「vin」の検索結果

Translate or revise localized UI strings and prose while preserving meaning, terminology, placeholders, and 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。

Restructure existing code while preserving observable behavior. Use for targeted technical debt or hotspot work with characterization 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。

Check that a web page you wrote or changed actually works, by opening it in a browser and driving every control on it. Use after creating or editing an HTML page, or the JavaScript or CSS that a page loads.

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。

Get the current market value of a vehicle from its VIN using the CarsXE API. Use this when a user asks how much a car is worth, wants to know its value, or is thinking about buying/selling a vehicle.

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。

Look up registered vehicle owners and residents using the CarsXE Ownership API (Enterprise). Use this when the user asks who owns a VIN, who lives at an address, contact details for a named person, or people in a ZIP. Do not use for lien/theft checks or vehicle history reports.

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。

Look up vehicle data by Year, Make, and Model using the CarsXE YMM API. Use this when a user doesn't have a VIN but knows the year, make, and model of a vehicle and wants specs, trims, or features.

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。

Retrieve a vehicle history report from a VIN using the CarsXE API. Use this when a user wants to know a car's history — past owners, accidents, title status, odometer readings, or whether it's been in a crash.

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。

Check for active liens and theft records on a vehicle by VIN using the CarsXE API. Use this when a user asks whether a car has a lien, is stolen, or wants to verify ownership is clean before buying.

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 or update READMEs, docs, repo instructions and handoff notes, checked against source. Use when: documenting something, writing a README or leaving a session handoff.

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。

Korean prose humanizer for Codex and Superloopy that rewrites Korean text so it sounds naturally human while preserving meaning, register, facts, protected tokens, and evidence. Use when the user asks to remove Korean AI tells, make Korean copy sound human, fix 번역투, remove ChatGPT/Claude/Gemini tone, polish Korean without changing meaning, or says "AI 티 없애줘", "AI 윤문", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean", "한글 AI 티 제거", "글 자연스럽게 다듬어줘". Inspired by Korean AI-tell ideas from https://github.com/epoko77-ai/im-not-ai. Handles Korean rewriting only; not for translation, fact expansion, SEO rewriting, legal drafting, or generic proofreading.

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 starting ANY conversation involving Bethesda Game Studio modding, MO2, xEdit, or modpack curation. Bootstrap that loads the toolkit overview, lists available task skills, and enforces the hard rules of this plugin. Auto-injected by the OpenCode plugin's chat.messages.transform hook and by the hooks/ session-start chain in Claude Code and Codex.

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。

BOM (Bill of Materials) management for electronics projects — the workflow skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills around a unified BOM lifecycle. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill analyzes the schematic for sourcing gaps, recommends which distributor/fab skills to call for each gap, and writes results back as symbol properties — the agent (or user) performs the actual searches via the called skills. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "compare pricing", or "check stock".

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。

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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 and edit React Native Storybook stories using Component Story Format (CSF). Use when writing .stories.tsx files, adding stories to React Native components, configuring Storybook addons (controls, actions, backgrounds, notes), setting up argTypes, decorators, parameters, or working with portable stories for testing. Applies to any task involving @storybook/react-native story authoring.

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。

Scaffold a TanStack app with tanstack create using --framework, --build-tool, --template, --toolchain, --deployment, --add-ons, --blank, and --router-only. Covers flag compatibility, non-interactive defaults, and intent-preserving command construction.

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。

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.

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。

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI. Also the default writing standard for all agent output: 80% ASD-STE100 controlled language plus the format ladder (prose, diagram, HTML page, explainer video).

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。

Produce and continuously maintain ONE living review document for a multi-item session — a cockpit header (Progress checklist, Working folder, Context) plus per-item review cards that you approve or request changes on directly in the doc or side panel. Use whenever a session has multiple deliverables you need to review/approve (meeting-processing + planning, multi-ticket work, briefs with several drafts, any "do X, then plan/draft Y and Z"). The doc is the interaction surface: you edit it (or reply in chat) to approve/change; the agent keeps it live as work progresses.

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。

Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries

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 drafting the cover letter for an Advanced Materials submission, whose core job is to convince the editor that the work is a genuine materials advance of broad impact worthy of the journal. Argues the impact case; does not fix the science or format the manuscript.

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 preparing an accepted AAMAS paper for the IFAAMAS open-access camera-ready, covering author de-anonymization, proceedings metadata, two-column reflow of game figures and learning curves, resolving rebuttal-stage promises, registration, in-person presentation, and the public code and environment release for a multiagent paper.

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 the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

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。

Mandatory operating rules for ANY task involving KiCAD projects. Loaded when the user mentions KiCAD, schematics, PCBs, or any .kicad_* file. Prevents file corruption by routing all changes through Konnect MCP tools.

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。

Answers natural-language business questions over existing Power BI reports and semantic models through the FabricIQ MCP endpoint, discovering artifacts, resolving entities, generating the DAX itself, and returning a plain-language answer. Use when the user asks a data question, including one that says to query the model directly. For DAX the user supplies, or measures to save, use semantic-model-authoring.

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。