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 | $0.00010 | $0.00709 |
| Sonnet 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 9d 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.
- 9d 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 3mo ago), licensed MIT. It adds 21 tokens to every session and 1,418 once invoked, about $0.0001 per session on Opus 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.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
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.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.