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 magnus919/agent-skills --skill ml-engineeringgit clone --depth 1 https://github.com/magnus919/agent-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/magnus919/agent-skills/ml-engineering)<a href="https://agentmods.dev/skills/magnus919/agent-skills/ml-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/ml-engineering/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/magnus919/agent-skills/ml-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/ml-engineering.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 | $0.00091 | $0.01155 |
| Opus 5 | $0.00046 | $0.00577 |
| Sonnet 5 | $0.00018 | $0.00231 |
| Haiku 4.5 | $0.00009 | $0.00115 |
Grade A, and why
ml-engineering 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Engineering Methodology
Machine learning engineering is the bridge between model research and production systems. This methodology covers the engineering disciplines needed to train, evaluate, deploy, and maintain ML models reliably.
The ML Engineer's Domain
| You own | You don't own |
|---|---|
| Model training — LoRA/QLoRA fine-tuning, full fine-tuning, distributed training | Statistical modeling and experimental design — that's the data scientist |
| Model evaluation — benchmark suites, custom eval sets, regression testing | Causal inference and hypothesis testing — that's the data scientist |
| Quantization — GGUF, GPTQ, AWQ, bitsandbytes | Training data collection and labeling — that's the data/ML ops team |
| Inference serving — vLLM, llama.cpp, TGI, Triton | Business metrics and KPI definition — that's the product manager |
| Evaluation harness — lm-eval-harness, custom pipelines | Data pipeline architecture — that's the data engineer |
| Model deployment — containerization, versioning, A/B testing | Infrastructure provisioning — that's the platform engineer |
Reference Files
| Reference | When to load |
|---|---|
references/fine-tuning.md |
Setting up a LoRA/QLoRA/ full fine-tuning run — data prep, hyperparameters, validation strategy |
references/evaluation.md |
Evaluating a model — benchmark selection, custom eval sets, regression tracking, comparison methodology |
references/quantization-inference.md |
Quantizing a model and serving it — GGUF/GPTQ/AWQ/bitsandbytes comparison, calibration data strategies, KV cache quantization, vLLM/llama.cpp/TGI/Triton architecture, production considerations |
references/training-infrastructure.md |
Selecting and provisioning training infrastructure — GPU selection, VRAM budgeting, multi-GPU strategies (DDP/FSDP/DeepSpeed), cloud vs on-prem, storage, monitoring |
Templates
| Template | When to Use |
|---|---|
templates/training-run-record.md |
Recording a training or fine-tuning run — model and data versions, full config, environment, eval results — so it can be reproduced |
templates/eval-regression-table.md |
Tracking model quality across runs and triaging a regression — one row per eval case or capability subset |
templates/quantization-decision-record.md |
Recording a quantization decision — baseline, candidates compared, quality threshold, and rollback path |
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/evals.json 11 KB
- README.md 2.2 KB
- references/evaluation.md 1.3 KB
- references/fine-tuning.md 1.3 KB
- references/quantization-inference.md 41 KB
- references/training-infrastructure.md 3.9 KB
- scripts/check-eval-overlap.py 9.3 KB runs code
- scripts/test_check_eval_overlap.py 7.7 KB runs code
- templates/eval-regression-table.md 1.7 KB
- templates/quantization-decision-record.md 2.4 KB
- templates/training-run-record.md 2.8 KB
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 · 74 lines · 91 tokens per session scan A eee61fc6ca46
ml-engineering is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 1,155 once invoked, about $0.0005 per session on Opus 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 2026-09-03.
Other skills, from other repositories
map
Build and commit a Cortex function knowledge graph — maps structural dependencies and domain intent relationships across all AI functions in the project. Supports --reduce (default on) for transitive reduction of the dependency graph.
test
Enter the Test phase of CocoBrew. Reads spec.md test requirements, generates test cases, executes SQL validation and quality checks, records results in test.md. Can be re-run without full rebuild. Requires Build phase completion.
map-diff
Analyze the impact of staged git changes against the committed Cortex function knowledge graph — shows which downstream functions are affected before you commit.
map-explain
Produce a natural-language explanation of a specific Cortex function, business capability, or schema element from the committed knowledge graph.
trace-health
Compute CocoTrace Snowflake asset health grade. Usage: $trace health.
neurolink-guide
Guide for using the NeuroLink SDK and CLI. Invoke when users ask how to use neurolink, integrate AI providers, add MCP tools, configure RAG, set up memory, deploy servers, or work with multimodal content. Covers SDK, CLI, providers, tools, and enterprise features.