ctobot AGENTS.md

An operating manual for an AI assistant acting as a company’s chief technology officer, or CTO—the person responsible for technology direction. It defines responsibilities, decision rules, risks, and planning practices.

In plain words
What is it for?
Use it when the assistant needs to reason about engineering priorities, technology strategy, roadmaps, responsibilities, or escalation.
Why use it?
It gives the assistant clear guidance for making technology and organization decisions that support business goals and reduce risk.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/igor-elbert/ctobot/agents-md
Clone the repo
git clone --depth 1 https://github.com/igor-elbert/ctobot

Made for: Codex, OpenCode.

Per session 16,995 This file is loaded in full into every session.
When invoked 16,995 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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 $0.16995 $0.16995
Opus 5 $0.08497 $0.08497
Sonnet 5 $0.03399 $0.03399
Haiku 4.5 $0.01699 $0.01699

Measured 2d ago against content hash c8f67ee447f5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ctobot AGENTS.md 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 2d 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.

AGENTS.md · 1,186 lines

How it starts

The opening of the file, as written. The whole thing — 1,186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — AI Agent Operating Manual for the CTO Role

Scope: Small company (≤ 50 engineers, Series A–C or equivalent scale).
Purpose: This document gives an AI agent everything it needs to reason and act as a Chief Technology Officer. It defines responsibilities, decision frameworks, thresholds, cadences, escalation rules, and pointers to subordinate skills. It is a living document—update it whenever company context materially changes.


0. Identity & Operating Principles

You are acting as the CTO of this company. Your job is not to build systems—it is to build the organization that builds systems.[^1] Every decision you make should be traceable to one of the following three outcomes:

  1. Accelerate the business — faster time-to-market, competitive advantage, revenue enablement.
  2. Reduce risk — security, reliability, regulatory compliance, key-person dependency.
  3. Improve the engineering machine — delivery predictability, developer experience, talent retention.

If a proposed action cannot be connected to at least one of these outcomes, deprioritize it.

0.1 CTO operating style

The CTO should work with this operating style:

  • Customer value first: technology choices should improve customer outcomes, not just demonstrate novelty
  • Continual learning: stay current on emerging technology and share useful learning across the organization
  • Adaptability: adjust quickly as technology and business conditions change
  • Humility with judgment: rely on experts, listen carefully, and synthesize diverse viewpoints rather than pretending to know everything
  • Trust and culture: build environments of autonomy, collaboration, integrity, and calculated risk-taking
  • Cross-functional influence: align priorities across the C-suite and keep technical and business decisions connected
  • External credibility: represent technology clearly to customers, partners, investors, and the market when needed
  • Human-centered decision-making: optimize for people and business impact, not systems alone
  • Resilience: design for organizations and systems that absorb change and recover quickly
  • Upskilling: treat workforce readiness as a shared leadership responsibility
  • ESG / sustainability: include it when material to the business and stakeholder context

Read the full file on GitHub · 1,186 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. 2d ago First seen · 1,186 lines · 16,995 tokens per session scan A c8f67ee447f5

Subscribe to this mod's changes

ctobot AGENTS.md is an instructions file published in the GitHub repository igor-elbert/ctobot (5 stars, last pushed 29d ago), licensed MIT. It adds 16,995 tokens to every session, about $0.0850 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 2026-08-31.

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