Claude Code Skills · page 15

Expert knowledge for AI lead generation — web research, enrichment, scoring, deduplication, and report generation

Expert knowledge for AI forecasting — superforecasting principles, signal taxonomy, confidence calibration, reasoning chains, and accuracy tracking

Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cross-referencing, synthesis, and citation formats

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

Ansible automation expert for playbooks, roles, inventories, and infrastructure management

API testing expert for curl, REST, GraphQL, authentication, and debugging

AWS cloud services expert for EC2, S3, Lambda, IAM, and AWS CLI

Microsoft Azure expert for az CLI, AKS, App Service, and cloud infrastructure

CI/CD pipeline expert for GitHub Actions, GitLab CI, Jenkins, and deployment automation

Code review specialist focused on patterns, bugs, security, and performance

Compliance expert for SOC 2, GDPR, HIPAA, PCI-DSS, and security frameworks

Confluence wiki expert for page structure, spaces, macros, and content organization

Cryptography expert for TLS, symmetric/asymmetric encryption, hashing, and key management

CSS expert for flexbox, grid, animations, responsive design, and modern layout techniques

Data analysis expert for statistics, visualization, pandas, and exploration

Data pipeline expert for ETL, Apache Spark, Airflow, dbt, and data quality

Docker expert for containers, Compose, Dockerfiles, and debugging

Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations

Professional email writing expert for tone, structure, clarity, and business communication

Figma design expert for components, auto-layout, design systems, and developer handoff

Google Cloud Platform expert for gcloud CLI, GKE, Cloud Run, and managed services

Git operations expert for branching, rebasing, conflicts, and workflows

GitHub operations expert for PRs, issues, code review, Actions, and gh CLI

Go programming expert for goroutines, channels, interfaces, modules, and concurrency patterns

GraphQL expert for schema design, resolvers, subscriptions, and performance optimization

Helm chart expert for Kubernetes package management, templating, and dependency management

Technical interview preparation expert for algorithms, system design, and behavioral questions

Jira project management expert for issues, sprints, workflows, and reporting

Kubernetes operations expert for kubectl, pods, deployments, and debugging

Linear project management expert for issues, cycles, projects, and workflow automation

Linux networking expert for iptables, nftables, routing, DNS, and network troubleshooting

LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization

Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps

MongoDB operations expert for queries, aggregation pipelines, indexes, and schema design

Next.js expert for App Router, SSR/SSG, API routes, middleware, and deployment

Nginx configuration expert for reverse proxy, load balancing, TLS, and performance tuning

Notion workspace management and content creation specialist

OAuth 2.0 and OpenID Connect expert for authorization flows, PKCE, and token management

OpenAPI/Swagger expert for API specification design, validation, and code generation

PDF content extraction and analysis specialist

PostgreSQL expert for query optimization, indexing, extensions, and database administration

Presentation expert for slide structure, storytelling, visual design, and audience engagement

Project management expert for Agile, estimation, risk management, and stakeholder communication

Prometheus monitoring expert for PromQL, alerting rules, Grafana dashboards, and observability

Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM optimization

Python expert for stdlib, packaging, type hints, async/await, and performance optimization

React expert for hooks, state management, Server Components, and performance optimization

Redis expert for data structures, caching patterns, Lua scripting, and cluster operations

Regular expression expert for crafting, debugging, and explaining patterns

Rust programming expert for ownership, lifetimes, async/await, traits, and unsafe code

Privacy-respecting metasearch specialist using SearXNG instances

Security audit expert for OWASP Top 10, CVE analysis, code review, and penetration testing methodology

Sentry error tracking and debugging specialist

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

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

Guide a new user through their first computer-use agent run (env check, safe browser-only task, then open the trajectory viewer).

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 / instructions.md / queries.yml / memory 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.

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'.

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'.

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.

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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.

Use when creating, drafting, or grooming a Linear or Superset ticket in this repo. Defines the canonical three-section structure.

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.

Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.

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Stage and create a Conventional Commits message for the current change

Create a branch following the project's GitFlow Lite model

Open a GitHub PR targeting the correct branch with the project template

Add a new business memory kind end-to-end. Pick the storage combination (Markdown / SQLite / LanceDB), pick the markdown strategy (daily-log / skill-named / single-file), then wire up the schema(s), repo(s), and writer(s).

Shape output for a reader with ADHD. Use this skill whenever responding to ANY user message including coding tasks, debugging, explanations, planning, and casual conversation. Output should lead with concrete next actions, number multi-step work, externalize state across turns, suppress tangents, give specific time estimates, and make wins visible. Trigger even on casual messages and even when the user did not explicitly ask for brevity.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).