancy, seepage, and PFC-FLAC coupling contracts while keeping version-specific commands and licensed coupling assets external.
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。
Govern Asphalt-Codex-Skills-5 as a PFC 5.0-only asphalt-mixture package, enforcing version purity, external fistPkg boundaries, portable assets, validation, and release hygiene.
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 prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.
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。
"Apply the Fama-French three-factor model to decompose asset returns into market, size, and value factors. Use this skill when the user needs to explain cross-sectional return differences, evaluate fund performance beyond CAPM alpha, assess small-cap or value tilts in a portfolio, or when they ask 'why do small caps earn more', 'is value premium real', or 'what factors drive returns'.".
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。
"Apply the Efficient Market Hypothesis (Fama, 1970) to evaluate information incorporation in asset prices across weak, semi-strong, and strong forms. Use this skill when the user needs to assess market efficiency, determine if a trading strategy can generate abnormal returns, evaluate event studies, or when they ask 'can technical analysis work', 'does the market already know this', or 'is this anomaly exploitable'.".
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。
"Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when they ask 'why do investors hold losers too long', 'how does loss aversion affect pricing', or 'what biases drive this market pattern'.".
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。
"M&A integration playbook covering eight modules: strategic rationale, target screening, due diligence (financial/legal/commercial), valuation with valuation bridge, synergy analysis, deal structuring (stock vs. asset, cash vs. equity, earn-out), SPA key clauses, and post-merger integration (PMI). Use for deal evaluation, valuation disputes, structure design, earn-out design, synergy breakdown, integration risk, or hostile takeover defense. Triggers: 『併購』『收購』『M&A』『盡職調查』『DD』『估值橋』『綜效』『earn-out』『換股比例』『PMI』『買殼』『借殼上市』『敵意併購』『交易結構』. For Taiwan EMBA 財管組 case studies and term reports (台大／政大／陽交). Complements Asgard `biz-dcf` and `fin-modeling` (valuation tools) plus `biz-corporate-governance` (governance layer) by providing the transaction-layer framework.".
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。
Prepare iOS App Store and Google Play submissions—metadata, assets, compliance checks, and release checklist. Use before store upload or review submission.
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 context about the regelrecht Figma design system and helps with Figma-related tasks like component lookups and design comparisons. Use this skill proactively when: implementing UI components, looking up Figma node IDs, comparing code with Figma designs, downloading Figma assets, or working with design tokens. Activate automatically when user mentions Figma, design system, component styling, or asks about RR-Components.
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 or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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 preparing a desktop or web app update for commit, packaging, installer validation, GitHub publication, release asset upload, or old-release pruning. Protects user data and verifies that published artifacts match the tested build.
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。
Turn launch dates, campaign inputs, and asset requests into a structured content calendar and workback plan. Use when the work is recurring marketing coordination and schedule packaging. Do not use for brand strategy, positioning design, or deep performance interpretation.
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 loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.
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 repeatedly placing GLB models in PlayCanvas to record each asset's uniform scale, grounding offset, pivot compensation, and yaw once.
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。
Reconnaissance and methodology playbook. Use when mapping assets, discovering endpoints, fingerprinting technology, and building a structured testing plan for a new target.
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。
Entry P1 category router for reconnaissance and methodology. Use when mapping scope, discovering assets, fingerprinting technology, building endpoint inventory, and choosing the first high-value security testing path.
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。
Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.
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 linking patterns - Universal Links (iOS), App Links (Android), URI schemes, expo-linking API, React Navigation linking config, Expo Router automatic linking, AASA/assetlinks.json setup, deferred deep links, testing
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。
Refresh tracked pre-launch stablecoin milestones, dates, launch phases, featured content, and promotion evidence. Use for the recurring upcoming-assets review.
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。
Toggle the badges-page Honesty Mode build-time flag on or off. ON makes the README badge generator emit direct /badges/_assets/<handle>/<file>.svg URLs (no Cloudflare Worker, no ?repo= validation). OFF restores worker-validated URLs. Use when the user says "/honesty-mode on", "/honesty-mode off", "turn honesty mode on/off", or asks for the current honesty-mode state.
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。
Gaia Skill Tree image-generation and deterministic export harness for Ascension Overdrive v3. Use when generating, iterating, refining, cropping, color-grading, making transparent cutouts, compressing, upscaling, or validating production image assets. Always use image gen 2 / gpt-image-2 and never nano-banana.
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。