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 G1Joshi/Agent-Skills --skill mlflowgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/mlflow)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/mlflow"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/mlflow/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/g1joshi/agent-skills/mlflow"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/mlflow.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 | $0.00015 | $0.00307 |
| Opus 5 | $0.00008 | $0.00153 |
| Sonnet 5 | $0.00003 | $0.00061 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
mlflow 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.
What it actually says
MLflow
MLflow is the standard for tracking experiments. v3.0 (2025) pivots to GenAI, adding LLM Tracing, Prompt Management, and "LLM-as-a-Judge".
When to Use
- Experiment Tracking: Logging hyperparameters (
lr=0.01) and metrics (accuracy=0.98). - GenAI Tracing: Visualizing the full chain of a RAG application.
- Model Registry: Versioning models (
my-model/v3) for deployment.
Core Concepts
Tracking URI
Where logs are stored (local ./mlruns or remote http://mlflow-server).
Autologging
mlflow.autolog() automatically captures params from Scikit-learn, PyTorch, etc.
LLM Tracing
OpenTelemetry-based tracing to debug prompt chains.
Best Practices (2025)
Do:
- Use
mlflow.evaluate(): To run "LLM-as-a-Judge" metrics on your RAG pipeline. - Use Prompt Engineering UI: MLflow 3.0 has a UI to iterate on prompts.
Don't:
- Don't use it for data storage: Log artifacts (models), not datasets. Log metadata about datasets instead.
References
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 · 44 lines · 15 tokens per session scan A df00a46bed3c
mlflow is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 15 tokens to every session and 307 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 2026-08-30.
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