701 件 ・ 関連度順
概要と使いどころ
Framework for evaluating and selecting Consent Management Platforms (CMPs). Covers TCF v2.2 certification requirements, Global Privacy Control support, multi-regulation compliance (GDPR, CCPA, LGPD), A/B testing capabilities, API integration options, reporting features, and a structured vendor comparison methodology.
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。
Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.
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。
Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3.
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。
Shioaji Taiwan financial trading API guide. Use when trading stocks/futures/options on Taiwan markets, subscribing to real-time market data, querying account info, or building automated trading systems. Shioaji 台灣金融交易 API 指南。適用於：股票/期貨/選擇權交易、即時行情訂閱、帳務查詢、自動交易系統開發。
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Use when ranking backlogs, deciding what to do first based on effort vs impact (quick wins vs big bets), prioritizing feature roadmaps, triaging bugs or technical debt, allocating resources across initiatives, identifying low-hanging fruit, evaluating strategic options with 2x2 matrix, or when user mentions prioritization, quick wins, effort-impact matrix, high-impact low-effort, big bets, or asks "what should we do first?".
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。
Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.
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。
Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.
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 a feature specification from scratch through a relentless, evidence-based interview that walks the design tree decision-by-decision, resolving dependencies as it goes. Use when the user wants to plan, design, scope, specify, or flesh out a new feature, capability, or system behavior before implementation. Produces a feature specification focused on system behaviors, not implementation detail. Does not plan a restructure of code that already exists — use plan-a-change. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features — use project-documentation. Does not design the contract for an interface — use design-an-api. Does not research open-ended options before there is a feature to specify — use research. Does not map the bounded contexts of existing code — use ddd-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。
Capture structured feedback on the Han skills and agents used in the current session and optionally post it as a GitHub issue to testdouble/han. Use at the end of any session where one or more han-* skills or agents ran, to rate a run, log what worked and what didn't, or submit observations for maintainers. Does not review code, investigate bugs, or research options; use code-review, investigate, or research for those. Does not provide feedback on skills or agents from non-Han plugins.
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 and updates coding standards, conventions, rules, and guidelines for the current project. Use when creating new standards from scratch, converting existing documents into coding standards, or updating existing standards. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not write feature or system documentation — use project-documentation for that. Does not research open-ended options — use research. Does not produce runbooks for operational scenarios — use runbook for that.
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。
Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not plan the structural change a root cause calls for — use plan-a-change. Does not map bounded contexts or domain boundaries — use ddd-analysis. Does not capture feedback on Han's own skills — use han-feedback for that.
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。
Performs a gap analysis between two artifacts (a current state and a desired state) and produces a plain-language, stakeholder-readable report indexed by stable gap IDs. Use when the user wants to compare, evaluate, audit, or reconcile one artifact against another. Does not investigate runtime bugs — use investigate. Does not assess module-level architecture — use architectural-analysis. Does not research open-ended options with no second artifact to compare against — use research.
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。
Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/. Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.
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。
Write or update the CLI `--help` text and README section for a new or changed rezolus feature (a subcommand, flag, mode, or behavior change), then prove the help is usable by dispatching a fresh subagent that has never seen the code. Use this whenever you add or change how rezolus is invoked — new `rezolus <subcommand>`, new flags/args on an existing mode, renamed options, or changed defaults — and any time the user says the help/README is thin, stale, confusing, or "an agent couldn't figure this out." Treats help text as an interface under test, not prose.
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 you need to choose from predefined options.
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 implementing suggest options as users type.
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 you need to compare features and options side-by-side.
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。
Every option you can pass to the TestDriver client, with defaults and examples
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 TestDriver sandbox options and environment settings
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 several distinct design directions from one HTML source plus a contrast sheet that shows every direction side by side. Use when the user says /reimagine-it variations, "show me options", "give me three takes", "which direction works best", or wants to compare design directions before choosing one. CLI: npx reimagine-it variations -i page.html -n 4 -o review/. Part of the reimagine-it Content-Derived Design suite.
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。
The research-domain clarification skill — find underspecified items in a thesis spec.md (prose wrong_if, universe rows without rationale, missing budget/expiry/pins, ambiguous pillars), ask the human structured questions WITH options (max 5 per round), encode the answers back into spec.md's Clarifications section, and re-evaluate checklists/thesis-quality.md after every write (Q32 bidirectional maintenance).
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。
portfolio hedging, tail risk hedging, options overlay, correlation regime, downside protection, portfolio insurance, risk mitigation strategy, hedge construction, black swan hedging, volatility hedging
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。
Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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。
Produce anime-style and stylized 2D animation with generative image and video tools as a craft discipline, not a filter. Use when a request asks for anime, manga-style, cel-shaded, sakuga, shonen/shojo/seinen/slice-of-life, mecha, or retro-90s-OVA animation; when planning shots, motion, and cutting rhythm to anime convention; when holding a character on-model and preventing style drift across shots or episodes; when choosing prompting vocabulary that actually controls anime look; when pairing voice, music, and impact SFX to stylized action; and when navigating the cultural, IP, and platform-policy sensitivities of AI anime (studio/artist style imitation, fan-art and IP boundaries, disclosure and monetization). This is provider-neutral; models are named only as illustrative options.
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。