「fact」の検索結果

ScalarDB Cluster および ScalarDB Analytics のアーキテクチャ、サイジング、構成を見積もるスキル。 性能要件、可用性要件、クラウド環境からScalarDB Cluster Pod数、Kubernetes構成、 バックエンドDB、API Gateway、監視システム等の全体構成を見積もる。 ScalarDB Analyticsを使用する場合はEMR/Databricksのサイジングも含む。 使用タイミング: - 「ScalarDBのサイジングを見積もりたい」「ScalarDB環境を構築したい」 - 「ScalarDB Clusterの構成を決めたい」「ScalarDBの費用を算出したい」 - 「開発/テスト/ステージング/本番環境のScalarDB構成」 - CI/CD、Blue/Green、Canary Deploymentを含む本番環境設計 - 「ScalarDB Analyticsを使いたい」「分析クエリ環境を構築したい」 - 「EMR/Databricksのサイジングを見積もりたい」 出力: Markdown形式の見積もり結果 + HTML形式のレポート 費用: USD/JPY両建て（為替レート明記）

レポートコンパイルエージェント - 分析結果のMarkdownファイルを統合HTMLレポートに変換。/compile-report [出力パス] で呼び出し。

GraphDB探索エージェント - RyuGraphデータベースを自然言語またはCypherで探索し、関連するコードや仕様書を返却。/query-graph [クエリ] で呼び出し。

GraphDB可視化エージェント - RyuGraphデータベースの内容をMermaid/DOT/HTML形式で可視化。/visualize-graph [出力パス] で呼び出し。

Implement, debug and review code with focused checks. Use to fix a bug or failing test, refactor, remove code, perform a code review, or plan engineering work in repository records. Combine with UI for interface concerns and Plan for its native records. Excludes conceptual questions unrelated to a codebase.

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Resolve complex or high-impact problems when the cause, solution path, requirements, or proof boundary is genuinely uncertain. Use when work needs evidence-led diagnosis, consequential design or implementation, or a completion audit; do not trigger merely because work has multiple steps, or for simple facts, fully specified routine changes, or pure creative generation.

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Find live third-party GitHub issues and discussions on topics where you have real hands-on experience and artifacts, then reply with that experience to build reputation. Use when asked to answer people about your work, find threads matching your expertise, or reply with evidence from your own setup.

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Close a work session properly: reconcile it against the wider collaboration, inventory what was actually built from git log and recently-touched files, summarize the arc, classify each artifact as keep, one-off or promote-to-permanent, route the durable ones to memory, canon or a new skill, save the retro as a vault note, and hand back an enriched /compact block so working memory shrinks without losing the thread. Triggers: "/retro", "/rr", "wrap up the session".

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Gate for any outbound message before it leaves: pull request or issue comment, DM, invite, post, email. Use before sending to check facts, tone, platform rules, registry status and AI-slop, and to block text that fails.

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Drive NotebookLM over a programmatic CLI with no browser: turn a vault slice into an audio overview, study guide or mind map, and fold the artifacts back into the vault. Browser automation is the fallback if CLI auth breaks. Triggers: "/notebooklm", "make an audio digest of <topic>", "podcast from the vault".

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Measure a target repository's queue and find a live open issue the contribution would close, before opening a pull request there. Reports how many PRs are open, how many of the last N closed ones were actually merged, and which issue to attach to, because a cold PR into a dead queue is a wasted artifact. Triggers: "/issue-match", "is this repo worth a PR", "measure the queue".

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- Verify whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned

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Researches what prompts people ask AI engines (ChatGPT, Gemini, Perplexity, Claude) about a product category and produces a prompts.csv artifact — a prioritized, strictly-schema'd list of the queries where the brand should be cited. Feeds the monitor workflow.

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Rewrite machine-written text so it reads like a busy human wrote it: cut 50-70%, break the even rhythm, drop AI-tell words and filler, keep facts and protected terms. Use before publishing any post, reply or email drafted by a model. Triggers: /ai-slop, /slop, humanize, anti-slop, make it shorter and alive.

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Pull new and changed claude.ai web conversations into an Obsidian vault, extract artifacts as first-class notes, concept-link them, and refresh the RAG index and dashboard. Idempotent: curated notes are never overwritten. Triggers: "/claudeai-sync", "pull claude.ai", "grab new claude.ai chats".

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Build a dashboard from idea to live link in one pass: create the visualization, file it in one shared catalog, cross-link it with the others, publish to GitHub Pages and return the working URL right away. Use when results carry more than one fact to decide on. Triggers: /dash, build a dashboard, show it visually, publish the dashboard, give me the dashboard link.

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After shipping a repo, release or feature, find who on GitHub actually needs it: search issues, discussions and catalogs for the symptom, rank live threads and draft one precise reply per consumer. Use right after any artifact goes public. Triggers: /consumer-hunt, /ch, find consumers, where to submit.

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Decide whether to reuse, improve, or scaffold a deterministic helper. Use when repeated work is slow, brittle, or blocked by a missing local tool.

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Create a detailed implementation plan before coding. Use when starting a new feature, multi-step refactor, or complex task. Invoked by saying "plan", "create a plan", or "plan this task".

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Continuous production path for deployless repositories. Use after mainline governance and release controls exist, when a repo needs CI/CD changes to automatically deploy, publish, or promote artifacts from main across any language, runtime, or hosting platform.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Register a custom Query type for JetEngine's Query Builder — extend \Jet_Engine\Query_Builder\Queries\Base_Query for the runtime query class, extend \Jet_Engine\Query_Builder\Query_Editor\Base_Query for the editor component, then hook BOTH register actions — jet-engine/query-builder/queries/register (factory) for the runtime, jet-engine/query-builder/query-editor/register for the editor UI. Five abstract methods on Base_Query — _get_items(), get_items_total_count(), get_items_page_count(), get_items_pages_count(), get_current_items_page(). Built-in cache via get_cached_data() / update_query_cache(). Custom queries participate in JE 3.8+ MCP tool exposure and the frontend query inspector automatically. Use when scaffolding a custom query type (HPOS WC orders, third-party API source, custom DB table) for JetEngine listings, dynamic widgets, or REST API endpoints.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

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Perform quick web searches, local business lookups, and URL content extraction. Covers web_search for general queries, brave_local_search for location-based discovery, and run_crawl4ai_job for deep page crawling. Use when fact-checking, trend spotting, doing competitor analysis, looking up local businesses, extracting page content, or performing content research that doesn't need multi-step deep 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。