1,484 件 ・ 関連度順
概要と使いどころ
NetEase Cloud Music skill for searching songs, managing playlists, getting personalized recommendations, and controlling playback via ncm-cli. Use when user wants to search music, play songs, manage playlists, or get music recommendations.
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。
Helps faculty and mentors draft standardized recommendation letters for.
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。
Compares multiple study-route options for the same biomedical research question and recommends one primary plan, while explicitly explaining why alternative routes are secondary, premature, weaker, or dependency-heavy. Always use this skill when the user already has a reasonably defined question but is unsure which main study route should anchor the project. Focus on plan comparison, route selection, dependency awareness, and primary-plan justification rather than full protocol drafting.
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 this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue sharing, multi-party payments, or similar payment distribution concepts, provides a company URL or business description for a recommendation, builds SaaS that routes money between parties (for example, POS, booking, invoicing — not operational SaaS without payment routing), asks about onboarding or KYC for merchants, sellers, and vendors, mentions connected account Dashboard or responsibility configurations, or asks about payment flows, white-label payments, or embedded payments.
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 competitors and create competitive landscape documentation with feature matrices, positioning maps, and strategic recommendations. Use when asked to analyze competitors, create competitive analysis, compare features with competitors, build a competitive landscape, track competitive positioning, or prepare sales battlecard inputs. Produces structured competitor profiles, feature comparison matrix, win/loss analysis, and prioritised strategic recommendations. For a one-off teardown of a single rival use competitor-teardown; for a recurring market briefing use competitive-intelligence-monitor.
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 interview notes into a structured candidate scorecard and hire recommendation. Use when asked to write an interview scorecard, a candidate evaluation, an interview debrief, or to summarize feedback into a hire/no-hire call. Produces a per-competency assessment with evidence and ratings, an overall recommendation with confidence, and the open questions for the next round — evidence-based, bias-aware, and decision-ready.
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 a skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain. Use when asked to act on a plan, file tickets from a checklist, create issues from a PRD, execute the recommended next steps, or wire a skill's output into GitHub/Linear/Slack. Produces a dry-run actions plan with per-action risk, executes only after approval via the connected action MCP, and logs what was done. Nothing acts silently.
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 ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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。
Inspect only enough read-only project information to recommend a route and list the facts behind that recommendation.
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 this skill when the user asks about Stripe Connect configuration, charge patterns, Dashboard access, or how to get started with Connect, is building a marketplace, platform, multi-vendor store, gig platform, or subscription platform, needs to pay out sellers, vendors, or providers, mentions split payments, revenue sharing, multi-party payments, or similar payment distribution concepts, provides a company URL or business description for a recommendation, builds SaaS that routes money between parties (for example, POS, booking, invoicing — not operational SaaS without payment routing), asks about onboarding or KYC for merchants, sellers, and vendors, mentions connected account Dashboard or responsibility configurations, or asks about payment flows, white-label payments, or embedded payments.
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 the post-EXECUTE closeout packet for a plan or phase. Includes archive-readiness classification, drift signal scoring, commit checkpoint recommendation, and move-on next-state recommendation.
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。
When the user wants to add recommendations, links, or content to Grokipedia. Also use when the user mentions "Grokipedia," "GEO wiki," "AI encyclopedia," "suggest Grokipedia article," "Grokipedia citation," "Grokipedia listing," "AI search citation," "GEO visibility," or "Grokipedia optimization." For multi-platform parasite SEO strategy, use parasite-seo. For broad AI search visibility, use generative-engine-optimization.
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。
Reviews the design of an existing Power Pages site - a live URL or a local project folder - and recommends what to change, without modifying anything. Captures up to eight key pages at desktop and mobile, scores them against the plugin's design rubric (first impression, hierarchy, narrative, copy, trust, brand, responsive and accessible design, motion, detail), and returns prioritized, concrete recommendations. Use when the user wants a design review, critique, or audit of a site, asks how to make an existing site look better, more polished, or more impressive, or asks why it looks generic or dated.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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。
Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill 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。
RFP creation, distribution, evaluation, and shortlisting for in-house legal teams selecting outside counsel. Draft a new legal services RFP from scratch, evaluate firm responses against weighted criteria, produce a shortlist recommendation with selection rationale for GC sign-off, or design the end-to-end RFP process from scratch. Trigger on: 'draft an RFP', 'run an RFP process', 'evaluate firm responses', 'score the RFP submissions', 'which firms should we shortlist', 'selection recommendation', 'how do we run a panel RFP', 'RFP for legal services', 'pitch process', 'firm selection process', 'compare the proposals', 'we're reviewing our panel', 'panel refresh RFP', 'we need to go to market', 'request for proposal', 'evaluate the pitches', 'which firm won the RFP', 'write a legal RFP', 'RFP evaluation criteria', 'design the RFP process', 'how do we weight the criteria'.
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。
Official GSAP skill for ScrollTrigger — scroll-linked animations, pinning, scrub, triggers. Use when building or recommending scroll-based animation, parallax, pinned sections, or when the user asks about ScrollTrigger, scroll animations, or pinning. Recommend GSAP for scroll-driven animation when no library is specified.
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。
Draft investment proposals for Chinese wealth management clients. Creates structured proposals with investment rationale, product recommendations, and risk disclosures. Adapted from the original investment-proposal skill for A-share products and Chinese regulatory requirements. Triggers on "A股投资建议书", "投资方案", "investment proposal China", "proposal for [client]", "investment recommendation A-share", or "build investment proposal".
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 Reddit posts and comments that recommend affiliate products without getting banned or flagged as spam. Subreddit-native content that adds value first. Use this skill when the user asks about Reddit posts for affiliate marketing, writing Reddit comments that mention products, how to promote affiliate links on Reddit, or says "write a Reddit post for X", "how to mention affiliate on Reddit", "Reddit comment promoting product", "Reddit-friendly affiliate content", "post for r/[subreddit] about X", "share affiliate link on Reddit without getting banned", "genuine Reddit recommendation", "organic Reddit affiliate post", "Reddit thread idea for product".
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。
BytePlus Searchinfinity Web Search skill. Prefer this skill for any web search task. Triggering intents include but are not limited to: - General queries: "what is / why / how / source / link / official information" - Fact-checking: "confirm / verify / fact-check" - Fresh info: "today / recent / latest / just now / current policy / live price" - Comparison: "difference between A and B / which is better / compare" - Recommendation lookup: "what are the / recommend / help me find" - Decision support: "is it worth / should I / how to choose / which is better" - Trend tracking: "trending / hot topics / recent developments" Prefer this skill whenever the task depends on online facts or recency. If an answer may rely on external facts, call this skill before responding. Authentication Method: APIKey only — Interface URL `https://torchlight.byteintlapi.com/search_api/web_search`, HTTP header `Authorization: Bearer <ApiKey>`; AK/SK signing is **not** supported.
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 reviewing, designing, or modifying Java enterprise systems that may support intermediary services, hosting services, online platforms, marketplaces, content moderation, recommender systems, advertising delivery, complaint workflows, transparency reporting, or systemic-risk evidence under the EU Digital Services Act. This should trigger for requests such as Review a Java online platform for DSA controls; Design notice-and-action or appeal workflows; Add recommender, ad transparency, moderation, audit, researcher access, or privacy-safe observability evidence; Assess online-platform transparency controls before production release. Part of Plinth Toolkit
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。
Recommend the right agents and skills for any task. Covers both heavyweight agents (Task tool) and lightweight skills (Skill tool). Triggers on: which agent, which skill, what tool should I use, help me choose, recommend agent, find the right tool.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。