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概要と使いどころ
Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.
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。
Document datasets, variables, sources, and merge keys for replication
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。
Experimental workflow for collecting and triaging product feedback, product telemetry, runtime errors, and issue reports. Use when scanning configured signal sources and applying separate fix, reply, or close rules.
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。
Experimental workflow for configuring an autonomous software delivery factory. Use when choosing feedback sources, schedules, worktree behavior, autonomy gates, or host automations.
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。
How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace 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。
Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` 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。
Rewrite existing pages so AI assistants can extract, quote, and cite them. Claude restructures your content into answer-ready blocks — direct answers, comparison tables, stats with sources, schema — without wrecking the human reading experience. Platform: AI visibility.
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 the exact pages AI assistants cite when answering questions in your category, and identify which ones you can get into. Claude clusters the cited sources, scores each by attainability, and outputs an outreach/content plan. Platform: AI visibility.
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 the built-in assets of the Unity Editor (located at Resources/unity_builtin_extra). Filters by name and/or type; built-in assets have no GUID so GUID-based lookups are not supported.
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 Unity's package registry plus locally installed packages (Git, local, embedded sources) by query string. Returns available versions and installation status. Online mode fetches exact matches from the live registry then supplements with cached substring matches.
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 or improve BB skills, including their triggers, instructions, and supporting resources.
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。
URL validation and content sanitization for untrusted sources — use when handling external input safely
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。
Thorough research across multiple sources — use for complex topics needing broad synthesis
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 deep research through DeepAPI and save a report with sources. Use when asked for deep research, DeepAPI research, or Perplexity deep research.
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 sourced JSON dossiers on people, companies, or topics with DeepAPI. Use for profiles, prospects, vendor due diligence, or customer research across sources; use deep-research for recommendations.
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 and inspect external sources for product, design, technical, or comparative research; verify provenance and turn findings into decisions. Use for requests to investigate online, research X posts or demos, compare approaches, or deepen a research task. Complements a research orchestrator without taking over its state or stop conditions.
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, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.
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。
持久战略：面对长期、复杂、短期不可能速胜的任务，客观评估态势，划分战略防御 / 相持 / 反攻三阶段，判定当前所处阶段，设置阶段转换条件，并在困难阶段仍给出局部进攻点。当任务跨多个会话或多轮迭代、资源暂时不足、或出现"想放弃"与"一把梭"两种情绪时触发；任务能在当前会话完成时不触发。 English: Protracted strategy. For long-horizon work that cannot be won quickly, assess the situation objectively, divide the effort into defense / stalemate / counter-offensive stages, name the current stage and its exit condition, and always identify a local offensive even while accumulating. Trigger when work spans many sessions or iterations, resources are temporarily short, or you notice either defeatism or a rush to finish; skip when the task fits in the current session.
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 published research and institutional views with sources, dates and related assets. Use when the user asks to research reports with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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。
Extract report views, ratings, key assumptions and risks with sources and dates. Use when the user asks to research report breakdown with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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 LazyCodex and Codex CLI installation health against the latest sources. Use whenever the user asks for a doctor or health check, says LazyCodex, lazycodex-ai, omo-codex, or Codex behaves oddly after an install, update, or config change, suspects a stale, drifted, or broken setup, or wants the local install audited and compared with the latest LazyCodex and Codex code.
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。
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
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 converting academic papers into dissemination assets such as Paper2Web websites, Paper2Video video abstracts, or Paper2Poster conference posters from LaTeX or PDF sources.
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。
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
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。