skills/codex/brand-guidelines/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: brand-guidelines description: Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply. --- # Anthropic Brand Styling ## Overview To access Anthropic's official brand identity and style resources, use this skill. **Keywords**: branding, corporate id
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/brand-guidelinesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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To access Anthropic's official brand identity and style resources, use this skill.
Keywords: branding, corporate identity, visual identity, post-processing, styling, brand colors, typography, Anthropic brand, visual formatting, visual design
Main Colors:
#141413 - Primary text and dark backgrounds#faf9f5 - Light backgrounds and text on dark#b0aea5 - Secondary elements#e8e6dc - Subtle backgroundsAccent Colors:
#d97757 - Primary accent#6a9bcc - Secondary accent#788c5d - Tertiary accentdevelopment
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -