Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
How to deploy Dravr infrastructure and apply Cloud Run config changes. Use when editing infra/ terraform, when a merged code change is live but a Cloud Run setting (cpu, memory, min/max instances, env var, scaling) hasn't taken effect, or when asked to plan/apply infra. Explains the two-pipeline model (app binary auto-deploys on push; terraform infra config is a separate manual apply) plus the cpu/cpu_idle guardrails.
Enforces zero-tolerance code quality policy using Clippy with strict lints, all warnings treated as errors
Write well-formatted notes to the dravr-vault Obsidian knowledge base. Use this skill whenever creating or updating an ADR, runbook, plan, API doc, guide, session output, or any structured document that should land in the vault — even when the user doesn't say "Obsidian" explicitly. Delegates to obsidian:obsidian-cli to write to the live vault and applies Dravr frontmatter and formatting standards.
Bootstrap Pierre server with database, admin user, coaches, and test users for development and testing
Validates coach markdown files for required frontmatter fields, sections, and naming conventions
Use when setting up the shared dravr-vault Obsidian vault on a new machine or for a new team member. Guides through cloning, symlinking claude_docs, and verifying obsidian-cli.
Provides React Native performance optimization guidelines for FPS, TTI, bundle size, memory leaks, re-renders, and animations. Applies to tasks involving Hermes optimization, JS thread blocking, bridge overhead, FlashList, native modules, or debugging jank and frame drops.