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 oyi77/1ai-skills --skill role-ai-ml-engineergit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote 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/oyi77/1ai-skills/role-ai-ml-engineer)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/role-ai-ml-engineer"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/role-ai-ml-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/oyi77/1ai-skills/role-ai-ml-engineer"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/role-ai-ml-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 | $0.00045 | $0.02096 |
| Opus 5.5 | $0.00018 | $0.00838 |
| Sonnet 5.5 | $0.00009 | $0.00419 |
| Haiku 4.5 | $0.00005 | $0.00210 |
Grade A, and why
role-ai-ml-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 today.
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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The role-ai-ml-engineer skill directs generative AI architecture, large language model (LLM) serving, retrieval-augmented generation (RAG) pipelines, fine-tuning protocols, and autonomous agent runtime optimization. Rather than treating AI engineering as superficial prompt hacking or unstructured API calls, this role coordinates multi-provider LLM routing with automated failovers, semantic retrieval chunking and embedding indexing, parameter-efficient fine-tuning (LoRA/PEFT), high-throughput GPU model serving (vLLM, Ollama), and continuous agent self-reflection.
[Phase 1: Task Modeling & Model Selection] ──► [Phase 2: RAG & Context Engineering] ──► [Phase 3: Prompt & Fine-Tuning Optimization]
│
[Phase 5: Self-Reflection & SRE] ◄── [Phase 4: Serving & Model Routing] ◄────────────┘
The AI/ML engineer enforces strict evaluation gates, token efficiency budgets, hallucination mitigations, and model fallback invariants across production agent runtimes.
When to Use
- When designing, building, or optimizing RAG pipelines (chunking, embedding models, vector databases, hybrid retrieval).
- When establishing multi-provider LLM routing (OpenAI, Anthropic, Gemini, local models) with cost/latency balancing.
- When formulating production prompt architectures (chain-of-thought, few-shot calibration, structured JSON schemas).
- When fine-tuning open-source LLMs using LoRA, QLoRA, or instruction dataset curation.
- When deploying high-performance local or cloud LLM inference engines (vLLM, TGI, llama.cpp, Ollama).
- When implementing autonomous agent self-reflection loops, memory retrieval, and skill self-improvement.
When NOT to Use
- When building web application frontend UI components or styling (use
content/role-ui-ux-designer). - When managing general cloud infrastructure, VPC networking, or Kubernetes ingress (use
devops/role-devops-sre-engineer). - When implementing application database schemas or non-AI backend APIs (use
development/role-software-engineer). - When conducting penetration testing or web vulnerability exploitation (use
cybersecurity/role-penetration-tester).
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.
- today First seen · 176 lines · 45 tokens per session scan A 29296a2468ac
role-ai-ml-engineer is a skill published in the GitHub repository oyi77/1ai-skills (17 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 2,096 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 2026-10-26.
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ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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generate-image
Generates or edits images with the native generateimage tool (Nano Banana Pro through Ace or a Gemini key, GPT Image 2 through an OpenAI key), for illustrations, cover art, graphical abstracts, conceptual visuals, photo-style figures and edits of existing images, saved straight into the workspace and checked before…
tinker-training-cost
Calculates training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.