Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add curiositech/some_claude_skills --skill anthropic-technical-deep-divegit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/curiositech/some_claude_skills/anthropic-technical-deep-dive)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/anthropic-technical-deep-dive"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/anthropic-technical-deep-dive/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/anthropic-technical-deep-dive"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/anthropic-technical-deep-dive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00094 | $0.04100 |
| Opus 5 | $0.00047 | $0.02050 |
| Sonnet 5 | $0.00019 | $0.00820 |
| Haiku 4.5 | $0.00009 | $0.00410 |
Grade A, and why
anthropic-technical-deep-dive scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
mindmap
root((Anthropic Technical Interview))
Constitutional AI
Principle-based alignment
RLAIF vs RLHF
Principle conflicts
Scalable oversight
RLHF & Training
Reward modeling
PPO and alternatives
Alignment tax
Reward hacking
DPO / Direct alignment
Interpretability
Circuits and features
Superposition
Sparse Autoencoders
Scaling Monosemanticity
Golden Gate Bridge Claude
Mechanistic interpretability
Scaling Laws
Kaplan et al
Chinchilla optimal
Data-constrained scaling
Emergent capabilities
Predictability vs surprise
Context Engineering
Long context retrieval
MCP protocol
Tool use architecture
RAG vs long context
Prompt engineering at scale
Agentic Systems
Computer use
Claude Code
Agent evaluation
Trust and safety
Tool use reliability
Multi-agent coordination
AI Safety
Alignment tax
Deceptive alignment
Sandbagging
Responsible Scaling Policy
Frontier risk assessment
Red teaming
Anthropic Culture
Race to the top
Responsible development
Commercial safety tension
Interpretability as priority
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 338 lines · 94 tokens per session scan A 91ef04461ba5
anthropic-technical-deep-dive is a skill published in the GitHub repository curiositech/some_claude_skills (218 stars, last pushed 4d ago), licensed MIT. It adds 94 tokens to every session and 4,100 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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