Data & AI skills

Ensure architecture diagrams are readable, consistent with text, and clear on scope, boundaries, and data flow.

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Write and improve prompts for Qwen-Image-2.1, Alibaba's open-weight image generation and editing model. Use whenever the user wants to generate or edit images with Qwen-Image / Qwen-Image-2.1 / Qwen-Image-Edit, asks for help prompting any Qwen image model, needs readable text rendered inside an image (posters, signs, UI mockups, infographics), wants to combine several reference images into one, or asks about aspect ratios, styles, or camera settings for Qwen image generation — even when they are only choosing an image model for a text-heavy graphic and have not named the model yet.

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Qwen-Image-2.1（アルババQwenのオープンウェイト画像生成・編集モデル）向けプロンプトの作成・改善ガイド（日本語版）。Qwen画像モデルで画像を生成・編集したい、画像内に読める文字を描きたい（ポスター・看板・UIモック・インフォグラフィック）、複数の参照画像を1枚に合成したい、アスペクト比・スタイル・カメラ指定を相談したいなど、Qwen画像生成のプロンプトを書く・直す場面で必ず使用。モデル名を明示しなくても、文字を多く含む画像を作るために画像モデルの選定やプロンプト作成をしている場合は使用すること。英語版スキルは qwen-image-prompt-en。

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Develops an implementation plan ready for wave-orchestration to execute. Takes the user's goal or rough plan as input, mines the codebase for facts, grills the user one question at a time for the decisions, drafts the planning/ artifacts (task DAG, wave table, worker prompt template, state file), then reviews its own plan on two axes before seeking approval. Use when the user wants to turn a feature idea, master plan, or backlog into a wave-orchestration execution plan.

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Captures web sources and compiles deterministic Markdown reference bundles with metadata and checksums. Use when asked to capture URLs, research web links, bundle search results, or archive web pages into an LLM-ready reference document.

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Builds multi-touch pipeline attribution models and generates stakeholder-ready narratives from campaign performance data. Use when analyzing channel contribution to pipeline, preparing monthly/quarterly revenue reports for leadership, diagnosing pipeline misses, modeling budget reallocation scenarios, or translating raw attribution data into board-deck language. Designed for B2B SaaS demand gen teams that report on pipeline and revenue, not leads or MQLs.

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Generates and maintains competitive battlecards from win/loss data, competitor intel, G2 reviews, and sales feedback. Use when building battlecards for a specific competitor, updating existing battlecards with new intel, preparing BDRs or AEs for competitive deals, analyzing win/loss patterns against a competitor, or responding to a competitor's product launch or pricing change. Also use when asked to "build a battlecard," "update competitive intel," "how do we beat [competitor]," "what do we say when a prospect mentions [competitor]," or "analyze our win/loss against [competitor]." Designed for B2B SaaS teams selling against known competitors in deals with multi-persona buying committees.

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Suno V6 統合スキル（V5.5 以前は退役済み）。アーティスト設定 → 歌詞生成 → 楽曲(Style/Exclude/YAML)生成の3ステップで曲を作る。 自由テキストで指示でき、足りない情報は対話で収集する。 全ての曲はアーティストに紐づく。アーティスト設定があれば歌詞・楽曲から開始。 トリガー: /suno, 曲作って, 歌詞書いて, スタイル作って, アーティスト作って, Suno prompt, Suno lyrics, Suno 用 YAML, Suno の WAV を整えて

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TypeDoc plugin that generates AI agent skills (SKILL.md) from TypeScript API documentation Use when working with agent-skills, ai, documentation, llm, skill-md, typedoc, typedoc-plugin.

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Use when creating, editing, reviewing, packaging, or organizing AI-agent skills and deciding whether a skill should be text-only, script-backed, or backed by an ML/specialist pipeline.

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Create an HTTP Ingest (webhook) capture to receive data via HTTP POST requests. Use when setting up webhooks from GitHub, Shopify, Stripe, or any JSON source. Use when user says "set up webhook capture", "HTTP ingest to Estuary", "receive POST requests to Estuary", or "webhook capture".

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Create a HubSpot materialization to write Estuary collections back into HubSpot CRM objects (Contacts, Companies, Deals, Tickets, etc). Use when setting up HubSpot as a destination for captured data. Use when user says "send to HubSpot", "materialize to HubSpot", "write back to HubSpot", or "HubSpot destination".

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Create a HubSpot Real-Time capture using flowctl to stream CRM data into Estuary collections. Use when setting up a HubSpot source for contacts, companies, deals, tickets, or other HubSpot resources. Use when user says "capture HubSpot", "stream from HubSpot", "HubSpot CDC", or "connect HubSpot to Estuary".

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Create a ClickHouse materialization to stream Estuary collections into ClickHouse tables. Use when setting up ClickHouse as a destination for captured data. Use when user says "send to ClickHouse", "materialize to ClickHouse", "ClickHouse destination", or "load into ClickHouse".

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Create a Databricks materialization to stream Estuary collections into Databricks tables via Unity Catalog. Use when setting up Databricks as a destination for captured data. Use when user says "send to Databricks", "materialize to Databricks", "Databricks destination", or "load into Databricks".

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Foundation for writing Estuary derivations — what they are, language choice (SQL/TypeScript/Python), project layout, flowctl workflow, stateless vs stateful shuffling, and universal gotchas. Read this before any other derivation-* skill. Use when user says "write a derivation", "how do derivations work", "create a derivation", "derivation setup", or before starting any transformation work.

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Create an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".

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Create an Estuary derivation for stateless filtering, field selection, and per-document field transformation. Use for WHERE clauses, projecting fields, computing new fields from existing ones, or data cleansing. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (renaming alone is usually a projection, not a derivation). Use when user says "filter-transform derivation", "derivation to filter by status", "transformation with WHERE clause", "derive computed fields", "cleanse data with a derivation", or "stateless derivation".

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Create an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".

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Create a materialization for any Estuary destination connector. Use this generic skill when no dedicated skill exists for the target system. Use when user says "materialize to", "send data to", "destination connector", or "set up materialization".

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Create an Alpaca capture using flowctl to stream stock trade data into Estuary collections. Use when setting up an Alpaca Market Data source for historical and real-time stock trades. Use when user says "capture Alpaca", "stream stock trades", "Alpaca market data", or "connect Alpaca to Estuary".

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Create a PostgreSQL materialization to stream Estuary collections into PostgreSQL tables. Use when setting up PostgreSQL as a destination for captured data. Use when user says "send to Postgres", "materialize to PostgreSQL", "Postgres destination", or "load into Postgres".

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Create a Redshift materialization to stream Estuary collections into Amazon Redshift tables. Use when setting up Redshift as a destination for captured data. Use when user says "send to Redshift", "materialize to Redshift", "Redshift destination", or "load into Redshift".

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Create a MySQL CDC capture using flowctl with binlog replication. Use when setting up streaming from MySQL, Amazon RDS MySQL, or Aurora MySQL. Use when user says "capture MySQL", "stream from MySQL", "MySQL CDC", "binlog replication", or "connect MySQL to Estuary".

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