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Search the public web and crawl URLs via the Unique AI Platform's `unique-cli web-search` command, with automatic per-turn citation tracking so cited web facts render as clickable external reference chips and `<sup>N</sup>` footnotes on the Unique platform. ALWAYS use this skill when the user asks to search the public web, research a topic online, look up news/articles/regulations, fetch the contents of one or more URLs, or perform any "two-phase" web research (search → review snippets → crawl selected URLs for full content). Routes through the Unique AI Platform's `/web-search-api` endpoints, so the engine (Google, Brave, Tavily, Jina, Firecrawl, …) and crawler are resolved server-side from the current environment — you never need to manage engine API keys yourself. Use this instead of fabricating answers from training data whenever fresh / source-cited information is needed. Do NOT use this skill for the internal knowledge base (`unique-cli search`) or for arbitrary tool execution (`unique-cli mcp`).

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。

ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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。

Delete local git branches whose remotes are gone, after verifying via gh CLI that each was actually merged.

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。

Structured code review through the shared OpenClaw agent-skills installation.

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 whenever SQL, ORM/jOOQ code, schema or migrations, indexes, N+1, or database load are in play, even without the word "review" — delegates the performance review of the current branch diff to the sql-performance-reviewer agent.

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 after a backend build succeeds or whenever authentication, authorization, actor/identity handling, external service calls (IdP, JWKS, DB, clock), logging, observability, configuration or error contracts are in play — delegates to the backend-design-reviewer agent for a design-quality review of the current branch.

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 independent tasks benefit from parallel work without shared state or sequential dependencies

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。

The default code-driven browser path for interactive browsing work: drives any browser from code with token-efficient a11y snapshots (57% smaller via compactSnapshot), ref-based clicks, coordinate control (CUA), viewport pinning, CAPTCHA solving (reCAPTCHA, Turnstile, hCaptcha, slider, OCR), Chrome MV3 APIs (tabs/bookmarks/history/downloads/topSites), network snoop (read API JSON instead of DOM), flight-recorder trace (jsonl + HAR + screenshots), OOPIF-aware snapshots, dialog policy, human handoff for login/OTP, device emulation, request routes, stealth via CloakBrowser as the default owned engine with zero exposed CDP ports, and an attached engine that drives the user's own signed-in browser through BrowserSkill (connectBrowserSkill, bskSnapshot, one-click onboarding via bskOnboard). MUST USE for interactive browser work: scraping, blocked/WAF/JS-rendered pages, logins, the user's logged-in sites and open tabs, extension popups, form filling, screenshots, web QA, CAPTCHAs. The ulw-research browsing lane runs on ultimate-browsing, not this 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。

Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.

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。

QA テストスイートの解析・前提条件解決・実行計画策定を行うフェーズスキル。ios-qa-workflow から参照される。「テスト計画」「QA 準備」「テストスイート解析」などのキーワードで自動適用。

Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.

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。

Install and wire a Telegram MCP server (chigwell/telegram-mcp, MTProto user account, not a bot) so Claude Code, Claude Desktop, Codex or any MCP client can read your chats and send replies. Use when connecting an agent to a personal Telegram account, applying the kit patches (shared SSE daemon, search_dialogs, multi-account), generating a session string, or debugging file tools that silently refuse, orphaned uv processes, database is locked, AuthKeyDuplicated or flood-wait.

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。

Manages project versioning following Semantic Versioning (SemVer) principles. Calculates increments based on changes and synchronizes version across documentation and metadata. Use when the user asks to "bump the version", "prepare a release", or when the `changelog` skill calls for a release.

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。

Social media command desk (Busabase App-in-Skill) for Kelly's Twitter/X, Facebook, and Instagram accounts (extensible to LinkedIn, YouTube, Threads, TikTok, Xiaohongshu), covering cross-platform monitoring and ECHO-based publishing. Use when the user invokes $kelly-social or /kelly-social, wants cross-platform account metrics and timelines, or needs a content calendar, draft review, engagement inbox, crisis playbook, share-of-voice, or pre-publish quality gate.

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。

Kelly Retail Intel: App-in-Skill daily industry intelligence cockpit for retail stores and consumer brands (Busabase App-in-Skill). Use when the user asks about retail, stores, promotions, merchandising, shopping trends, weather/event sales opportunities,零售, or门店 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external 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。

Kelly Restaurant Intel: App-in-Skill daily industry intelligence cockpit for restaurants, cafes, and F&B groups (Busabase App-in-Skill). Use when the user asks about restaurants, cafes, food and beverage, menus, local events, daily offers,餐饮, or餐厅 scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external 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。

Kelly Real Estate Intel: App-in-Skill daily industry intelligence cockpit for real estate and property agencies (Busabase App-in-Skill). Use when the user asks about real estate, property agencies, listings, transactions, competitor ads, client follow-up, or a地产/中介 intelligence workflow. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external 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。

Personal App-in-Skill voice-of-customer desk (Busabase App-in-Skill) aggregating user feedback from support email, Discord/Slack communities, X replies, app-store reviews, surveys, and interviews into clustered feature requests and a roadmap decision queue. Use when the user invokes $kelly-feedback or /kelly-feedback, or mentions user feedback, voice of customer, feature requests, roadmap decisions, feedback clustering, feedback triage, request dedupe, changelog replies, decline replies, or reviewing what users are asking for.

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。

Busabase App-in-Skill review queue that scores candidate SME financing deals (revenue-based/RBF-style credit) with a deterministic, fully auditable rule-based rubric — never an LLM or API call. Use when the user invokes $kelly-deal-scorer or /kelly-deal-scorer, wants to review a deal-underwriting queue, score financing candidates, compute a composite score breakdown, see a suggested revenue-share rate range, or record approve/send-back/reject decisions for a private-credit or RBF-style lending 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。

Kelly AI Newsroom: App-in-Skill daily industry intelligence cockpit for AI/news-source intelligence (Busabase App-in-Skill). Use when the user asks for AI trend sourcing, news-source monitoring, buyer-intent analysis, or a reusable industry-intelligence cockpit. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external 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。

Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.

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。

Kelly Beauty Intel: Busabase-backed App-in-Skill daily industry intelligence cockpit for beauty, wellness, and medical aesthetics. Use when the user asks about beauty, medical aesthetics, wellness, treatments, competitor offers, IG/Xiaohongshu content,美容,医美, or health-service sales scenes. Prepares news/source signals, buyer-intent interpretation, approved sales actions, and channel drafts for review before any external 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。

Busabase-backed App-in-Skill CRM operator for contacts, companies, deals, and agent-drafted follow-ups in a reviewable follow-up queue. Use when the user invokes $kelly-crm or /kelly-crm, mentions CRM, pipeline, contacts, companies, deals, follow-ups, relationship management, meeting-note capture, outreach drafts, next steps, or wants to review/approve agent-drafted follow-up messages before they are sent through other channels.

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。

Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live 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。