「skill discovery」の検索結果

Design consumer ordering experiences — discovery, cart, checkout, tracking, and reorder flows that convert.

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。

ChIP-seq analysis — peak calling, differential binding, motif discovery, and QC with phantom-peak metrics.

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。

Deploy cloud access security brokers — shadow IT discovery, SaaS governance, and cloud data protection.

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Optimize content for AI answer engines (AEO/GEO): citation-friendly structure, factual precision, and measuring AI visibility. Use when AI search drives discovery.

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 the user provides a Facebook URL or asks to read personal posts, comments, reactions, friends, timelines, profiles, stories, feeds, groups, events, or saved items, or to publish personal text posts, comments, and replies, or to discover public events happening near a place, nearby, or in a local area on a date, or to create, edit, publish, or delete their own Marketplace listings. To find, browse, or buy Marketplace listings, use shopping instead. Use pages commands for managed Facebook Page discovery, insights, native draft editing/deletion, same-draft publication, approved posts and native scheduling.

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Autonomous competitive analysis and feature discovery pipeline. Runs /compete and /new-features on each project, produces actionable market positioning insights. Schedule daily or weekly.

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Full-spectrum product research pipeline. Runs competitive analysis, technology trend scouting, user feedback analysis, and feature ideation. Triggers: on: research, competitive research, market research, feature discovery, what should we build next.

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Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.

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Audit and fix agent readiness: the Lighthouse Agentic Browsing fraction, accessibility tree for agents, robots.txt and Content-Signal for AI agents, WAF treatment of agent traffic, llms.txt, Markdown delivery, ai-catalog.json, /.well-known discovery files, and WebMCP tools. Exclude AI citability and brand signals (seo-geo) and commerce protocol depth (seo-ecommerce).

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。

Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for source-grounded research context. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".

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Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.

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。

Find every LLM workflow in the current repository and label it, so Caveman Cloud groups spend by what the code actually does (support-reply, nightly-digest) instead of one anonymous bucket. Use when the user pastes the Caveman discovery prompt, says "discover workflows", or asks to break LLM spend down by workflow. The repo should already route through the Caveman gateway (the caveman-setup skill does that part).

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 verified Salesforce research packet into a concise, role-aware learning brief with clear objectives, concept sequencing, worked examples, release caveats, checks for understanding, and citations mapped to claims. Trigger keywords: teach me Salesforce, explain Salesforce concept, create learning brief, Salesforce study guide, role-based lesson. NOT for open-ended source discovery or freshness verification — use architect/salesforce-learning-research first. NOT for generating production metadata or changing an org.

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Eliciting, documenting and structuring requirements for a Salesforce implementation: stakeholder discovery interviews, As-Is and To-Be process mapping, and gap analysis against standard Salesforce capability. Trigger keywords: requirements gathering, user story, As-Is To-Be, gap analysis, stakeholder interview, process mapping, business requirements, fit gap. NOT for authoring the user stories themselves - use admin/user-story-writing-for-salesforce. NOT for scoring requirements against a specific org - use admin/fit-gap-analysis-against-org. NOT for technical design decisions - use architect/solution-design-patterns. Also covers the requirements catalogue itself (one row per requirement, MoSCoW priority, source stakeholder, downstream target) and Salesforce non-functional requirements: record volume, sharing layer, integration pattern, reporting joins, mobile/offline, and licence implication.

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Documenting As-Is and To-Be business processes for a Salesforce project as swim-lane diagrams — one lane per actor, explicit decision points, parallel and exception/sad paths, and per-step automation-tier annotations that drive downstream build decisions. Trigger keywords: As-Is To-Be salesforce process, swim lane salesforce, business process map, automation candidate identification, sad path documentation, process actor mapping, manual residue. NOT for Opportunity stage and exit-criteria design — use admin/sales-process-mapping. NOT for picking Flow vs Apex for one requirement — use admin/process-automation-selection.

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Run a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.

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Use this skill when the user is working with an Aspire distributed application and needs to operate the AppHost or its resources through the Aspire CLI: start, restart, stop, or wait on the app; inspect resources, logs, traces, docs, or health; add integrations; manage secrets or config; publish, deploy, or rerun a named pipeline step; initialize Aspire in an existing app; recover missing `.modules` files in a TypeScript AppHost; discover the right frontend URL for Playwright from Aspire state; expose custom dashboard/resource commands; or understand unfamiliar Aspire AppHost APIs in C# or TypeScript. Use it even if they describe the task in terms of an AppHost, resources, dashboard, existing app bootstrap, missing generated modules, Playwright URL discovery, C# API understanding, or local distributed app workflow without explicitly naming Aspire. Do not use it for non-Aspire .NET apps, container-only repos with no AppHost, or ordinary build and test tasks.

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ออกแบบ UX ตั้งแต่ discovery, IA, user flow, wireframe, prototype จนถึง usability test สำหรับเว็บและแอป

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Use this skill when creating, scaffolding, reviewing, or maintaining Python Home Assistant custom integrations, including work on custom_components, manifests, config flows, options flows, config entries, platforms/entities, DataUpdateCoordinator, integration automation triggers, diagnostics, repairs, discovery/networking, authentication, HACS-ready repositories, or Home Assistant integration quality scale compliance.

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Design and build cohesive web pages with custom visual identity — landing pages and portfolios get rich anatomy + section archetypes; any other page type (blog, about, pricing, contact, coming-soon, dashboard, admin, e-commerce, custom) is supported through generic anatomy + skeleton. Use this skill whenever the user mentions: landing page, portfolio, personal site, hero section, marketing page, product page, sales page, splash page, blog page, about page, pricing page, contact page, coming-soon, waitlist, dashboard, admin panel, e-commerce store, redesign my site, design my portfolio, build a portfolio, work showcase, hire-me page, perfect-ui, perfect landing, or asks to create any web page surface. Two tiers: special (landing | portfolio — rich treatment) and generic (everything else — universal craft toolkit). Self-contained 8-phase pipeline: vibe discovery → visual direction (color/typography/mood) → custom icon set (NO emoji, NO icon library) → AI-generated visuals → optional shader effects layer → inline plan + implement on Next.js + Tailwind + shadcn. Always proposes effect layer as optional. No refusals; outputs distinctive sites, not AI slop.

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ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability

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Operate Aavegotchi Gotchiverse player workflows on Base mainnet (8453): alchemica channeling, surveying and harvesting, crafting installations/tiles, building on parcels (equip/unequip/move/batch equip), installation upgrades, craft/upgrade queue management, and parcel access-right management. Use when interacting with Realm/Installation/Tile diamonds via subgraph-first discovery and onchain verification/execution with Foundry cast.

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 concise, actionable plans for coding tasks. Scope work, identify dependencies, and deliver atomic, ordered steps from discovery through validation and rollout.

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。

Find and rank influencers by niche, engagement, and authenticity using Xpoz. Searches Twitter, Instagram, and Reddit for active voices in any topic. Use when asked to "find influencers", "discover thought leaders", "who's talking about X", "influencer research", or "find KOLs".

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。