「que」の検索結果

Quarkusアプリの認証・アクセス権限・入力検証を実装し、安全性を見直します。秘密情報の管理、パスワード保存、アクセス制限、監査ログや依存関係の点検も扱います。

• Quarkus APIにJWTやOIDC認証を追加したいとき
• 役割別の権限と所有者確認の点検
• 入力検証とデータベース検索の見直し

Perlコードの入力処理、ファイル操作、外部コマンド、SQL、Web出力を点検し、具体例とチェックリストに沿って安全な実装へ見直すスキル。

• Perlの入力検証を点検したいとき
• ファイル操作とコマンド実行の確認
• DBIのSQLインジェクション対策

リアルタイム画面やデータ配信の遅延、情報の鮮度、処理待ちを個別に測り、イベント発生から画面表示までの経路を改善して、稼働環境で結果を確かめるスキル。

• リアルタイム画面の遅延調査
• 配信データの鮮度を確認したいとき
• キューの滞留を減らしたいとき

MySQLとMariaDBのテーブル設計や遅いSQLを見直し、インデックス、更新処理、接続管理、複製の遅延を点検して、修正案と検証手順をまとめるスキル。

• テーブルとインデックスの設計
• 遅いSQLやデッドロックの調査
• 大規模な本番テーブルの変更レビュー

ReactやNext.jsで、ドラッグ操作、スワイプ、文字やSVGのアニメーションを実装します。複数段階の動きや読み込み表示まで扱い、用途に合うAPI選びも支えるスキル。

• ドラッグで閉じる画面や並べ替えリストの実装
• 単語ごとの文字表示や数値アニメーション
• SVGの線描画や円形の進捗表示

曖昧な機能要望や影響の大きい変更を、作業範囲・前提・リスクとともに整理し、期待する動作と検証方法が明確な受け入れ基準や引き継ぎ資料にまとめるスキル。

• 曖昧な機能要望を整理したいとき
• 認証変更やデータ移行の受け入れ基準作成
• 別のエージェントやチームへの実装引き継ぎ

チームの開発に合うGitのブランチ運用を選び、コミットやプルリクエストの書き方、履歴の整理、変更の衝突解消、リリース管理まで手順を整えるスキル。

• 新規プロジェクトのブランチ運用選定
• コミット文とPR説明の統一
• PR前にローカルの履歴を整理したいとき

Kotlinでデータベースの検索・更新処理を実装するためのパターン集です。JetBrains Exposedのクエリから接続管理、構造変更、テストまで扱います。

• データベースの検索・更新を実装したいとき
• データベース接続の再利用を設定したいとき
• データベース構造の変更管理

Djangoアプリの構成、API設計、データ取得、キャッシュの実装とレビューを支援します。環境別の設定分離や処理の整理、不要な問い合わせを減らす設計例を示します。

• Djangoの構成と設定の整理
• Django REST FrameworkのAPI設計
• 不要なデータベース問い合わせの削減

A multi-frame gamified mobile-app prototype — three phone frames on a dark showcase stage. Frame 1: cover / poster, Frame 2: today's quests with XP ribbons and a level bar, Frame 3: quest detail. Vivid quest tiles, level ribbon, bottom tab bar. Use when the brief asks for a "gamified app", "habit tracker", "RPG-style life app", "level-up app", "daily quests", "XP / streak app", or "ELI5-style explainer app".

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 requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".

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。

Clarify a material user-owned product decision before substantive implementation commitments. Use only when request and named/governing evidence still leave a choice that would materially change scope, platform support, user-facing surface, data/API contract, domain values, project placement, or cross-system architecture; also use when a truncated request omits such a choice. On the initial turn, inspect only named or governing requirements evidence, then ask directly without loading: prefer structured input; ask one blocking outcome question or at most three coupled questions; wait. After the answer, load the body before write or delegation. Do not use for implement/build/fix/debug/refactor work when expected behavior is settled, plan/design deliverables owned by Plan, complexity/greenfield/integration alone, agent-owned technical choices, locally discoverable facts, reversible defaults, permission to begin, or stop/no-tool turns. Never re-ask supplied facts or turn clarification into refusal.

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。

Queries the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for queues, async workers, cron jobs, delayed tasks, retry behavior, worker-only config drift, and payload-to-side-effect chains. Use when the user asks to trace a queue payload, inspect async job execution, explain worker-only behavior, follow retries or dead-letter handling, or connect an enqueued item to a later file, cache, email, or privilege-bearing side effect. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Map advanced persistent threat (APT) group TTPs to the MITRE ATT&CK framework using the attackcti Python library to query STIX/TAXII data for group-technique associations, then generate ATT&CK Navigator layer files to visualize and compare defensive coverage against adversary profiles. Use when profiling an APT group's techniques, building Navigator coverage heatmaps, or assessing technique coverage gaps against a specific threat actor.

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。

Detect sandbox and VM evasion techniques in malware samples by analyzing timing checks, VM/hypervisor artifact queries, user-interaction checks, and sleep-inflation patterns from Cuckoo or AnyRun behavioral reports. Use when a sample shows no or minimal activity in a sandbox, when a behavioral report needs review for evasion indicators, or when building detections for anti-analysis techniques.

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。

BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

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。

Writes prospect outreach and follow-up sequences that sound like the owner wrote them, not like an AI did. Learns a voice profile from the owner's own sent mail, grounds every message in something specific about that prospect, builds the follow-up sequence behind it, queues every send for approval, and logs each touch to the CRM. Runs draft-only when no mail connector is available, so the copy can be pasted anywhere. Use this whenever the owner needs to reach out to prospects or customers — including phrasings like "write this cold email," "draft a follow-up," "reach out to these leads," "what do I say to them," "I need a sequence for this list," "nudge the people who didn't reply," or "write it but don't make it sound like a robot." Reach for it whenever outreach copy is the deliverable, even for a single message.

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 correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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 optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.

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 write, improve, or build a sequence of B2B cold outreach emails to prospects who haven't asked to hear from them. Use when the user mentions 'cold email,' 'cold outreach,' 'prospecting emails,' 'SDR emails,' 'sales emails,' 'first touch email,' 'follow-up sequence,' or 'email prospecting.' Also use when they share an email draft that sounds too sales-y and needs to be humanized. Distinct from email-sequence (lifecycle/nurture to opted-in subscribers) — this is unsolicited outreach to new prospects. NOT for lifecycle emails, newsletters, or drip campaigns (use email-sequence).

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。

Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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。

Walk through a Data Subject Access Request (or deletion, portability, correction request) and draft the response — verify identity, locate data system-by-system, assess exemptions, draft the acknowledgment and substantive response letters. Use when a DSAR comes in, the user pastes an access/deletion/portability/correction request, or says "DSAR came in", "access request", "right to be forgotten", or "someone wants their data".

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 polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid.

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。