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 vignesh2027/AI-AGENT-SKILLS --skill ai-ml-engineeringgit clone --depth 1 https://github.com/vignesh2027/AI-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/vignesh2027/ai-agent-skills/ai-ml-engineering)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/ai-ml-engineering"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/ai-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/vignesh2027/ai-agent-skills/ai-ml-engineering"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/ai-ml-engineering.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 | $0.00020 | $0.00922 |
| Opus 5 | $0.00010 | $0.00461 |
| Sonnet 5 | $0.00004 | $0.00184 |
| Haiku 4.5 | $0.00002 | $0.00092 |
Grade A, and why
ai-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 12d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
ML engineering failures are silent and delayed. A model that scores well on the benchmark can fail badly in production. This skill enforces the practices that catch these failures before they reach users: proper evaluation harnesses, data leakage detection, distribution shift monitoring, and safety checks.
When to Use
- Before training or fine-tuning a model
- Before deploying a model to production
- When integrating a third-party LLM API
- When evaluating model quality
- When debugging unexpected model behavior
Process
Step 1: Define the task and success metric precisely
Before any code: what is the exact prediction task? What metric proves the model is good enough? What metric proves it is safe enough? Document these as your evaluation contract.
Step 2: Establish the baseline
Compute a simple baseline (majority class, rule-based system, GPT-4 zero-shot). Your model must beat this baseline by a meaningful margin to justify the complexity.
Step 3: Audit the training data
- Check for data leakage (test set information in training set)
- Check for label quality (sample 100 examples manually)
- Check for demographic skew (does the dataset represent production distribution?)
- Check for PII that should not be in training data
- Document the data provenance and version
Step 4: Implement a reproducible training pipeline
- Pin all dependency versions
- Set all random seeds
- Version the dataset (not just the model)
- Store training hyperparameters with model artifacts
- Confirm: can you reproduce this exact model from scratch?
Step 5: Build the evaluation harness before training
Write your evaluation pipeline before training. Evaluations should be:
- Automatic (run in CI)
- Deterministic (same inputs → same scores)
- Multi-dimensional (accuracy, latency, cost, safety, fairness)
- Comprehensive (held-out test set + edge case suite)
Step 6: Train with monitoring
Track: training loss, validation loss, gradient norms. Flag: loss spikes, NaN gradients, overfitting (train loss << val loss), underfitting.
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.
- 12d ago First seen · 105 lines · 20 tokens per session scan A 17c14a87d9f2
ai-ml-engineering is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (1 stars, last pushed 14d ago), licensed MIT. It adds 20 tokens to every session and 922 once invoked, about $0.0001 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 2026-08-31.
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