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概要と使いどころ
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
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Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
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Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; PostgreSQL/MySQL CDC in PuPr) into Unity Catalog with serverless pipelines.
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Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
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Identify EMERGING trends by connecting dots across unrelated sources. Monitor niche communities, academic research, GitHub, patents, funding, regulatory changes. Predict what will trend in 3-6 months based on weak signals.
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Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
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Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.
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Maps the full customer journey from first touch to advocacy. Generates a comprehensive customer-journey.md with all stages, touchpoints, emotions, pain points, opportunities, Mermaid diagrams, and metrics. Use when mapping customer experience, designing onboarding flows, identifying churn risks, or optimizing conversion funnels.
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 a manuscript needs a copy-edit for consistency and non-native English clarity. Flags abbreviation, US/UK spelling, en-dash range, P/p, hyphenation, number-style and unit-spacing issues, then polishes style only. AI-tell removal is /humanize.
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 checking a radiology or medical AI study design before drafting or submission. Reviews the analysis unit, cohort logic, leakage risks, comparator, validation strategy and reporting-guideline fit. AI-vs-expert benchmarks are /design-ai-benchmarking.
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。
Scans HN, Lobsters, Reddit, and tech blogs for community experience reports. Use when gathering practitioner opinions on a technology or approach.
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。
Converts a Claude Code session into a blog post, case study, or Reddit post. Use when publishing dev blog content or community posts from real sessions.
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。
Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.
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 Brian2 for clock-driven spiking-neural-network modeling, simulation, synaptic connectivity, input generation, recording, physical-unit equations, spatial neurons, runtime code generation, and C++ standalone 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。
Use Astropy core for astronomy units, coordinates, times, tables, FITS/WCS files, modeling, statistics, visualization, cosmology, configuration, and public CLIs.
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Same pixels, fewer tokens — for user-facing deliverables where visual polish is the spec. Use when building or editing a landing page, marketing site, hero, pricing/feature section, dashboard, or any HTML/CSS UI component. Keeps the full rendered design (layout depth, hierarchy, motion, responsive richness, a11y) and cuts tokens by expressing that design densely — CSS custom properties, shared classes, shorthand, fluid units — instead of by cutting the design. The honey core trims code and prose; this trims how the design is *written*, never how it looks. Reach for it whenever output is user-facing markup, even if the user never says "minimal".
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。
Same pixels, fewer tokens. For user-facing markup: keeps the full design, expresses it densely with CSS vars, shorthand, fluid units.
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 evaluating, designing, or pressure-testing the business model of an AI agent product. Triggers on "agent business model", "agent economics", "agent canvas", "evaluate agent product", "agent pricing", "agent unit economics", "agentic business", "AI agent company", "agent cost 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。
Folk horror art direction for Blender with rural ritual uncanny, pastoral daylight dread, and community-wrongness storytelling.
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。
Go outside the founder brief and research the real market before the plan is written: the exact communities, newsletters, and venues where the user's ICP already sits, how comparable products launched and what worked, the problem in strangers' own words, and the rivals and openings the user never mentioned. Saves docs/gtm-cofounder/market-research.md with a link for every finding. Use right after start-here and before strategy-and-roadmap, before picking channels in first-50-users or planning launch-it, or whenever the plan feels like it only repeats what the user already said.
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Plan a developer launch (Show HN, Reddit, Product Hunt) that earns goodwill instead of a flaming. Use when the user is sitting on a launch out of fear, wants to "go viral," or is about to post a press-release-style announcement to a developer community.
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Deeply research technical architecture, source code, mechanisms, SDKs, frameworks, project comparisons, and system-design options across repositories, history, official docs, issues, discussions, and high-quality community signals. Use when the user asks for deep research, source-level understanding, architecture comparison, mechanism satisfaction, or evidence for designing a system. Always map the question and obtain confirmation before beginning substantive research; skip for simple factual lookups or straightforward implementation tasks.
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Break a trusted implementation plan (or other provided context) into independently-grabbable, atomic work items, written to a single work-items.md file. Use when the user wants to convert a plan into work items, create implementation tickets or tasks, divide a plan into work units, or break the plan down into grabbable pieces. Do not use when there is no implementation plan yet or the plan is not yet trusted — use plan-implementation to produce the plan or iterative-plan-review to harden it first. Does not sequence work into demoable delivery phases — use plan-a-phased-build for that. Does not write code — use tdd to implement a work item.
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。
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
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。