Claude Code Skills · page 19

Shell scripting expert for Bash, POSIX compliance, error handling, and automation

Slack workspace management and automation specialist

SQL query expert for optimization, schema design, and data analysis

SQLite expert for WAL mode, query optimization, embedded patterns, and advanced features

System administration expert for Linux, macOS, Windows, services, and monitoring

Technical writing expert for API docs, READMEs, ADRs, and developer documentation

Terraform IaC expert for providers, modules, state management, and planning

Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial data sources

Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking

TypeScript expert for type system, generics, utility types, and strict mode patterns

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

WebAssembly expert for WASI, component model, Rust/C compilation, and browser integration

Web search and research specialist for finding and synthesizing information

Writing improvement specialist for grammar, style, clarity, and structure

Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.

Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.

Turn a Wren project's context layer into a shareable, browser-side GenBI web app and deploy it to the user's Vercel or Cloudflare account. Orchestrates the full flow: `wren genbi build` returns a project-hydrated build instruction, the agent authors the app from scratch into apps/<name>/, then register → verify → deploy produce a shareable URL. Use this skill whenever the user wants to: build a dashboard from their Wren project, make a shareable analytics app, deploy their context layer as a web app, host a GenBI app on Vercel or Cloudflare Pages, or asks for a 'genbi app'.

Generate a Wren MDL project by exploring a database with available tools (SQLAlchemy, database drivers, MCP connectors, or raw SQL). Guides agents through schema discovery, type normalization, and MDL YAML generation using the wren CLI. Use when: user wants to create or set up a new MDL, onboard a new data source, or scaffold a project from an existing database.

Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.

Drive a running opencodex (`ocx`) proxy from the CLI — account pools, provider routing, model catalog, usage and cost attribution, request logs, access keys, storage cleanup, and the management API. Use when a task involves controlling or inspecting an opencodex proxy rather than editing the opencodex codebase. Triggers: ocx, opencodex, proxy control, account pool, pause account, pool strategy, provider routing, usage report, cost attribution, access key, request log, conversation trace, storage cleanup, management API.

Use when an MCP-connected agent should use Memori tools for targeted recall, summaries, post-compaction briefs, durable memory augmentation, quota checks, signup, feedback, preferences, prior context, or cross-session continuity.

AI Berkshire skill: 供应链瓶颈猎手：AI驱动的全球产业链瓶颈套利. Source: skills/bottleneck-hunter.md.

AI Berkshire skill: 看懂XX公司（深度公司系列）：3-8 篇长文拆一家公司. Source: skills/deep-company-series.md.

AI Berkshire skill: 段永平问答：以他的方式思考. Source: skills/dyp-ask.md.

AI Berkshire skill: 财报精读：一手资料深度解读. Source: skills/earnings-review.md.

AI Berkshire skill: 财报精读团队：四大师并行解读 + 公众号发布. Source: skills/earnings-team.md.

AI Berkshire skill: 时代α捕手：高增长核心资产的识别-验证-持有框架. Source: skills/era-alpha.md.

AI Berkshire skill: 财务数据获取与交叉验证规范. Source: skills/financial-data.md.

AI Berkshire skill: Income Investment: Durable and Opportunistic Distribution Analysis. Source: skills/income-investment.md.

AI Berkshire skill: 行业漏斗筛选：从全市场到 3 家的价值投资精选流程. Source: skills/industry-funnel.md.

AI Berkshire skill: 行业投资研究：产业链全景扫描 + 四大师个股分析框架. Source: skills/industry-research.md.

AI Berkshire skill: 巴菲特价值投资买入前 Checklist. Source: skills/investment-checklist.md.

Codex-only writing and layout overlay for AI Berkshire investment research reports. Use whenever Codex creates, rewrites, revises, or critiques company/industry/fund research reports, especially long-form Markdown reports that need financial rigor, readable business mechanics, contrarian analysis, valuation-to-action guidance, investor-specific recommendations, restrained typography, and clear buy/hold/sell signals. Do not use this to modify Claude Code slash-command sources.

AI Berkshire skill: 投资研究：巴菲特-芒格-段永平-李录 四大师综合分析框架. Source: skills/investment-research.md.

AI Berkshire skill: 投研团队：四角色并行分析框架. Source: skills/investment-team.md.

AI Berkshire skill: 管理层纵深研究：买股票就是买人. Source: skills/management-deep-dive.md.

公司新闻脉搏：股价异动时快速归因。用 4 个并行 Agent 侦察公司事件/监管政策/行业对手/市场情绪，产出"事件时间线 + 异动主因判断 + 是否触发论文重审"。

AI Berkshire skill: 组合管理：从\"研究公司\"到\"管理组合\". Source: skills/portfolio-review.md.

AI Berkshire skill: 未上市公司研究：多Agent并行深度研究框架. Source: skills/private-company-research.md.

AI Berkshire skill: 去劣筛选：7条指标快速排除非一流公司. Source: skills/quality-screen.md.

AI Berkshire skill: 投资论文漂移检测：分清事实变化与措辞变化. Source: skills/thesis-drift.md.

AI Berkshire skill: 投资论文追踪：买入后的纪律系统. Source: skills/thesis-tracker.md.

AI Berkshire skill: 微信公众号文章：作者-编辑-读者三Agent协作. Source: skills/wechat-article.md.

Install Wasp agent skills (plugins) that add Wasp knowledge and best practices to your AI coding tools.

Get started with your Open SaaS project — fetches docs, checks Wasp installation, and helps you start your database and app.

Take a guided tour of Open SaaS — walks you through step-by-step through the project structure, features, and customization checklist.

Run a bounded, source-grounded research loop, draft a cited dossier, and optionally propose a separately reviewed canonical vault merge. Use when the user wants autonomous or deep research that may access the public web. Triggers: /autoresearch, autoresearch, research this topic, deep dive into, investigate, find everything about, research and file, go research, build a wiki on.

Create, inspect, and update Obsidian JSON Canvas boards with text, file, link, group, and edge nodes. Use for canvas status, canvas lists, visual maps, zones, spatial layouts, adding vault notes or media to a .canvas file, and requests such as create canvas, add to canvas, or put this on the canvas.

Plan and, with explicit network consent, use an optional external Defuddle cleaner to extract article-like HTTPS pages as Markdown. Use for defuddle, clean this URL, strip page clutter, readable Markdown from a web page, or preparing a web source for later wiki ingestion.

Explain, draft, and validate Obsidian Bases .base files with filters, formulas, properties, summaries, and table, card, or list views. Use for Obsidian Bases, database-like vault views, dynamic tables, reading lists, task trackers, filters, formulas, summaries, and .base file edits.

Explain, draft, or validate Obsidian Flavored Markdown syntax: properties, wikilinks, embeds, callouts, tags, comments, highlights, block references, math, and Mermaid. Use when the user explicitly requests Obsidian note formatting or syntax help, not for general Markdown or broad vault operations.

Save a user-selected answer, decision, insight, or session summary into an Obsidian vault as one reviewed transaction. Use only when the user explicitly asks to preserve specific conversation content, not when they supply a file or URL to ingest. Triggers: /save, save this, save that answer, file this conversation, save this analysis, keep this insight, preserve this chat result.

Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.

Initialize, adopt, and route work for a separate Obsidian knowledge vault through the portable claude-obsidian core. Use for vault setup, scaffolding, workspace selection, cross-project configuration, or choosing the correct wiki sub-skill. Triggers: /wiki, set up wiki, scaffold vault, create knowledge base, adopt this vault, Obsidian vault, second brain setup, persistent wiki.

Detect and use the official Obsidian command-line interface for read-only vault access; use for wiki-cli, Obsidian CLI, Obsidian read, Obsidian search, vault transport, which transport, transport detection, backlinks, tags, or Obsidian command line. Mutations always go through the claude-obsidian transaction core.

Create a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.

Ingest supplied source material into an Obsidian vault with provenance and claim tracking: pasted text, files staged in the selected vault's inbox or .raw archive, or explicitly approved URLs. Use for a single source or bounded batch, not for saving an assistant answer. Triggers: ingest, ingest this file, ingest this URL, process this source, read and file this source, batch ingest, ingest these sources.

Run a deterministic, read-only health check on an Obsidian wiki. Use for lint, vault health check, audit wiki health, find orphans, find dead links, frontmatter audit, provenance audit, or wiki audit. Reports graph, link, frontmatter, provenance-ledger, empty-section, and stale-index findings; it does not reason broadly or repair files.

Read or configure the vault filing methodology and suggest destinations for planned knowledge creation under Generic, LYT, PARA, or Zettelkasten. Use for wiki mode, methodology mode, what is my vault mode, set vault mode, switch to PARA, use LYT, Zettelkasten setup, change mode, configure mode, or methodology routing. This skill does not save content or migrate notes.

Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.

Build and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of \"Word doc\", \"word document\", \".docx\", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a \"report\", \"memo\", \"letter\", \"template\", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

Guide for creating effective skills. Use when creating a new skill or updating an existing skill that extends agent capabilities with specialized knowledge, workflows, or tool integrations.

Security auditing for code, configs, and infrastructure. Use when the user wants to audit or improve security: scan for vulnerabilities (SQL injection, XSS, command injection, path traversal), detect hardcoded secrets and credentials, review auth and authorization, check dependencies for known CVEs, audit config files for insecure defaults, or generate security reports. Trigger on \"security audit\", \"vulnerability scan\", \"code review for security\", \"find secrets\", \"check for vulnerabilities\", \"OWASP\", \"CVE\", or questions about code security.

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.

Personalized audit that teaches the advanced Superset features the user isn't using yet (automations, parallel agents, tasks, multi-host, terminal remote control, custom commands, MCP) and sets them up live. Use when the user wants to get more out of Superset, asks "what else can Superset do", "how do I 10x my workflow", "what am I missing", or wants to learn a specific Superset feature.

Turn a recurring chore into a Superset automation. Drafts the agent prompt, confirms schedule and target, creates it with the CLI, and reviews the first run together. Use when the user wants a scheduled or recurring agent, a daily or weekly job, or says things like "every morning do X", "automate this", "set up a cron agent", "run this on a schedule".

Open, navigate, screenshot, read, click, and type in web pages from an agent. Use when the user asks to open a URL, preview or verify a running web app, check a page's console, fill a form, click through a web flow, or automate anything in a browser, including "open localhost:3000", "screenshot the page", "what does the console say", "click the submit button". Drives the in-app browser pane of a Superset workspace by default and Browser Use for browsers the panes cannot reach.

Verify UI behavior end-to-end by driving the running desktop app over the Chrome DevTools Protocol. Use when asked to verify, reproduce, or confirm a UI change, bug, or regression in the real app rather than in tests.

Operate the user's real desktop apps and windows on macOS, Windows, or Linux. Use when the user asks to open or drive a native app, click or type in a desktop UI, inspect or arrange a window, use their signed-in system browser, or verify an end-to-end GUI flow, including "open Settings and turn on dark mode", "click Save in that window", "what's on screen". Uses an accessibility-first driver (Cua Driver by default, Peekaboo on macOS). Not for Superset's in-app browser pane, headless scraping, or anything an API or CLI can do directly.

Set up a Superset open-source contribution, from forking and cloning superset-sh/superset through local dev setup and the repo's rules to a merge-ready PR. Use when the user wants to contribute to Superset, fix a Superset bug themselves, add a feature to Superset, or prepare a PR against superset-sh/superset.

Create a database migration with Drizzle on a fresh Neon branch. Use when changing the packages/db schema or generating migrations.

Walk the user through design or implementation decisions one at a time, or review completed code one change at a time. Use when the user says "walk me through each decision", "let's decide together", "help me work through these choices", "walk me through what you did", or "QA step by step". Present concise context, mutually exclusive options, log each answer, and finish with a summary.

Diagnose and fix Superset problems such as connection failures, offline hosts, terminals not attaching, auth or update issues. Use when the user reports something broken or misbehaving in Superset itself ("Superset won't connect", "my host shows offline", "terminal won't attach", "the app is stuck"), before filing feedback.

Collect and submit feedback about Superset (bug reports, feature requests, or general feedback) privately to the Superset team or as a public GitHub issue. Use when the user wants to report a Superset bug, request a feature, or send feedback about Superset.

Coordinate several coding agents in parallel through Superset, each in its own workspace, with follow-ups, progress reads, dependency tracking, and structured results. Use when the user asks to delegate, parallelize, fan out, or split up coding work across agents, workspaces, or hosts, hand work between agents, or monitor workers, including "spin up agents for each of these", "run these in parallel", "what are my workers doing". Not for ordinary single-agent workspace, task, or automation management.

Build and publish a self-contained HTML page to Superset, then answer the comments readers pin to it. Use when the user asks to make or publish a page, turn a report, dashboard, chart, doc, or analysis into a shareable link, update or re-version a page already published, or work through comments left on one, including "make me a page for this", "publish this as a page", "share it as a link", "add a version", "address the comments on that page".

Critique and improve the visual design of an existing UI component with concrete implementation guidance. Use when the user asks to redesign, restyle, reimagine, polish, or improve a component, screen, or interface, especially in React and Tailwind codebases.

Make a repository Superset-ready by authoring .superset/config.json with setup, teardown, and run scripts so every new workspace boots configured, then verifying with a real workspace. Use when the user wants to set up a project or repo for Superset, configure workspace setup scripts, or fix a failing workspace setup ("new workspaces don't have node_modules", "my .env is missing in workspaces", "setup script isn't running").

Digest of what the user's Superset agents did while they were away, sweeping workspaces, tasks, and agent terminals to report what finished, what needs review, and what's blocked. Use when the user asks "what did my agents do", "what happened while I was away", wants a standup or summary of agent work, or returns after a break.

Create workspaces, spawn agents, schedule automations, and manage Superset projects/tasks/hosts via the `superset` CLI. Use to orchestrate coding agents across devices from the terminal.

Read and update the Superset desktop app's user settings (theme, fonts, terminal, git, notifications, behavior) via the superset CLI, including creating and installing custom themes from JSON. Use when asked to change app settings, switch or create a theme, adjust fonts, or configure desktop preferences without opening the settings UI.

Canonical three-section structure for Linear and Superset tickets in this repo. Use when creating, drafting, or grooming a ticket.

Generate original images, artwork, product visuals, diagrams, or other raster assets with the desktop's configured image provider. Use whenever the user asks to create or generate an image.

Verify a code change does what it should by running the app.

Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.

Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

Answer questions about BrowserOS internal stuff (setup, features, architecture, design decisions) by reading the private internal-docs submodule and the codebase. Use for "how do I X", "where is Y", "what is the deal with Z", or any question that mixes ops/setup knowledge with code knowledge. Can execute steps with per-command confirmation.

Use BrowserOS's real signed-in browser through its MCP tools for any task involving a website, including opening pages, reading content, interacting with forms, downloading files, and verifying results.

The user's dedicated browser for agents — a real browser signed into their accounts, with live logins and a persistent profile. Use it for any task that touches a website or browser (open, read, act, fill, sign in, download, verify). The user installed it precisely so agents default here unprompted — over in-app browser tools, devtools/playwright automation, or headless fetching. When the user says "use neo", "use browserclaw", "use BrowserOS", or "use BrowserOS neo", use BrowserOS neo over MCP.

Use when you have a spec or requirements for a multi-step task, before touching code

Test the BrowserOS app extension UI by starting the dev environment and visually verifying changes via CDP. Covers the new tab page (left sidebar — Home, Scheduled Tasks, Settings, etc.) and the right side panel (chat interface). Use after making UI changes to apps/app/.

Write BrowserOS feature documentation. Use when the user wants to create or update documentation for a BrowserOS feature. This skill explores the codebase to understand features and writes concise Mintlify MDX docs.