Design

Consult PostgreSQL's pg_dump implementation for guidance on system catalog queries and schema extraction when implementing pgschema features

Build and debug Reflex (rx) UIs in this repo. Use for editing ui/*.py, choosing rx components, fixing Var/conditional/foreach issues, and applying responsive/layout patterns from the Reflex docs.

Guide for adding SQL function translations to dbplyr backends. Use when implementing new database-specific R-to-SQL translations for functions like string manipulation, date/time, aggregates, or window functions.

Guide for creating new Claude Code skills. Use when you need to create a new skill to package expertise or workflow into a reusable capability that Claude can automatically invoke.

Guide for researching SQL syntax and behavior for database backends. Use when you need to research how a SQL function, command, or feature works in a specific database before implementing it in dbplyr.

Guide for deprecating R functions/arguments. Use when a user asks to deprecate a function or parameter, including adding lifecycle warnings, updating documentation, adding NEWS entries, and updating tests.

Deploy, manage environment variables, view logs, and configure cron jobs with Vercel CLI. Use when deploying to Vercel, managing env vars (add/update/remove), viewing runtime/build logs, or configuring scheduled tasks in vercel.json.

Systematically clarify user needs, preferences, and constraints before planning or implementation. Classifies work type, investigates existing systems, discovers edge cases and integration points, resolves assumptions, and creates detailed specifications. Use when building features, enhancements, or integrations where requirements need clarification.

Create and use custom slash commands to automate Claude Code workflows. Learn argument passing, frontmatter configuration, bash integration, and file references for building reusable prompts.

Teaches how to write effective documentation and instructions for LLMs by assuming competence, using progressive disclosure, prioritizing examples over explanations, and building feedback loops into workflows.

Create structured implementation plans before coding. Use when breaking down complex features, refactors, or system changes. Validates requirements, analyzes codebase impact, and produces actionable task breakdowns with identified dependencies and risks.

Defines required structure, frontmatter format, and best practices for SKILL.md files. Use BEFORE creating or editing any skill - this is the spec to follow, not optional reference.

Adapt Claude Code for different use cases beyond software engineering by customizing system prompts for teaching, learning, analysis, or domain-specific workflows.

Package expertise into discoverable, reusable capabilities that extend Claude's functionality. Use when creating new Skills, understanding how Skills work, or organizing existing capabilities.

Systematically evaluate code changes for security, correctness, performance, and spec alignment. Use when reviewing PRs, assessing code quality, or verifying implementation against requirements.

Performs initial binary triage by surveying memory layout, strings, imports/exports, and functions to quickly understand what a binary does and identify suspicious behavior. Use when first examining a binary, when user asks to triage/survey/analyze a program, or wants an overview before deeper reverse engineering.

Solve CTF reverse engineering challenges using systematic analysis to find flags, keys, or passwords. Use for crackmes, binary bombs, key validators, obfuscated code, algorithm recovery, or any challenge requiring program comprehension to extract hidden information.

MANDATORY: When writing Go tests, you MUST use 'When...it should...' format for ALL test names. When writing any Go code, you MUST remind user to run 'make lint-fix' and 'make verify'. These are non-negotiable HyperShift requirements.

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.