MLflow is an open-source platform for building, testing, monitoring, and managing applications that use machine-learning models, large language models, or AI agents. Teams use it to inspect application behavior, evaluate results, manage prompts, and control access to models and data in production. Catalogue add-ons provide coding-agent workflows for MLflow.
Borrowing it
Nothing to install: this file belongs to mlflow/mlflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mlflow/mlflow/master/.claude/skills/upload-media/SKILL.mdgit clone --depth 1 https://github.com/mlflow/mlflowWrote 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/mlflow/upload-media)<a href="https://agentmods.dev/skills/mlflow/mlflow/upload-media"><img src="https://agentmods.dev/badge/skills/mlflow/mlflow/upload-media.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.00199 |
| Opus 5 | $0.00024 | $0.00100 |
| Sonnet 5 | $0.00010 | $0.00040 |
| Haiku 4.5 | $0.00005 | $0.00020 |
Grade A, and why
upload-media 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.
What it actually says
Upload media
Files: $ARGUMENTS (when empty, the paths named in the request).
uv run --package skills skills upload-media <path>... # prints "<path>\t<url>" per file
Embed an image as . Embed a video as the bare URL in its own paragraph, blank line above and below; anything else renders as a link rather than a player.
No GitHub documentation covers this endpoint, so it can stop working without notice. Source: https://x.com/steipete/status/2088486859244741020.
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 First seen · 18 lines · 49 tokens per session scan A 7ba9eb8537c5
upload-media is a skill published in the GitHub repository mlflow/mlflow (27,822 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 199 once invoked, about $0.0002 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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