「sel」の検索結果

Routes problems to appropriate mathematical frameworks using expert heuristics

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。

Chunking strategies, embedding model selection, hybrid search, reranking, eval 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。

Data structure selection, pub/sub patterns, Lua scripting, pipelining, and cluster topology strategies.

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。

Knowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.

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。

Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.

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。

Run Open Claude Code (OCC) for coding tasks and verify its CLI installation. Use when asked to use OCC, select its provider, or perform a bounded coding task through its CLI.

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。

Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.

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 dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

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。

Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.

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。

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

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。

Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.

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 form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.

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。

Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

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。

Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.

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。

Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.

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 and select character motion references, or transfer approved motion to a human reference for a later mocap workflow.

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。

Get the current date (with day of week) and time (HH:MM:SS) in a specified timezone. Auto-trigger when needing to know current time, date, or day of week — including when guessing user's location based on schedule, answering "what time is it", "what day is it", "today is what date", or any context where accurate real-time clock data is needed. Also trigger proactively at the start of conversations to ground yourself in the current time.

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。

Run a tutorial-specific quality gate and write a PASS/FAIL report for the final tutorial deliverable.

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。

Instantiate or update a workspace `UNITS.csv` from a selected pipeline and units template (deps/checkpoints/acceptance).

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。

Score an existing reader-facing deliverable and emit a deterministic PASS/FAIL scorecard plus bounded repair plan; use after synthesis, not to rewrite content.

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。

Build a final, machine-readable argument snapshot from current H3 sections and refresh section fingerprints before merge.

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。

freee-mcp / freee-sign-mcp と連携するスキル。会計・人事労務・請求書・工数管理・販売・IT管理・固定資産・業務委託管理・サーベイ・開業・人事評価・申告・サイン（電子契約）の詳細APIリファレンスと使い方ガイドを提供。freee の経費申請・取引登録・勤怠打刻・給与明細・見積書・試算表・仕訳・従業員管理・工数登録・売上管理・SaaSアカウント管理・備品管理・固定資産管理・業務委託先の企業ユーザー/部門管理・サーベイ企画/実施回の取得・開業申請用データの参照/更新・人事評価結果の取得・法人税申告データや帳票の参照・電子契約の文書管理などの操作やAPI仕様を調べたいときに使う。ユーザーが freee のデータ操作、会計処理、人事労務管理、請求・見積、プロジェクト工数管理、販売管理、IT管理、固定資産管理、業務委託管理、サーベイ、開業、人事評価、申告、電子契約について質問や操作を依頼してきた場合は、明示的に freee と言及していなくても、このスキルの利用を検討すること。サインは別途 freee-sign-mcp の設定が必要。

• freeeで経費申請や取引登録をしたいとき
• 勤怠や従業員情報を確認したいとき
• 請求書や見積書を作りたいとき

Generate HeyGen presenter videos via the v3 Video Agent pipeline — handles Frame Check (aspect ratio correction), prompt engineering, avatar resolution, and voice selection. Required for any HeyGen video generation. Replaces deprecated endpoints with v3. Use when: (1) generating any HeyGen video (via API or otherwise), (2) sending a personalized video message (outreach, update, announcement, pitch, knowledge), (3) creating a HeyGen presenter-led explainer, tutorial, or product demo with a human face, (4) "make a video of me saying...", "send a video to my leads", "record an update for my team", "create a video pitch", "make a loom-style message", "I want to appear in this video", "generate a HeyGen video", "make a talking head video". Accepts avatar_id from heygen-avatar for identity-first HeyGen videos, or uses a stock presenter. Returns video share URL + HeyGen session URL for iteration. Chain signal: when the user wants to create/design an avatar AND make a video in the same request, run heygen-avatar first, then return here. Conjunctions to watch: "and then", "and immediately", "first...then", "X and make a video", "design [presenter] and record" = always CHAIN. If the user provides a photo AND wants a video, route to heygen-avatar first. NOT for: avatar creation or identity setup (use heygen-avatar first), cinematic footage or b-roll without a presenter, translating videos, TTS-only, or streaming avatars.

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。

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。