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 ihatesea69/kiro-kit --skill ml-opsgit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/ihatesea69/kiro-kit/ml-ops)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/ml-ops"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/ml-ops/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/ihatesea69/kiro-kit/ml-ops"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/ml-ops.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 | $0.00029 | $0.00295 |
| Opus 5.5 | $0.00012 | $0.00118 |
| Sonnet 5.5 | $0.00006 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
ml-ops 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.
What it actually says
MLOps
Activate this skill when deploying models or managing ML infrastructure.
When to Use
- Deploying models to production endpoints
- Setting up experiment tracking (MLflow, W&B)
- Building model registries and versioning
- Implementing A/B testing for models
- Monitoring model drift and performance
Core Tools
- MLflow: Experiment tracking, model registry
- Weights & Biases: Experiment visualization
- BentoML/Ray Serve: Model serving
- DVC: Data and model versioning
- Evidently AI: Model monitoring
Patterns
import mlflow
mlflow.set_experiment("classification_v2")
with mlflow.start_run():
mlflow.log_params({"lr": 0.001, "epochs": 50})
mlflow.log_metrics({"accuracy": 0.94, "f1": 0.91})
mlflow.sklearn.log_model(model, "model")
Rules
- Version everything: code, data, models, configs
- Automate training pipelines (no manual steps)
- Monitor prediction distributions for drift
- Implement rollback mechanisms for model updates
- Log all experiments, even failed ones
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 · 46 lines · 29 tokens per session scan A 326f9056085d
ml-ops is a skill published in the GitHub repository ihatesea69/kiro-kit (19 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 295 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 2026-09-03.
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