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 mlflow/skills --skill instrumenting-with-mlflow-tracinggit clone --depth 1 https://github.com/mlflow/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/mlflow/skills/instrumenting-with-mlflow-tracing)<a href="https://agentmods.dev/skills/mlflow/skills/instrumenting-with-mlflow-tracing"><img src="https://agentmods.dev/badge/skills/mlflow/skills/instrumenting-with-mlflow-tracing/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/mlflow/skills/instrumenting-with-mlflow-tracing"><img src="https://agentmods.dev/badge/skills/mlflow/skills/instrumenting-with-mlflow-tracing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00122 | $0.01521 |
| Opus 5 | $0.00061 | $0.00760 |
| Sonnet 5 | $0.00024 | $0.00304 |
| Haiku 4.5 | $0.00012 | $0.00152 |
Grade A, and why
instrumenting-with-mlflow-tracing 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 yesterday.
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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MLflow Tracing Instrumentation Guide
Language-Specific Guides
Based on the user's project, load the appropriate guide:
- Python projects: Read
references/python.md - TypeScript/JavaScript projects: Read
references/typescript.md
If unclear, check for package.json (TypeScript) or requirements.txt/pyproject.toml (Python) in the project.
Databricks: verify auth before the first run
When the target is a Databricks workspace, confirm auth and the target workspace before running instrumented code. An expired token, or a default profile pointed at the wrong workspace, drops traces silently at export with no error raised.
databricks auth token --profile <name> # fails if the token is expired. Re-run: databricks auth login --profile <name>
python -c "import mlflow; print(mlflow.get_tracking_uri())" # confirm it targets the intended workspace
What to Trace
Trace these operations (high debugging/observability value):
| Operation Type | Examples | Why Trace |
|---|---|---|
| Root operations | Main entry points, top-level pipelines, workflow steps | End-to-end latency, input/output logging |
| LLM calls | Chat completions, embeddings | Token usage, latency, prompt/response inspection |
| Retrieval | Vector DB queries, document fetches, search | Relevance debugging, retrieval quality |
| Tool/function calls | API calls, database queries, web search | External dependency monitoring, error tracking |
| Agent decisions | Routing, planning, tool selection | Understand agent reasoning and choices |
| External services | HTTP APIs, file I/O, message queues | Dependency failures, timeout tracking |
Skip tracing these (too granular, adds noise):
- Simple data transformations (dict/list manipulation)
- String formatting, parsing, validation
- Configuration loading, environment setup
- Logging or metric emission
- Pure utility functions (math, sorting, filtering)
Rule of thumb: Trace operations that are important for debugging and identifying issues in your application.
What ships with it
8 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.
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.
- yesterday Changed · +11 lines 368fdfae0530
- 12d ago First seen · 135 lines · 122 tokens per session scan A 0e4f5afe5c8f
instrumenting-with-mlflow-tracing is a skill published in the GitHub repository mlflow/skills (75 stars, last pushed yesterday), licensed Apache-2.0. It adds 122 tokens to every session and 1,521 once invoked, about $0.0006 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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