「ols」の検索結果

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。

Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (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。

Design decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-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。

Target a specific Google Chrome user profile for browser automation via CDP. Discovers installed profiles from the Chrome Local State file, launches Chrome with the requested profile attached to a debug port, and connects agent-browser via `agent-browser connect <port>`. Use when "automate using my Work/Personal Chrome", "open site with my saved session", or "use Chrome profile named <name>". NOT for headless scraping without an existing user profile (use mk:agent-browser directly); NOT for importing cookies only (use agent-browser --auto-connect state save).

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 up-to-date library, framework, and project documentation using scripts + MCP tools (Context7, Context Hub) with intelligent fallback. Use this skill whenever the user or agent needs documentation for any library, framework, API, SDK, or internal project spec. Triggers on "docs for [X]", "how does [library] work", "find documentation", "API reference for", "look up [feature] in [library]", "latest docs", "what's the API for", "find our [internal spec]", or any request that requires current, accurate documentation rather than relying on training data. Always prefer this skill over raw WebSearch for documentation retrieval — it returns structured, context-efficient results.

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。

Browser automation CLI for AI agents using agent-browser. Use for navigating websites, clicking/filling pages, screenshots, data extraction, web app testing, exploratory QA, dogfooding, Electron apps, Slack automation, Vercel Sandbox browser runs, AWS AgentCore cloud browsers, auth-heavy flows, and long autonomous browser sessions. Prefer over generic browser tools when a fresh/tool-managed Chrome session is fine. NOT for the user's real Chrome cookies/profile (see mk:chrome-profile); NOT for reusable Playwright specs (see mk:qa-manual).

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 user wants to connect, set up, or configure Fusionaly in their AI client, or when the fusionaly MCP tools are missing. Verifies the Fusionaly URL and agent key, then adds the MCP server to Claude Code, Codex, Cursor, Gemini CLI, or another client.

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 user asks about website analytics, traffic, visitors, page views, referrers, conversions, goals, or mentions "fusionaly". Answers from a self-hosted Fusionaly instance through its MCP tools (or its SQL API), in plain English with numbers.

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。

Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.

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。

code-review-graph (CRG) MCP wrapper — token-efficient context retrieval via AST knowledge graph. Exposes 4 core tools from CRG.

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 qualified Fusion CAM setups, operations, toolpaths and quarantined NC candidates using explicit manufacturing assets and authority. Never controls physical equipment.

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。

Diagnose and optimize timing-critical Chisel RTL for FPGA implementation while preserving cycle semantics. Use for Vivado timing bottlenecks, long ready or admission paths, queue and issue selection, free-list bank mapping, priority encoders, one-hot arbitration, wide muxes, late-arriving forwarding or override data, high-fanout controls, cross-module predicates, register-boundary changes, emitted-Verilog inspection, or routed-DCP A/B analysis. Also use when a source-level simplification needs proof that it changes timing without changing architectural state.

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。

Initialize or safely upgrade a processor project's root AGENTS.md from a maintained baseline. Use when starting a processor project, adding project-level Agent collaboration rules, or comparing an existing AGENTS.md with the package baseline. This skill only handles AGENTS.md; it does not scaffold documentation, inspect or configure environments, install tools, or modify processor source.

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 UX pains and opportunities fast through competitor scans, pattern research, and web search. Surfaces how others solve the same flow, common failure modes, and conventions users already expect. Use when the user says "research this UX", "how do others do X", "competitor scan", "find UX patterns", "what's the convention for Y", or before ideating a flow. Part of the Product Design set. Uses web search/fetch 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。

Apply or adapt a dual-theme liquid glass UI board for premium dashboards, workspace launchers, settings surfaces, design-system previews, and glossy interface concepts. Use when Codex should build or restyle interface work with frosted translucent panels, aurora glow accents, pill controls, circular glass actions, blurred color blooms, and a polished light-plus-dark material system.

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。

Apply or adapt a complete dual-theme UI specification board for product interfaces. Use when Codex should build or restyle dashboard, admin, workspace, settings, or system UI with both light and dark themes, shared semantic tokens, structured component states, and a full preview board covering foundations, controls, navigation, data display, and feedback patterns.

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 and install prerequisites for the Todo app project. Use when setting up the development environment, when a user first opens the workspace, when troubleshooting missing tools, or when the user mentions setup, install, prerequisites, or environment.

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 Mastra framework guide for building agents, workflows, tools, memory, workspaces, and storage with current APIs. Use for documentation lookup, API verification, TypeScript setup, common errors, migrations, and `mastra api` CLI tasks: inspect or call resources on local, Mastra platform, Trace Intelligence, or remote servers.

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 common CLI, Kali, and shell tools to inspect web Targets, captured assets, logs, HAR/PCAP files, packages, and generated artifacts, then turn observations into Evidence, Security Signals, and ranked Hypotheses. Use when the user asks for web bug-hunting inspection, digital records, URL maps, crawling, endpoint discovery, JS bundle/package analysis, light forensics, or command-line investigation workflows.

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。

Passive, citation-grounded review of security-relevant specifications, protocols, standards, APIs, and implementations. Use when a reviewer must trace external preconditions and invariants into source code without treating a cited claim as proof of a vulnerability.

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 vulnerability research using conventional bug-hunting and pentest stage names, specialty playbooks, and a research-hypothesis-experiment loop. Use when designing a bug bounty workflow, selecting tools and strategies, building a decision tree, or turning a target/scope into a staged hunting plan.

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。

End-to-end smoke against the local rembric dev server (`pnpm run dev`, host Next.js dev on :3000). Apply when the user says "smoke", "probar contra docker", "dev:up", or after applying an OpenSpec change that touches HTTP (`apps/web/src/app/api/`), MCP tools (`packages/mcp/src/`), or DB migrations (`packages/db/src/migrations/`). Encodes bring-up, readiness verification, probe pattern and teardown — not the probes themselves.

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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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。

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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。