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Found 5,545 Skills
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
Analyze community opinions from forums and comment sections. Scrapes comments from Bilibili, Reddit, or GitHub Issues, clusters them by semantic similarity, and extracts the core arguments, debates, and viewpoints. Produces a structured report showing what the community actually thinks — not just a summary of comments, but the underlying positions people hold and where the real disagreements are. Use this skill when the user wants to understand public opinion on a topic, find the main points of contention in a discussion, or do competitive/event research from community sources. Triggers include requests to "analyze comments", "what are people saying about X", "summarize the debate", "find the key arguments", "what's the community consensus", or any task involving opinion extraction from forum or comment data.
Create, refine, review, critique, or iterate on page briefings under `stardust/briefings/**/*.md` (including `_site.md`) — intent, audience, key messages, CTAs, tone, page copy (headlines, hero, section copy), imagery direction, plus site-level information architecture and multi-page content reuse maps. Sole source of truth for page copy. Independent of brand extraction: can be authored before or after `/stardust:brand`. Use when the user wants to plan pages, write briefings, define audience or CTAs, plan imagery, map shared sections across pages, when the user asks to change, refine, refactor, review, improve, polish, critique, or iterate on any file under `stardust/briefings/`, or whenever the user asks to modify a file under `stardust/briefings/**/*.md`.
Explains the intent behind source code by finding original session transcripts. Use explain with a function, file, or line of code to understand why it exists.
Query Stream data and run CLI operations against Chat, Video, Feeds, and Moderation: list channels, list calls, show flagged messages, find users, query any Stream resource. Run stream api / stream config / stream auth commands. Install the Stream CLI binary. Use when the user gives operational verbs ('list', 'show', 'find', 'check', 'query') with Stream nouns, or invokes the CLI literally.
Run a thorough, source-heavy investigation on any topic. Use when the user asks for deep research, a comprehensive analysis, an in-depth report, or a multi-source investigation. Produces a cited research brief with provenance tracking.
Use when starting a session, deciding which framework skill applies to the current task, or sequencing them across a feature. Maps the user's intent to one of the five framework skills (ai-driven-prd, init-claude-project, generate-dev-plan, declarative-design, execute-plan) and enforces the cross-skill operating behaviors. Triggers on "which skill should I use", "where do I start", "how do these skills fit together", "I have a PRD now what", "/using-agent-skills".
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.
使用SIF（搜索情报框架）数据分析ASIN的流量来源构成与曝光分布，覆盖本期/上期/新进/退出周期对比。当用户提到ASIN流量来源、流量结构分析、自然流量与付费流量占比、曝光得分拆解、周期对比、新进/退出流量词、竞品流量分析、SP广告关键词数量、品牌广告曝光、Amazon's Choice曝光、编辑推荐曝光、Top Rated曝光、视频广告曝光、自然搜索曝光比例、PPC流量来源、促销秒杀流量来源、推荐位结构拆解、ASIN traffic analysis, traffic sources, organic traffic share, ad traffic share, exposure analysis, traffic structure, period-over-period comparison, keyword churn, SIF时触发此技能。即使用户未明确提及"SIF"，只要其需求涉及分析ASIN的流量来源、曝光渠道分布、跨周期对比或竞品流量结构对比，也应触发此技能。
Use when designing Kotlin Multiplatform expect/actual or interface boundaries for platform services, native SDKs, source sets, Compose Multiplatform UI, permissions, files, settings, sensors, or platform interop.
Generate project-specific design system rules for Figma-to-code workflows. Useful for capturing tokens, naming, and lint rules in one source.