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概要と使いどころ
Use when a carrier asks about electric trucks — Tesla Semi, Freightliner eCascadia, Volvo VNR Electric, Peterbilt 579EV, range, charging, total cost of ownership, regulatory pushes (CARB ACT, ACF), incentives, infrastructure, depot vs en-route charging, and which routes/applications are EV-ready today.
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。
Redux state for agent execution and firing agent shortcuts. Use when editing features/agents/redux/, building agent UI, creating a conversation, touching assembleRequest or NDJSON stream state, wiring agent-state selectors, adding a per-conversation capability, or triggering a shortcut from a button, menu, or code (useShortcutTrigger, launchAgentExecution).
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。
Locate CCSPlayerPawn::SetModelFromClass in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromClass" (symbol CCSPlayerPawn_SetModelFromClass). Shortlist via callers of CBaseModelEntity::SetModel inside pawn model-selection code: this variant takes the pawn's class model. The body resolves a cached model precache handle from the player class and calls SetModel. Distinguish from SetModelFromLoadout by the ABSENCE of loadout/inventory item lookups in the call chain. Trigger: CCSPlayerPawn_SetModelFromClass, CCSPlayerPawn::SetModelFromClass
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。
Find and identify the CCSPlayer_ItemServices_GiveNamedItem virtual function, the CCSPlayer_WeaponServices_Weapon_GetSlot helper, and the CBasePlayerPawn_RemovePlayerItem helper in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate GiveNamedItem by RTTI-walking the CCSPlayer_ItemServices vtable, then to locate the other two by decompiling the already-known CCSPlayer_ItemServices_GiveDefaultItems anchor and matching its callees' argument shapes and inventory-slot bookkeeping semantics. Trigger: CCSPlayer_ItemServices_GiveNamedItem, CCSPlayer_WeaponServices_Weapon_GetSlot, CBasePlayerPawn_RemovePlayerItem
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。
Find and identify the CBasePlayerPawn::CommitSuicide virtual function's vtable slot in the CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering server.dll or libserver.so to locate the self-inflicted-death handler (used by the "kill"/bot_kill console commands and related gameplay code) by RTTI-walking CBasePlayerPawn's primary vtable and reading its known slot index directly, then confirming the body matches CommitSuicide(bool bExplode, bool bForce) semantics. Trigger: CBasePlayerPawn_CommitSuicide
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 an Agent SKILL.md fallback for an existing find-XXXX finder that relies on a fragile discovery foundation — above all LLM_DECOMPILE (patterns C/D/E), which matches the decompiled shape of a predecessor function against a stored reference and breaks when a symbol is inlined or de-inlined in a way the reference does not cover. The generated fallback coexists with the preprocessor and runs only when it returns failure, recovering every target robustly by decompiling the predecessor and following the inline/de-inline boundary with semantic anchors. Use when a finder broke on a game update, or you want to durably backstop one before it does. The recipe generalizes to any finder foundation. Triggers: create agent skill fallback, add SKILL.md fallback, robust fallback for finder, backstop LLM_DECOMPILE finder, final guarantee 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。
セミナー・講義・ウェビナー・研修・実演動画をローカルで文字起こしし、内容を分析して、必要なスライド画像や動画クリップ付きのHTML／Markdown資料を作る。動画から学習資料・実践ガイドを作りたい場合に使う。映像作品の再現設計やCanvas生成は対象外。
Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.
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。
Generates Bulkhead pattern for PHP 8.5. Creates resource isolation with semaphore-based concurrency limiting and thread pool isolation. Includes unit tests.
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。
Search academic paper repositories (arXiv, Semantic Scholar) for scholarly articles in physics, mathematics, computer science, quantitative biology, AI/ML, and related fields
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。
Academic paper search across 14+ scholarly platforms including arXiv, PubMed, Google Scholar, Web of Science, Semantic Scholar, Sci-Hub, and more. Use for literature review, research discovery, and citation management.
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。
ABP Framework application layer patterns including AppServices, DTOs, Mapperly mapping, Unit of Work, and common patterns like Filter DTOs and ResponseModel. Use when: (1) creating AppServices, (2) mapping DTOs with Mapperly, (3) implementing list filtering, (4) wrapping API responses.
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 writing recommendation letters, reference letters, or award nominations for students, postdocs, or colleagues. Invoke when user mentions recommendation letter, reference, nomination, letter of support, endorsement, or needs help with strong advocacy, comparative statements, or evidence-based character assessment.
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。
Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.
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。
Analyze multi-body contact. Use when user mentions parts touching, friction between surfaces, bolt-plate contact, press fit, or assembly with contact.
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。
Uses Chrome DevTools MCP for accessibility (a11y) debugging and auditing based on web.dev guidelines. Use when testing semantic HTML, ARIA labels, focus states, keyboard navigation, tap targets, and color contrast.
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 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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。
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
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。
Comprehensive patterns and techniques for analyzing compiled binaries, understanding assembly code, and reconstructing program logic.
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 search applications with vector, hybrid, and semantic search capabilities.
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。
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.
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。
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
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。
Cut a new semver release — bump all version strings via bump-version.ts, open a release PR, and after merge tag main and push. Use when cutting a release, tagging a version, shipping a build, or preparing a deployment. Trigger keywords - cut tag, release tag, new tag, cut release, tag version, ship it.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。