繰り返すLLMへのAPIリクエストで、共通の指示や資料を再利用しやすく並べるスキル。キャッシュ利用量と費用、応答時間を確認し、構成の見直しを支援します。
- 繰り返すAPIの共通指示を並べ替え
- キャッシュの読み書きと保存費用を試算
- キャッシュ利用率と応答時間を測定
1.5万 件 ・ 関連度順
概要と使いどころ
繰り返すLLMへのAPIリクエストで、共通の指示や資料を再利用しやすく並べるスキル。キャッシュ利用量と費用、応答時間を確認し、構成の見直しを支援します。
Node.js、Express、Next.jsのサーバー側開発で、API設計やデータ取得、キャッシュ、認証、エラー処理を整理するスキルです。
Node.js、Express、Next.jsのAPIとデータ処理を設計・見直すスキル。役割の分離、データベース最適化、キャッシュ、認証の実装例を示します。
営業・提携・資金調達の候補者を調べて優先順位を付け、共通の知人を通じた紹介経路を探し、相手の近況と送り手の文体に合わせてメールやSNSの連絡文を下書きします。
既存コードから色・文字・余白などの共通ルールを抽出し、デザインの仕様書とプレビューを作るスキル。画面の統一感を10項目で評価し、修正箇所も示します。
処理の高速化を、現状の計測、仮説ごとの変更案の比較、正しさの検証へ分解し、時間や費用の上限内で最も速い安全な案と再実行できる手順を残すスキル。
LLMの出力をJSONや正規表現、Pydanticの型定義に合わせて生成するスキル。文章からの情報抽出や分類結果を、後続の処理で扱いやすい形式に整えます。
1Passwordに保存したパスワードやAPIキーを、コマンド実行や設定テンプレートで使うためのスキル。CLIの導入、認証、秘密情報の取得・受け渡しを案内します。
プロジェクトにデータベースやSMSなどの外部サービスを用意し、認証情報を`.env`へ同期して、キーの更新や料金プラン変更まで管理するスキル。
文章から必要な情報を取り出し、Pydanticで型や値を検証するスキル。LLMの出力を決まった構造に整え、検証失敗時の再試行や途中結果の受け取りも扱います。
Polymarketの予測市場を検索し、出来事の見込みを価格に基づく割合で示すスキル。取引量、売買注文の一覧、価格履歴を取得して、市場の動きを調べられます。
The technique catalog: five velocity-matched SEAMS (zoom-through, INVERSE zoom-through, cut-the-curve, waterfall cut, rack-focus blur-cut) plus the two in-scene techniques — waterfall ENTRY (staggered arrival cascades for title cards / segment openers) and the nudge curve (slow-fast-slow three-phase group slides). Covers partial-travel (~12% of frame) velocity matching via mirrored power4 eases, the Z scale-sign rule, size-scaled blur (10px text / 18-20px full-frame), word-by-word staggered cuts, cascade pacing by element weight, and the 10/65/25 slide ratio. Read before authoring any transition, text-beat handoff, kinetic text entry, or group reposition. [depth, zoom, inverse-zoom, scale-sign, mirrored-zoom, rack-focus, pacing, velocity, cut-the-curve, waterfall, stagger, cascade, kinetic-text, title-card, segment-opener, nudge, slide, easing, group-motion, z-depth, motion-graphics, cinematic, transition, blur, directional-continuity]
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。
Prepares and structurally reviews readiness evidence for ISO management-system and laboratory-competence standards - ISO 13485 medical device QMS, ISO 14971 device risk management, ISO/IEC 17025 testing and calibration laboratories, and ISO 15189 medical laboratories. Use when organizing declared scope, controlled documents, risk-management files, scope of accreditation, traceability, CAPA, external-provider controls, or bounded local evidence manifests, and when separating ISO certification from laboratory accreditation, FDA QMSR inspection, CLIA certification, MDSAP, and EU MDR/IVDR evidence boundaries. Not for legal applicability, compliance, certification, or accreditation decisions; contains no clause text.
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。
Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical 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。
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
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 LSASS credential dumping, SAM database extraction, and NTDS.dit theft (e.g. via Mimikatz) using Sysmon Event ID 10 process-access logging, Windows Security logs, and SIEM correlation rules. Use when hunting for credential-theft activity on Windows/Active Directory hosts or triaging EDR alerts on LSASS access.
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。
Plants Canarytokens-based decoy artifacts (honey credentials, DNS tokens, web-bug URLs, AWS keys, documents, kubeconfigs) using Thinkst's open-source Canarytokens project and alerts via email or webhook when a token is touched. Use for high-fidelity intrusion detection in low-telemetry areas like file shares or credential stores, or to catch credential dumping and data-theft staging.
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 Microsoft's Enhanced Security Admin Environment (ESAE) tiered administration model for Active Directory, covering Tier 0/1/2 separation, privileged access workstations (PAWs), administrative forest design, and authentication policy silos. Use when designing or hardening AD privileged-access architecture, segmenting Domain/Enterprise Admin accounts into tiers, or containing lateral movement and credential theft (pass-the-hash, Kerberoasting, golden tickets).
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 and harden a Sliver C2 team server (BishopFox's Go-based adversary emulation framework) with multi-protocol listeners (mTLS, HTTP/S, DNS, WireGuard), redirectors, domain fronting, and multi-operator support for authorized red-team operations. Use when standing up resilient C2 for a red-team engagement or generating beacon/session implants that must survive blue-team detection.
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 and configure the Havoc C2 framework (teamserver, HTTPS/HTTP/SMB listeners, Nginx redirectors, and Demon agents) with malleable traffic profiles and OPSEC-hardened infrastructure for authorized red team operations. Use when standing up or hardening Havoc C2 infrastructure for a written, authorized adversary emulation engagement.
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。
Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).
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 changing shared upstream-owned files to add Kilo-specific behavior, editing `kilocode_change` markers in shared code, or moving additive behavior out of shared code to reduce upstream merge conflicts. Do not use for changes confined to Kilo-owned paths such as `packages/kilo-vscode/` or `packages/kilo-ui/`.
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 an n8n-mcp account targets more than one n8n instance — i.e. the `n8n_instances` tool is available, the user mentions multiple n8n instances or environments (prod vs staging, several teams or clients), a workflow / datatable / credential / execution call returns an unexpected NOT_FOUND or reads data you don't recognize, or a credential create/update/delete is refused with an `INSTANCE_AMBIGUOUS` error. Covers choosing and switching which instance this MCP session targets, verifying the target before high-stakes work — credential writes above all — and recovering from misroutes and ambiguous-write fail-closes. Always consult this skill before operating on a specific instance, before any credential create/update/delete on a multi-instance account, or when a call hits the wrong/empty data or an `INSTANCE_AMBIGUOUS` error.
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。
Provides recommendations for environmental sustainability, carbon footprint reduction, and energy efficiency based on the Sustainability pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks to assess, design, or optimize Google Cloud workloads for sustainability—including the shared responsibility model, selecting low-carbon regions (CFE%), reducing resource and AI/ML energy waste, designing efficient software and storage lifecycles, or measuring and tracking emissions using Google Cloud Carbon Footprint. Don't use for financial cost reduction (use google-cloud-waf-cost-optimization), latency and throughput tuning (use google-cloud-waf-performance-optimization), or high availability and disaster recovery (use google-cloud-waf-reliability).
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。