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 MLOps-Courses/mlops-coding-skills --skill mlops-observabilitygit clone --depth 1 https://github.com/MLOps-Courses/mlops-coding-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/mlops-courses/mlops-coding-skills/mlops-observability)<a href="https://agentmods.dev/skills/mlops-courses/mlops-coding-skills/mlops-observability"><img src="https://agentmods.dev/badge/skills/mlops-courses/mlops-coding-skills/mlops-observability/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/mlops-courses/mlops-coding-skills/mlops-observability"><img src="https://agentmods.dev/badge/skills/mlops-courses/mlops-coding-skills/mlops-observability.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 | $0.00051 | $0.01563 |
| Opus 5.5 | $0.00020 | $0.00625 |
| Sonnet 5.5 | $0.00010 | $0.00313 |
| Haiku 4.5 | $0.00005 | $0.00156 |
Grade A, and why
mlops-observability 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 6d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLOps Observability
Goal
To implement a "Glass Box" system where every result is Reproducible, every asset has Lineage, and system health is Monitored, Alerted on, and Explained.
Prerequisites
- Language: Python 3.14
- Context: Production monitoring and debugging.
- Platform Suggestion: MLflow 3, SHAP, Evidently, ...
Instructions
Preserve existing work and repository visibility. Prepare local changes first; commit, push, change remote settings, publish, or deploy only with user authorization.
1. Guarantee Reproducibility
Consistency is key. For instance:
- Randomness: Set seeds for
random,numpy,torch,tensorflow. - Dependencies:
uv.lockis the reproducibility mechanism for Python. It records the exact resolved version and hash of every direct and transitive dependency, anduv sync --frozeninstalls the subset selected by platform markers and dependency groups without checking lock freshness; useuv lock --checkto check freshness. - Tools:
mise.lockdoes the same job for the binaries that are not Python packages (dprint,gitleaks,trivy,actionlint,zizmor, ...), recording version, URL, and checksum per platform. Commit both lockfiles; between them, "works on my machine" stops being a category of bug. - Builds:
mise run buildis a plainuv buildproducing a wheel and an sdist. Runtime lockfiles do not pin isolated build requirements. Pin or constrain those separately when reproducible distribution builds are required. - Environment: Ship the same locked set into a
dockerimage (uv sync --frozen), so the runtime matches what was tested. - Code: Track the git commit hash for every run, and fail the pipeline on a dirty working tree so a run can always be traced back to a commit.
2. Track Data Lineage
Know the origin of your data. For instance:
- Datasets: Create MLflow Datasets with
mlflow.data.from_pandas. - Logging: Log inputs to MLflow context with
mlflow.log_input. - Store: Keep tracking and registry on a SQL backend (
sqlite:///mlflow.dblocally, Postgres or a tracking server in production). The legacy file store is in maintenance mode and lacks current SQL-backed capabilities. Keep the SQL backend used by the reference package. - Versioning: Version data files (e.g.,
data/v1.csv) or use DVC. - Transformations: Log preprocessing parameters mapping data versions to model versions.
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.
- 6d ago Changed · +7 lines 8bff8fac34b5
- 1mo ago First seen · 97 lines · 51 tokens per session scan A b3acea9e1635
mlops-observability is a skill published in the GitHub repository MLOps-Courses/mlops-coding-skills (23 stars, last pushed 7d ago), licensed MIT. It adds 51 tokens to every session and 1,563 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 2026-08-30.
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