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概要と使いどころ
Use when building React 18+ applications in .jsx or .tsx files, Next.js App Router projects, or create-react-app setups. Creates components, implements custom hooks, debugs rendering issues, migrates class components to functional, and implements state management. Invoke for Server Components, Suspense boundaries, useActionState forms, performance optimization, or React 19 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。
Integrates with Atlassian products to manage project tracking and documentation via MCP protocol. Use when querying Jira issues with JQL filters, creating and updating tickets with custom fields, searching or editing Confluence pages with CQL, managing sprints and backlogs, setting up MCP server authentication, syncing documentation, or debugging Atlassian API integrations.
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。
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost optimization, disaster recovery, landing zones, security architecture, serverless design.
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 modifying an already-installed workspace capability — adding or editing its MCP servers, skills, integrations, or agent guidance. Use when the user wants to customize, change, or extend an existing capability.
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。
Add or update workspace MCP servers using holaOS mcp_registry syntax.
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 Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
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 to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
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 launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, bounded blocking polls, platform-specific process control via terminal_job_manage capabilities, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start, or right after terminal_exec auto-backgrounded.
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。
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs. new-session composer, terminal strip vs. agent terminal, all twelve native harnesses). Covers the web UI, native harness terminals, and the CLI.
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。
Spin up a live local Omnigent server and exercise the GitHub Copilot SDK harness end-to-end — build copilot agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the copilot harness (omnigent/inner/copilot_executor.py, copilot_harness.py, omnigent/onboarding/copilot_auth.py) or its auth / model / tool-bridge behavior.
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。
Spin up a live local Omnigent server + runner and exercise the native Antigravity (agy) TUI harness (antigravity-native) end-to-end — launch the real `agy` CLI via `omnigent antigravity`, drive turns through the web UI, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity-native harness (omnigent/inner/antigravity_native_executor.py, omnigent/antigravity_native.py, antigravity_native_bridge.py, antigravity_native_rpc.py, antigravity_native_reader.py, antigravity_native_launch.py) or its agy launch / RPC mirror / tmux delivery / OAuth / MCP-relay behavior. NOT the in-process `antigravity` Gemini SDK harness.
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。
Run the Omnigent load test and produce a results file explaining the latencies. Load when the user wants to load-test / stress-test / benchmark Omnigent under concurrency ("load test omnigent", "stress test the server", "how many hosts/sessions/turns can it handle", "load test real agent turns / conversations", "run a load test"). The test makes each simulated user a real omnigent host that creates host-bound sessions and drives real multi-turn conversations with a mocked LLM; it boots its own local stack (dev/loadtest/run.py). Gather inputs, run it, then read the generated summary.md and explain the latency distribution (avg/median/p95/p99, throughput, failures). NOT for single-request latency micro-benchmarks (that is dev/benchmarks/).
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。
Spin up a live local Omnigent server and exercise the Cursor SDK harness end-to-end — build cursor agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the cursor harness (omnigent/inner/cursor_executor.py, cursor_harness.py, cursor_auth.py) or its auth / model / tool-bridge behavior.
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。
Spin up a live local Omnigent server + runner and exercise the native Pi TUI harness (pi-native) end-to-end — launch the real `pi` CLI via `omnigent pi`, drive turns through the web/bridge, smoke-test, and bug-bash. Load when developing, testing, or debugging the pi-native harness (omnigent/inner/pi_native_executor.py, pi_native_harness.py, omnigent/pi_native.py, pi_native_bridge.py, pi_native_credentials.py) or its bridge / extension / auth / model behavior.
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 test the polly multi-agent coding orchestrator's critical user journeys (CUJs). Two halves — a deterministic mock-LLM driver (polly_cuj.py) that boots a throwaway local server + mock LLM and asserts the substrate (boot, bridged sys_* tool dispatch, the blast_radius / spawn_bounds / headless_subagent_purpose_guard guardrails, fan-out delegation), and a live real-CLI recipe (real claude/codex/pi, real worktrees/PRs) for polly's actual judgment. Load when developing, testing, or debugging examples/polly — its config.yaml, the claude_code/codex/pi sub-agents, the investigate/fanout/cross-review skills, or the omnigent.inner.nessie.policies guardrails — or reproducing a polly orchestration bug.
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。
Spin up a live local Omnigent server and exercise the Antigravity (Gemini) SDK harness end-to-end — build antigravity agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the antigravity harness (omnigent/inner/antigravity_executor.py, antigravity_harness.py, omnigent/onboarding/antigravity_auth.py) or its auth / model / tool-bridge behavior.
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, modify, debug, migrate, review, or verify TypeScript MCP servers and MCP Apps with mcp-use. Use for tools, resources, prompts, middleware, Views, authentication, Skills over MCP, scaffolding, and advanced 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。
Build, modify, debug, migrate, review, or verify TypeScript MCP servers and MCP Apps with mcp-use. Use for tools, resources, prompts, middleware, Views, authentication, Skills over MCP, scaffolding, and advanced 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。
Build, modify, debug, migrate, review, or verify TypeScript MCP servers and MCP Apps with mcp-use. Use for tools, resources, prompts, middleware, Views, authentication, Skills over MCP, scaffolding, and advanced 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。
Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser and server events, offline conversions, and platform reconciliation. Use for attribution audit, attribution models, conversion windows, requests to add or total Meta and Google conversions, incompatible reporting-window aggregation, GA4 attribution, MMP review, AppsFlyer, Adjust, Branch, Singular, or cross-platform discrepancies.
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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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。
Generate game assets with fal (fal.ai) through the fal MCP server, the `um fal` CLI (REST) or `fal api`. Covers sprites and icons with transparent backgrounds, consistent variants and animation frames, pixel art, seamless textures, PBR material maps, image-to-3D models, remeshing, auto-rigging, sound effects, music, voice lines, and trailer or cutscene video. Use whenever a mod needs new art, audio or 3D models, or the user mentions fal, generating sprites, textures, models, SFX or music for a game.
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。
Read how a game actually works so a mod can hook it. Decompile .NET/Mono (ILSpy), IL2CPP (Cpp2IL, Il2CppDumper), Java (Vineflower) and native code (Ghidra, IDA, via MCP servers); dump data and asset formats; reverse-engineer a binary file format and prove it with a round trip; read live memory (Cheat Engine, Frida, x64dbg); find render passes (RenderDoc). Use when a mod needs game internals, e.g. "how does the boss AI work", "find the damage function", "what format is this .sld/.pak/.dat", "where is the player position in 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。
Daemon process management — start, monitor, restart, stop long-running services. ALWAYS invoke this skill when the user asks to start, check, restart, or stop background services, dev servers, or daemon processes.
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。