Agent skill · research science · erichowens
anthropic-technical-deep-dive
Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview",
Why this skill is useful
Provides deep domain-specific knowledge on Anthropic's research areas and interview preparation that the AI wouldn't reliably generate on its own.
What it needs
About 11k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
Anthropic Technical Deep Dive Build genuine, defensible technical opinions on Anthropic's core research areas. This is the competitive edge for demonstrating real intellectual engagement with Anthropic's mission -- not reciting papers, but showing you have thought critically about the work and can connect it to your own engineering experience. When to Use Use for: Preparing for Anthropic-specific technical interview rounds Developing nuanced opinions on Constitutional AI, RLHF, interpretability Bridging a CV/ML/engineering background to alignment and safety work Practicing articulation of complex AI safety trade-offs Understanding Anthropic's product landscape and strategic position Preparing for "what do you think about X?" style questions NOT for: General ML system design interviews (use ml-system-design-interview) Behavioral/values interview prep (use values-behavioral-interview) Coding interview prep or algorithm practice Writing research papers or conducting original research Preparing for interviews at other AI labs (different emphasis areas) --- Topic Landscape --- The Five-Layer Articulation Framework For every topic, prepare five layers of depth. An interviewer may stop at any layer. Being able to go deeper signals genuine understanding. Layer 1: Core Idea (2 sentences) State what the thing is and why it matters. No jargon soup -- a smart non-specialist should follow. Layer 2: Key Technical Challenge Identify the hard unsolved problem. This shows you understand the frontier, not just the textbook version. Layer 3: Nuanced Opinion with Evidence Make a specific claim. Support it with evidence from papers, your experience, or first-principles reasoning. Acknowledge uncertainty. Layer 4: Connection to Engineering Practice Bridge the abstract idea to a concrete engineering problem you have solved or could solve. This is where 15 years of ML/CV experience becomes a superpower. Layer 5: Open Questions and Limitations Identify what we do not know. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/anthropic-technical-deep-dive