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Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.

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。

Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)

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。

Wraps every action item in the JetFormBuilder action editor with custom UI via the 'jet.fb.action.item' wp.hooks filter — the wrapper renders on top of (or alongside) the original action editor for each action and can read/write that action's settings and events array. Use to add quick toggles or panels to every action without modifying each action's own editor — e.g. a TRUE/FALSE/Always button group that drives which custom event the action responds to, a "run once per session" toggle, an inline label override, any visual shortcut over the action's persisted state. Triggers on mentions of "jet.fb.action.item", "useLoopedAction", "useActionsEdit", "useActions", "ActionItemWrapper", "ActionItemBody", "decorate every action", "per-action toggle", or "visual control over action events".

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。

Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy operations.

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。

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.

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。

Guides creation and validation of custom dotnet new templates. Generates templates from existing projects and validates template.json for authoring issues. USE FOR: creating a reusable dotnet new template from an existing project, validating template.json files for schema compliance and parameter issues, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, packaging templates as NuGet packages for distribution. DO NOT USE FOR: finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).

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。

Fix MSBuild targets that generate files during the build but those files are missing from compilation or output. Only activate in MSBuild/.NET build context. USE FOR: generated source files not compiling (CS0246 for a type that should exist), custom build tasks that create files but they are invisible to subsequent targets, globs not capturing build-generated files because they expand at evaluation time before execution creates them, ensuring generated files are cleaned by the Clean target. Covers correct BeforeTargets timing (CoreCompile, BeforeBuild, AssignTargetPaths), adding to Compile/FileWrites item groups, using $(IntermediateOutputPath) instead of hardcoded obj/ paths. DO NOT USE FOR: C# source generators that already work via the Roslyn pipeline, T4 design-time generation that runs in Visual Studio, non-MSBuild build systems. INVOKES: no tools — pure knowledge skill.

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。

The perception-by-culture lens for psycho-logic — establish HOW a specific region, country, or subculture reads a signal before you send it, because perception is culturally coded and the same move can mean the opposite elsewhere. Use when adapting a product, price, message, or campaign to a new market; when the user asks 'will this work in [country/region]', 'why does this land in X but flop in Y', 'how do people here perceive Z', 'how do we localise/differentiate by region', or is about to copy a US/global playbook into another market. Covers the cultural dimensions that move Sutherland's levers — uncertainty avoidance, individualism/collectivism, status/power distance, high- vs low-context communication, mental-accounting norms, trust locus, and taboo/sacredness — and how each rewires reframing, costly-signaling, and experience-design. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with market-recon for live local data. 日本語・中文の相談（「この地域で通用する？」「适合本地市场吗？」）にも使う。

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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。

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

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。

Expert in turning data visualizations into compelling video and animation content for social media and marketing. Activate on: data commercial, animated chart, data storytelling video, chart animation, Remotion data video, Motion Canvas, Flourish animation, D3 animation, data viz social media, dashboard recording, data narrative video. NOT for: static data visualization (use data-viz-2025), general video editing (use video-processing-editing), full video production pipeline (use ai-video-production-master).

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。

運用者が設定したTeamサーバーの更新を、指定担当のNight Watchに引き継ぎ、既存の更新機構による検証・復旧・完了確認まで進めるスキル。

• Teamを公式mainへ更新したいとき
• 失敗・延期した更新の続行調整
• データベース移行と復旧の調整

Slackの認証やメッセージ操作、OpenClawの受信・返信を検証するスキル。APIで確かめた結果と実際の画面操作を区別し、証拠と後片付けを確認します。

• Slackの認証やアクセス権の確認
• メッセージの作成・編集・削除テスト
• OpenClawの受信・返信の検証

Javaのtinystructで、端末とWebの両方から使える処理を実装するスキル。起動スクリプトの生成からAPI、データ保存、テストまで開発手順を案内します。

• 端末とWebで使える処理の実装
• 不足する起動スクリプトの生成
• JSON APIとデータ保存の実装

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

REST・GraphQL APIの不具合を、接続、認証、送受信データの順に切り分けます。再現用コマンドや回帰テストを整え、原因と修正案をまとめるスキル。

• APIの認証エラーを調べたいとき
• Postmanとコードの動作差を再現
• ページ分割や回数制限の確認

When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," "lifecycle emails," "trigger-based emails," "email funnel," "email workflow," "what emails should I send," "welcome series," or "email cadence." Use this for any multi-email automated flow. Emails avoid AI tells like 'it's not X, it's Y' reveals, 'no X, no Y, no Z' lists, and fake 'Re:' subject lines. For cold outreach and outbound sequences, see cold-email. For in-app onboarding, see onboarding.

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。

Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.

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 rolling out organizational changes without chaos. Covers the ADKAR model adapted for startups, communication templates, resistance patterns, and change fatigue management. Handles process changes, org restructures, strategy pivots, and culture changes. Use when announcing a reorg, switching tools, pivoting strategy, killing a product, changing leadership, or when user mentions change management, change rollout, managing resistance, org change, reorg, or pivot communication.

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。

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).

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。

Systematic methodology for identifying and exploiting cryptographic implementation weaknesses in real-world applications. Covers padding oracle attacks against CBC-mode ciphers with PKCS7 padding (Vaudenay's original attack through modern padbuster automation), ECB mode exploitation including block cut-and-paste and byte-at-a-time decryption, hash length extension attacks against SHA1/SHA256/MD5-based MACs using HashPump, RSA vulnerabilities including small public exponent, common modulus, Bleichenbacher PKCS1v1.5 padding oracle, and Coppersmith's method for partial key recovery. Addresses weak PRNG exploitation targeting time-seeded generators and Mersenne Twister MT19937 state recovery from observed outputs, timing side-channel attacks against comparison operations, nonce reuse in AES-GCM leading to authentication key recovery, and key derivation weaknesses including insufficient iteration counts and missing salts. Primary tooling includes padbuster, RsaCtfTool, hashpump, and PyCryptodome for building custom exploit payloads. Maps to CWE-327 (Use of a Broken or Risky Cryptographic Algorithm), CWE-328 (Use of Weak Hash), and CWE-330 (Use of Insufficiently Random Values). Emphasizes black-box identification of vulnerable implementations before transitioning to targeted exploitation.

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 you have just been primed into an EXISTING seat through a planned handover — a different agent retired and handed you the seat plus its earned context — and you need a world model of what just happened to you. Covers how a handover differs from compaction and from a fresh launch, the inherit-the-seat-not-the-identity rule, treating the handover packet as testimony you verify (and refusing stale ghost prompts, and verifying your OWN identity surfaces), why a seat is a COMPOUNDING LINEAGE (inherit the state, acquire the predecessor's epistemology, then improve and re-deposit so the next generation is better), how to query your predecessor, your duty to stream feedback and re-deposit your earned wisdom, and how to route to role-specific onboarding. NOT the driver-side handover mechanic (retiring-and-inheriting-a-seat) and NOT unplanned compaction/crash recovery (claude-compaction-restore / session-compaction-and-restore).

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。

A.I.G Scanner — AI security scanning for infrastructure, AI tools / skills, AI Agents, and LLM jailbreak evaluation via Tencent Zhuque Lab AI-Infra-Guard. Uses built-in exec + Python script, no plugin required. Requires AIG_BASE_URL to be configured. Triggers on: scan AI service, AI vulnerability scan, scan AI infra, check CVE, audit AI service, scan MCP, scan skills, audit AI tools, scan agent, red-team LLM, jailbreak test, 扫描AI服务, 检查AI漏洞, 扫描AI工具, 检查MCP安全, 审计Agent, 越狱测试.

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, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.

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。