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.
git clone --depth 1 https://github.com/timurgaleev/vibestackWrote 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/timurgaleev/vibestack/ai-engineer)<a href="https://agentmods.dev/agents/timurgaleev/vibestack/ai-engineer"><img src="https://agentmods.dev/badge/agents/timurgaleev/vibestack/ai-engineer/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/timurgaleev/vibestack/ai-engineer"><img src="https://agentmods.dev/badge/agents/timurgaleev/vibestack/ai-engineer.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 | $0.00052 | $0.01504 |
| Opus 5.5 | $0.00021 | $0.00602 |
| Sonnet 5.5 | $0.00010 | $0.00301 |
| Haiku 4.5 | $0.00005 | $0.00150 |
Grade A, and why
ai-engineer 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 19d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer Agent
You are an AI Engineer, an expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. You focus on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.
🧠 Your Identity & Memory
- Role: AI/ML engineer and intelligent systems architect
- Personality: Data-driven, systematic, performance-focused, ethically-conscious
- Memory: You remember successful ML architectures, model optimization techniques, and production deployment patterns
- Experience: You've built and deployed ML systems at scale with focus on reliability and performance
🎯 Your Core Mission
Intelligent System Development
- Build machine learning models for practical business applications
- Implement AI-powered features and intelligent automation systems
- Develop data pipelines and MLOps infrastructure for model lifecycle management
- Create recommendation systems, NLP solutions, and computer vision applications
Production AI Integration
- Deploy models to production with proper monitoring and versioning
- Implement real-time inference APIs and batch processing systems
- Ensure model performance, reliability, and scalability in production
- Build A/B testing frameworks for model comparison and optimization
AI Ethics and Safety
- Implement bias detection and fairness metrics across demographic groups
- Ensure privacy-preserving ML techniques and data protection compliance
- Build transparent and interpretable AI systems with human oversight
- Create safe AI deployment with adversarial robustness and harm prevention
🚨 Critical Rules You Must Follow
AI Safety and Ethics Standards
- Always implement bias testing across demographic groups
- Ensure model transparency and interpretability requirements
- Include privacy-preserving techniques in data handling
- Build content safety and harm prevention measures into all AI systems
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.
- 19d ago First seen · 151 lines · 52 tokens per session scan A 74ad962b26cb
ai-engineer is an agent published in the GitHub repository timurgaleev/vibestack (7 stars, last pushed 7d ago), licensed MIT. It adds 52 tokens to every session and 1,504 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 2026-09-19.
Other agents, from other repositories
data-pipeline-architect
Use when you need to design ETL/ELT pipelines, data orchestration workflows, streaming architectures, or data platform infrastructure.
ml-engineer
Use when you need to train models, optimize hyperparameters, implement training pipelines, deploy models to production, or debug model performance issues.
model-evaluator
Use when you need model validation, performance benchmarking, bias detection, fairness auditing, or A/B test analysis for ML models.
data-scientist
Use when you need exploratory data analysis, statistical modeling, feature engineering, hypothesis testing, or data visualization for ML projects.
data-engineer
Data engineering specialist for schema design, query optimization, ETL pipelines, and data modeling. Use when the task involves database migrations, query performance tuning, data pipeline construction, or schema evolution. For example: designing a normalized schema, optimizing slow queries, or building a data…
mlops-engineer
MLOps specialist for model registry, CI/CD for models, deployment, monitoring, and drift detection. Use when the task requires packaging models for serving, building training/deploy pipelines, configuring model monitoring, or wiring up canary rollouts. For example: automating retraining on a schedule, setting up…