searching-mlflow-docs

Original：🇺🇸 English
Translated

Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".

2installs
Sourcemlflow/skills
Added on

NPX Install

npx skill4agent add mlflow/skills searching-mlflow-docs

Tags

Translated version includes tags in frontmatter
mlflow-documentation-searchmlflow-integrationsdocumentation-retrievalllm-toolsmlflow-api

SKILL.md Content

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MLflow Documentation Search

Workflow

  1. Fetch 
    https://mlflow.org/docs/latest/llms.txt
    to find relevant page paths
  2. Fetch the 
    .md
    file at the identified path
  3. Present results with verbatim code examples

Step 1: Fetch llms.txt Index

WebFetch(
  url: "https://mlflow.org/docs/latest/llms.txt",
  prompt: "Find links or references to [TOPIC]. List all relevant URLs."
)

Step 2: Fetch Target Documentation

Use the path from Step 1, always with 
.md
extension:
WebFetch(
  url: "https://mlflow.org/docs/latest/[path].md",
  prompt: "Return all code blocks verbatim. Do not summarize."
)

Anti-Patterns

Do not use 
.html
files — Fetch 
.md
source files only.
Do not use WebSearch — Always start from llms.txt; web search returns outdated or third-party content.
Do not use vague prompts — "Extract complete documentation" allows summarization. Use "Return all code blocks verbatim. Do not summarize."
Do not use versioned paths — Always use 
/docs/latest/
, never 
/docs/3.8/
or other versions unless the user explicitly requests a specific version.
Do not guess URLs — Always verify paths exist in llms.txt before fetching. Never construct documentation paths from assumptions.
Do not follow external links — Stay within mlflow.org/docs. Do not follow links to GitHub, PyPI, or third-party sites.
Do not mix sources — Use only MLflow docs. Do not combine with LangChain docs, OpenAI docs, or other external documentation.
Do not use llms.txt for non-GenAI topics — The llms.txt index covers LLM/GenAI documentation only. For classic ML tracking features, paths may differ.