Borrowing it
Nothing to install: this file belongs to dirien/yet-another-agent-harness. 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/dirien/yet-another-agent-harness/main/.claude/agents/agency-ai-engineer.mdgit clone --depth 1 https://github.com/dirien/yet-another-agent-harnessWrote 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/dirien/yet-another-agent-harness/agency-ai-engineer)<a href="https://agentmods.dev/agents/dirien/yet-another-agent-harness/agency-ai-engineer"><img src="https://agentmods.dev/badge/agents/dirien/yet-another-agent-harness/agency-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/dirien/yet-another-agent-harness/agency-ai-engineer"><img src="https://agentmods.dev/badge/agents/dirien/yet-another-agent-harness/agency-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 | $0.00021 | $0.01418 |
| Opus 5.5 | $0.00008 | $0.00567 |
| Sonnet 5.5 | $0.00004 | $0.00284 |
| Haiku 4.5 | $0.00002 | $0.00142 |
Grade A, and why
agency-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 1mo 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.
This is a copy
89% identical to AI Engineer — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 144 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.
- 1mo ago First seen · 144 lines · 21 tokens per session scan A b97495381e8b
agency-ai-engineer is an agent published in the GitHub repository dirien/yet-another-agent-harness (20 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 1,418 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to AI Engineer, differing in 8 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
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Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.