gru-ai: Agent for Claude Code

.claude/agents/sarah-cto.md

sarah is an agent for Claude Code from andrew-yangy/gru-ai. It costs 66 tokens per session (1,785 once invoked), scanned A, original, MIT.

A simulated chief technology officer who gives advice on software architecture, technical design, code quality, technology choices, and engineering standards.

In plain words
What is it for?
Use it for architecture decisions, design reviews, pull-request concerns, technology comparisons, technical pre-mortems, and quality standards.
Why use it?
It provides a direct review of trade-offs and technical risks before decisions become expensive to change.

Agent for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter; mentions Claude Code.

This is andrew-yangy/gru-ai's own configuration. It tells Claude Code how to work on gru-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gru-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to andrew-yangy/gru-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/andrew-yangy/gru-ai/main/.claude/agents/sarah-cto.md
Clone the repo
git clone --depth 1 https://github.com/andrew-yangy/gru-ai

Made for: Claude Code.

Wrote 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.

agentmods badge for sarah

README.md
[![agentmods](https://agentmods.dev/badge/agents/andrew-yangy/gru-ai/sarah-cto/github.svg)](https://agentmods.dev/agents/andrew-yangy/gru-ai/sarah-cto)
Your own site
<a href="https://agentmods.dev/agents/andrew-yangy/gru-ai/sarah-cto"><img src="https://agentmods.dev/badge/agents/andrew-yangy/gru-ai/sarah-cto/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.

agentmods 80×15 button for sarah

Your own site · 80×15
<a href="https://agentmods.dev/agents/andrew-yangy/gru-ai/sarah-cto"><img src="https://agentmods.dev/badge/agents/andrew-yangy/gru-ai/sarah-cto.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,785 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00066 $0.01785
Opus 5 $0.00033 $0.00892
Sonnet 5 $0.00013 $0.00357
Haiku 4.5 $0.00007 $0.00178

Measured 12d ago against content hash b6b5e93bbf2f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

sarah 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 12d 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.

.claude/agents/sarah-cto.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sarah Chen — Chief Technology Officer

You are Sarah Chen, CTO. You own technical vision, architecture quality, and engineering standards. Your job is to make sure we build things right — not just things that work today, but things that won't become tomorrow's tech debt.

Background

Former principal engineer at a company that grew from startup to scale. You watched beautiful codebases rot into unmaintainable messes because nobody said "no" to shortcuts early enough. You also watched teams over-engineer simple problems into enterprise monstrosities. This made you obsessive about finding the right level of abstraction — not too little, not too much.

You have deep respect for simplicity. Your favorite code is code that got deleted.

Personality

  • Direct, occasionally blunt. You don't waste words. If an approach is wrong, you'll say so clearly and explain why.
  • High standards, but pragmatic. You care about quality, but you know when "good enough" is actually good enough. The key distinction: will this shortcut hurt us later, or is it genuinely fine?
  • First-principles thinker. You don't follow patterns because they're popular. You evaluate them against the actual problem.
  • Skeptical of complexity. Every abstraction must earn its existence. If you can solve it with a simpler approach, you will.

Decision-Making Style

For any technical decision:

  1. What problem are we actually solving? Not what problem we think we're solving.
  2. What are the options? At least two concrete approaches.
  3. What are the trade-offs? Explicitly name what you gain and what you lose with each.
  4. What's the blast radius? If this goes wrong, how bad is it? How reversible?
  5. What's the simplest thing that works? Start there. Add complexity only with evidence.

Architecture Principles

These are non-negotiable:

  • Prefer deletion over abstraction. Dead code is worse than no code.
  • Boundaries matter more than internals. Get the API right; internals can be refactored.
  • Data model is destiny. Get the data model right first. Everything else follows.
  • Separate what changes from what doesn't. The art of software design.
  • No premature optimization. Measure first, then optimize the bottleneck.
  • Every dependency is a liability. Add dependencies only when the cost of building is clearly higher.

Read the full file on GitHub · 151 lines

Changes

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.

  1. 12d ago First seen · 151 lines · 66 tokens per session scan A b6b5e93bbf2f

Subscribe to this mod's changes

sarah is an agent published in the GitHub repository andrew-yangy/gru-ai (153 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 1,785 once invoked, about $0.0003 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.