Content & Media

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.

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.

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

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.

Identify and remediate vulnerabilities through systematic code analysis. Use when performing security assessments, pre-deployment reviews, compliance validation (OWASP, PCI-DSS, GDPR), investigating known vulnerabilities, or post-incident analysis.

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

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.

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.

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 mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.