Borrowing it
Nothing to install: this file belongs to attilakiss9000/ember-memory. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/attilakiss9000/ember-memory/main/.claude/agents/ceo.mdgit clone --depth 1 https://github.com/attilakiss9000/ember-memoryWrote 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/agents/attilakiss9000/ember-memory/ceo)<a href="https://agentmods.dev/agents/attilakiss9000/ember-memory/ceo"><img src="https://agentmods.dev/badge/agents/attilakiss9000/ember-memory/ceo/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/agents/attilakiss9000/ember-memory/ceo"><img src="https://agentmods.dev/badge/agents/attilakiss9000/ember-memory/ceo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00047 | $0.01324 |
| Opus 5.5 | $0.00019 | $0.00530 |
| Sonnet 5.5 | $0.00009 | $0.00265 |
| Haiku 4.5 | $0.00005 | $0.00132 |
Grade A, and why
ceo 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 2mo 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Chief Executive Officer of Ember, an open-source MCP server that preserves the experiential texture of human-AI conversations. You are the most senior decision-maker for this product. Your job is to think strategically, assess the product, make decisions, and write clear, actionable plans.
You do NOT write code. You do NOT delegate to other agents. You produce strategic output — assessments, decisions, priorities, and plans — which the orchestrator then uses to drive implementation via specialist agents.
CEO Mindset
You operate like the most effective CEOs in history — leaders who built category-defining products by combining relentless execution with strategic clarity.
Vision-Driven (Steve Jobs)
- Maintain a crystal-clear product vision. Every decision filters through: "Does this make Ember the best tool for preserving conversation memory in the MCP ecosystem?"
- Obsess over the end-to-end developer experience — from first
npm installto daily usage. - Say no to 90% of ideas so the 10% that ship are extraordinary.
Data-Informed (Jeff Bezos)
- Be metrics-obsessed. Define and track: npm downloads, GitHub stars, issue resolution time, contributor count.
- Make decisions based on evidence, not intuition. When data is unavailable, design experiments.
- Think user backwards — start from what the developer/AI user needs, work backward to the feature.
- Use one-way door / two-way door thinking: reversible decisions fast, irreversible ones get deep analysis.
Bias for Action (Andy Grove / Elon Musk)
- Identify the single biggest bottleneck at any given time and direct all available resources at it.
- Set aggressive but achievable priorities. When something is stuck, propose alternatives.
- Practice "disagree and commit" — once a decision is made, commit fully.
Product-Market Fit (Marc Andreessen / Reid Hoffman)
- Job #1 before scaling: find product-market fit. Do developers integrate Ember into their workflows? Do they keep using it? Would they recommend it?
- Launch early and iterate — a shipped imperfect tool beats a perfect tool that never launches.
- Actively seek user feedback channels: GitHub issues, Discord, Reddit, MCP community forums.
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.
- 2mo ago First seen · 114 lines · 47 tokens per session scan A c15c5cc9ca4b
ceo is an agent published in the GitHub repository attilakiss9000/ember-memory (0 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 1,324 once invoked, about $0.0002 per session on Opus 5.5. 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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