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Modern Python idioms for any Python 3.10+ project: PEP 8 style, PEP 484/526 type hints, dataclasses, pathlib, enums, f-strings, structural pattern matching (match/case), context managers, exception handling, and null discipline. Stack-agnostic — referenced by every Python plugin in the marketplace. Use this skill to: - Write strongly-typed code with type hints on every function signature and class attribute. - Replace isinstance chains and magic constants with match/case and enums. - Use dataclasses or NamedTuple for value objects and data containers. - Handle resources safely with context managers and pathlib for all file paths. - Apply PEP 8 naming and structure conventions consistently. Do NOT use this skill for: - Framework-specific idioms (Django ORM, FastAPI routers, Flask blueprints — those live in framework plugin skills). - Packaging and dependency management — see python-foundation:python-tooling. - Testing patterns — see python-foundation:pytest-testing.

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Use when configuring MCU sleep/stop/standby, WFI, STM32 PWR, nRF sleep, clock gating, wake-up EXTI sources, or measuring current draw. Not for the wake ISR: use interrupts-and-exceptions-baremetal.

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Find company & contact data. Turn your agent into a prospecting platform. Get contact information, roles, tech stack, business events, website changes, intent data. Build lead lists, research prospects, identify talent. 150M+ companies, 800M+ professionals, 50+ data sources.

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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

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Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore - same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

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Extract, document, or normalize a product UI design system from code, screenshots, or brand sources. Use for visual tokens, theme variables, typography, spacing, colors, radii, shadows, component inventories, variants, states, or building a reusable UI library. Do not use for software architecture, database schemas, or generic system design.

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Use when writing, reviewing, refactoring, or modernizing CSS/Tailwind. Apply a policy-first approach: semantic tokens, explicit cascade order, static CSS, intrinsic component layouts, accessible state, measured performance, and progressive enhancement backed by current sources.

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Research a question across web and academic sources and produce a cited report.

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Answer a factual question from the web, or enumerate what exists, with sources

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把一个目标/主题/文章/截图/数据转成有观点、有节奏、有视觉约束的单文件 HTML 演示（网页 PPT、科技战报、产品拆解、行业观察、路演/发布会 deck、社媒可传播长图）。**7 套各锚定公认设计体系的视觉人格**——技术崇高派 Technical Sublime（工程冷光·技术战报/算力）/ 构成主义 Constructivism（宣言/号召）/ 信息·数据设计（Tufte·数据/图表）/ 判断震慑极简 Luxury（高端品牌/判断）/ De Stijl 新造型（标准/秩序）/ 编辑主义 Editorial（衬线·深度观察）/ 演示禅 Zen（暗墨·思想/品牌 keynote）——用户可指定，否则按内容自动判选并透明告知。token + validator 机检（P0/P1/P2）+ 32 维审计**机器强制设计一致性**；反伪造（不造数据/logo/截图/来源·示意标注）；字体本地子集；图按角色路由（默认 CSS/SVG·要图先探测生图环境）。Generates single-file HTML decks with 7 design-system-anchored visual personas, a semantic layout grammar, locked design tokens, evidence-grading, machine validation (P0/P1/P2 + 32-dimension audit), an anti-fabrication discipline, and image-generation environment detection. Use when the user wants a presentation / deck / slides / 网页 PPT / 战报 / 产品拆解 / 行业观察 / 路演 / 发布会 / 社媒长图, or asks to turn an article / screenshot / dataset into slides. Do NOT use for large data-table training material, strict legal/medical/financial compliance documents, multi-author collaborative PPT editing, plain-text summaries, requests to replicate a brand's official keynote visuals, or any request to fabricate realistic-looking data / fake logos / fake screenshots / fake sources.

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Create high-converting free resources that capture emails and build trust. Produces complete lead magnets (ebooks, checklists, templates, toolkits, quizzes) with landing page copy, thank-you page, and follow-up email sequence. Use when someone needs a list-building asset, wants to grow their email list, needs an opt-in incentive, a content upgrade, a gated download, or top-of-funnel content. Triggers on 'lead magnet', 'ebook', 'checklist', 'template', 'free resource', 'opt-in', 'grow my list', 'email capture', 'content upgrade', 'list building', 'gated content', or 'free download'. Even if someone just says 'I need something to capture emails' or 'how do I get more subscribers', this is the skill. Every lead magnet passes a 4-gate quality test before shipping.

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Reorganize label-heavy or netlist-like electronic schematics into human-readable functional diagrams with visible local wiring while preserving proven connectivity. Use for schematic readability correction, overlapping wires or symbols, visually floating connectors or support logic, functional-sheet restructuring, bus presentation, layout annotations, or multi-EDA handoff across KiCad, Altium, EasyEDA, EAGLE/Fusion, gEDA/Lepton, SPICE/netlist, and PDF/image sources.

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Use when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"

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Plan agency resources. TRIGGERS - Use when user needs help with agency-resource-planning related tasks.

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。

Investigate a topic, library, or approach before planning. Combines user context, prior knowledge, and external 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。

odu MCP server launcher — drive CI from a coding agent. `bin/serve` resolves odu via Nix and runs `odu mcp` in the cwd. See the repo README for the tools/resources and override knobs.

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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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Use when designing event-driven workflows, decoupling services, implementing fan-out patterns (one trigger, many downstream handlers), implementing idempotent event handling with IDs (24-hour dedupe window), or handling at-least-once delivery from external sources like Stripe webhooks. Covers Inngest event schema, payload format, naming conventions, IDs for idempotency, the ts param, fan-out patterns, and system events like inngest/function.failed.

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Use when operating Inngest API resources from the terminal with `npx inngest-cli@latest api`: Cloud/local run debugging, event-run lookup, function traces, function invocation, app syncs, webhooks, environments, keys, account checks, and Insights queries. Provides prescriptive command routing for agents: which CLI command to run for a run ID, event ID, app ID, function ID, Cloud environment, API key, missing ID, or potentially mutating operation. Use `inngest-cli` for dev server setup/general CLI workflows and `inngest-api` only when raw REST API v2 docs or OpenAPI fallback are needed.

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Prismatic API access patterns and GraphQL reference. Covers the two-tier access hierarchy (MCP tools → Prism CLI), CLI usage rules, GraphQL query patterns, pagination, authentication, and managing platform resources programmatically.

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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond default agent web search tools.

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Use when a set of sources, dossiers, graph records, or experiment notes must be synthesized into project understanding, evidence pressure, uncertainty, and optional strict-review graph proposals.

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。