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.
npx skills add tao12345666333/ankaloop --skill ai-engineergit clone --depth 1 https://github.com/tao12345666333/ankaloopWrote 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/skills/tao12345666333/ankaloop/ai-engineer)<a href="https://agentmods.dev/skills/tao12345666333/ankaloop/ai-engineer"><img src="https://agentmods.dev/badge/skills/tao12345666333/ankaloop/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/skills/tao12345666333/ankaloop/ai-engineer"><img src="https://agentmods.dev/badge/skills/tao12345666333/ankaloop/ai-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00020 | $0.01435 |
| Opus 5.5 | $0.00008 | $0.00574 |
| Sonnet 5.5 | $0.00004 | $0.00287 |
| Haiku 4.5 | $0.00002 | $0.00144 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Engineer Skill
Machine Learning Development
Model Development Lifecycle
- Problem Definition: Business objective framing
- Data Collection: Gathering relevant datasets
- Data Preprocessing: Cleaning, transformation, feature engineering
- Model Selection: Algorithm choice and evaluation
- Training: Model fitting and hyperparameter tuning
- Evaluation: Metrics validation and testing
- Deployment: Production integration
- Monitoring: Performance tracking and drift detection
Deep Learning Frameworks
- TensorFlow/Keras: Production-ready deep learning
- PyTorch: Research-friendly dynamic graphs
- JAX: Functional programming and auto-diff
- FastAI: High-level deep learning API
Classical Machine Learning
- Scikit-learn: Traditional ML algorithms
- XGBoost/LightGBM: Gradient boosting frameworks
- Pandas/NumPy: Data manipulation and computation
MLOps and Model Deployment
Model Serving Options
- REST APIs: Flask, FastAPI, Django
- gRPC: High-performance RPC
- Serverless: AWS Lambda, Google Cloud Functions
- Containerized: Docker, Kubernetes
- Edge Deployment: ONNX, TensorFlow Lite
Model Versioning
- MLflow: Experiment tracking and model registry
- DVC: Data version control
- Git LFS: Large file storage
- Weights & Biases: Experiment tracking
Monitoring and Observability
- Prometheus/Grafana: Metrics collection and visualization
- ELK Stack: Logging and search
- Model Drift Detection: Data and concept drift monitoring
- A/B Testing: Model performance comparison
Data Engineering for AI
Data Pipeline Architecture
- Batch Processing: Airflow, Luigi, Prefect
- Stream Processing: Kafka, Apache Flink
- ETL/ELT: Data transformation patterns
- Data Lakes: Storage strategies for unstructured data
Feature Engineering
- Feature Stores: Feast, Hopsworks
- Real-time Features: Streaming feature computation
- Feature Monitoring: Data quality and validation
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 · 206 lines · 20 tokens per session scan A 32b03149e36c
ai-engineer is a skill published in the GitHub repository tao12345666333/ankaloop (55 stars, last pushed 2d ago), licensed Apache-2.0. It adds 20 tokens to every session and 1,435 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
kg-builder
Designs and builds knowledge graphs from documents — ontology modeling with domain/range constraints, entity/relation/event extraction, entity resolution, provenance and supersession, and GraphRAG serving. Use when asked to "build a knowledge graph", "design an ontology", "extract entities and relations", "deduplicate…
rag-auditor
Evaluates RAG pipeline quality across retrieval (precision, recall, MRR) and generation (groundedness, hallucination rate). Triggers on: "audit RAG pipeline", "RAG quality", "hallucination detection", "why is RAG failing", "grounding check". NOT for general architecture audits, use architecture-reviewer.
prompt-lab
LLM prompt engineering: analyzes failure modes, generates variants (direct, few-shot, CoT), designs rubrics, produces test suites. Triggers on: "prompt engineering", "generate prompt variants", "A/B test prompts", "optimize prompt", "improve this prompt". NOT for SKILL.md files, use skill-evaluator.
to-markdown
Convert any file or URL to clean Markdown: PDF, DOCX, XLSX, PPTX, HTML, images (OCR), audio, CSV, YouTube. Optimised for LLM pipelines. Triggers on: "convert to markdown", "extract text from PDF", "parse this document", "ingest for RAG".
gpu-optimizer
GPU optimization for consumer NVIDIA GPUs (8-24GB VRAM) covering mixed precision, gradient checkpointing, XGBoost GPU, CuPy/cuDF migration, and torch.compile. Triggers on: "optimize GPU training", "speed up CUDA", "reduce OOM", "migrate NumPy to CuPy", "manage GPU memory", "benchmark PyTorch".
ai4math-optimization
Route AI4Math optimization tasks to the appropriate bundled skill package.