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概要と使いどころ
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.
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。
Runs Scenic probabilistic scenarios against a running CARLA server with the `scenic FILE --simulate --2d` CLI — the carlaChallenge1-10 traffic scenarios shipped by ScenarioRunner (`model srunner.scenic.models.model`) and by Scenic itself (`model scenic.simulators.carla.model`) — bounding the run, sampling scenes headlessly first to tell a map problem from a syntax problem, and restoring the world afterwards. Use when the user asks to "run a Scenic scenario", "run carlaChallenge", "test a .scenic file", "scenic --simulate", or reports a Scenic scenario that rejects every sample or cannot find blueprints.
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。
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
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。
Picks the model a coding agent uses when a test or browser check sends an AI call. Use when choosing a model for any test, UI check, bench, or agent run, or when a test names Opus, Sonnet, Fable, Astra, or any model by habit.
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 create AEM Workflow models on AEM as a Cloud Service. Use when creating workflow models via the Workflow Model Editor or content package XML, defining step types (PROCESS, PARTICIPANT, DYNAMIC_PARTICIPANT, OR_SPLIT, AND_SPLIT), configuring step properties, declaring workflow variables, and deploying models through the Cloud Manager pipeline.
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。
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
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。
Agent fallback for CEngineServiceMgr_GetActiveLoopClientServerMode (auto-generated, category: func). Locate CEngineServiceMgr_GetActiveLoopClientServerMode in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CEngineServiceMgr_GetActiveLoopClientServerMode, CEngineServiceMgr_GetActiveLoopClientServerMode
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。
Agent fallback for CBaseModelEntity_SetRenderAttribute (auto-generated, category: func). Locate CBaseModelEntity::SetRenderAttribute in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseModelEntity_SetRenderAttribute, CBaseModelEntity::SetRenderAttribute
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。
Agent fallback for CBaseModelEntity_DamageDecal (auto-generated, category: vfunc). Locate CBaseModelEntity::DamageDecal in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseModelEntity_DamageDecal, CBaseModelEntity::DamageDecal
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。
Agent fallback for CBaseModelEntity_vtable (auto-generated, category: func). Locate CBaseModelEntity_vtable in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseModelEntity_vtable, CBaseModelEntity_vtable
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。
Agent fallback for CBaseModelEntity_RegisteredScriptFuncs (auto-generated, category: func). Locate CBaseModelEntity_RegisteredScriptFuncs in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseModelEntity_RegisteredScriptFuncs, CBaseModelEntity_RegisteredScriptFuncs
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。
Agent fallback for CBaseModelEntity_GetScriptDescInternal (auto-generated, category: func). Locate CBaseModelEntity::GetScriptDescInternal in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CBaseModelEntity_GetScriptDescInternal, CBaseModelEntity::GetScriptDescInternal
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。
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Use when the user explicitly asks to threat model a codebase or path, enumerate abuse paths, or perform AppSec threat modeling.
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。
Jailbreak API-served LLMs using G0DM0D3 techniques — Parseltongue input obfuscation (33 techniques), GODMODE CLASSIC system prompt templates, ULTRAPLINIAN multi-model racing, encoding escalation, and Hermes-native prefill/system prompt integration. Use when a user wants to bypass safety filters, jailbreak a model, or red-team model robustness.
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 create AEM Workflow models on AEM as a Cloud Service. Use when creating workflow models via the Workflow Model Editor or content package XML, defining step types (PROCESS, PARTICIPANT, DYNAMIC_PARTICIPANT, OR_SPLIT, AND_SPLIT), configuring step properties, declaring workflow variables, and deploying models through the Cloud Manager pipeline.
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。
[BETA] Detect, review, and fix code-quality and correctness issues in an AEM as a Cloud Service project — locally, with no external services or network calls. Use whenever a user wants to check, review, assess, audit, scan, modernize, upgrade, or fix AEM Java, Sling Models, OSGi, or Maven code — for example: "check my Sling Models are implemented correctly", "review my @Inject usage", "are my Maven dependencies up to date", "scan this AEM project for issues", "modernize my Sling Models", or "fix code-quality problems". Name the files to assess, or ask it to scan the repo; it detects issues, plans, and — only when you ask — applies surgical edits on a branch or in place, then verifies with mvn compile. It recognises the intent and handles each issue type itself, reporting anything it cannot yet fix. This skill is in beta. Verify all outputs before applying them to production projects.
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 complete dark/light mode theming systems using CSS variables, Tailwind dark mode, React context, and system preference detection. Use when users request "add dark mode", "theme toggle", "dark theme", "light mode switch", or "color scheme".
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。
OpenClawのモデル通信やツール実行の不具合を、ログと再現操作で切り分けるスキル。保存済みの会話や添付資料も調べ、原因の修正と再検証につなげます。
文章をローカル環境で読み上げ音声に変換し、WAVファイルに保存します。sherpa-onnxの音声モデルを使い、クラウドに接続せずに音声を生成します。
Perl 5.36以降のコード作成・レビュー・旧コードの改修を支援します。関数の引数定義、例外処理、クラス設計、ファイル操作や依存関係管理の書き方を示します。
機械学習モデルを本番で使うために、データの仕様、再現可能な学習、品質評価、配備、監視、切り戻しを整理し、実装計画やレビュー項目にまとめるスキル。
機械学習のない既存アプリに、推薦・分類・予測などを追加するための手順を整理します。課題とデータの確認から、最初のモデルの学習・評価・組み込みまでを支援します。