New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Setting Up Experiment Tracking

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Implement machine learning experiment tracking using MLflow or Weights & Biases.

MITAuto-check passedData & Analytics

Install Setting Up Experiment Tracking

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill setting-up-experiment-tracking -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace setting-up-experiment-tracking --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/setting-up-experiment-tracking .claude/skills/setting-up-experiment-tracking && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
setting-up-experiment-tracking
GitHub stars
2.8k
Token cost
~954 tokens
SKILL.md length
436 words
Files
4 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Implement machine learning experiment tracking using MLflow or Weights & Biases.

  • Works in 4 steps: Analyze Context: The skill analyzes the… → Configure Environment: It configures the… → Initialize Tracking: The skill… → …
  • Asked to setup experiment tracking
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • Initialize MLflow

What it does

Setting Up Experiment Tracking is an agent skill from jeremylongshore/tons-of-skills-marketplace. Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts, reference files and assets (for example `assets/README.md`, `references/README.md` and `scripts/README.md`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning. It works with MLflow, Weights & Biases and Python. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to setup experiment tracking
  • Initialize MLflow
  • With relevant phrases based on skill purpose

Example prompts

  • “setup experiment tracking”
  • “initialize MLflow”
  • “/setting-up-experiment-tracking”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Analyze Context: The skill analyzes the current project context to determine the appropriate experiment tracking tool (MLflow or W&B)…
  2. Configure Environment: It configures the environment by installing necessary Python packages and setting environment variables.
  3. Initialize Tracking: The skill initializes the chosen tracking tool, potentially starting a local MLflow server or connecting to a W&B…
  4. Provide Code Snippets: It provides code snippets demonstrating how to log experiment parameters, metrics, and artifacts within your ML code.

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Setting Up Experiment Tracking loads about 954 tokens when it runs, and up to ~971 if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 436 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~954
With references · SKILL.md plus every file in references/, read only if the agent opens them
~971

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 436 words, ~954 tokens.

Download SKILL.mdSave it as .claude/skills/setting-up-experiment-tracking/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
setting-up-experiment-tracking
description
Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.19.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, ml, logging

Experiment Tracking Setup

Configure ML experiment tracking with MLflow or Weights & Biases, including environment setup and code for logging parameters, metrics, and artifacts.

Overview

This skill streamlines the process of setting up experiment tracking for machine learning projects. It automates environment configuration, tool initialization, and provides code examples to get you started quickly.

How It Works

  1. Analyze Context: The skill analyzes the current project context to determine the appropriate experiment tracking tool (MLflow or W&B) based on user preference or existing project configuration.
  2. Configure Environment: It configures the environment by installing necessary Python packages and setting environment variables.
  3. Initialize Tracking: The skill initializes the chosen tracking tool, potentially starting a local MLflow server or connecting to a W&B project.
  4. Provide Code Snippets: It provides code snippets demonstrating how to log experiment parameters, metrics, and artifacts within your ML code.

When to Use This Skill

This skill activates when you need to:

  • Start tracking machine learning experiments in a new project.
  • Integrate experiment tracking into an existing ML project.
  • Quickly set up MLflow or Weights & Biases for experiment management.
  • Automate the process of logging parameters, metrics, and artifacts.

Examples

Example 1: Starting a New Project with MLflow

User request: "track experiments using mlflow"

The skill will:

  1. Install the mlflow Python package.
  2. Generate example code for logging parameters, metrics, and artifacts to an MLflow server.
Example 2: Integrating W&B into an Existing Project

User request: "setup experiment tracking with wandb"

The skill will:

  1. Install the wandb Python package.
  2. Generate example code for initializing W&B and logging experiment data.
Show full SKILL.md (172 more words)Show less

Best Practices

  • Tool Selection: Consider the scale and complexity of your project when choosing between MLflow and W&B. MLflow is well-suited for local tracking, while W&B offers cloud-based collaboration and advanced features.
  • Consistent Logging: Establish a consistent logging strategy for parameters, metrics, and artifacts to ensure comparability across experiments.
  • Artifact Management: Utilize artifact logging to track models, datasets, and other relevant files associated with each experiment.

Integration

This skill can be used in conjunction with other skills that generate or modify machine learning code, such as skills for model training or data preprocessing. It ensures that all experiments are properly tracked and documented.

Prerequisites

  • Appropriate file access permissions
  • Required dependencies installed

Instructions

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

  • Invalid input: Prompts for correction
  • Missing dependencies: Lists required components
  • Permission errors: Suggests remediation steps

Resources

  • Project documentation
  • Related skills and commands

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references, assets) in skills/.curated/setting-up-experiment-tracking of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Setting Up Experiment Tracking next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Setting Up Experiment Tracking compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Setting Up Experiment Tracking this skilljeremylongshore/tons-of-skills-marketplace2.8k—~954Automated safety check: PassMIT
Senior Data Scientistalirezarezvani/claude-skills28k1 repos~2.3kAutomated safety check: PassMIT
MLflow Experiment TrackingOrchestra-Research/AI-Research-SKILLs13k2 repos~3.9kAutomated safety check: PassMIT
ML Pipeline ExpertJeffallan/claude-skills12k—~1.9kAutomated safety check: PassMIT
LaminDB Biological Data Managementdavila7/claude-code-templates33k12 repos~3.6kAutomated safety check: PassMIT
Experiment Tracking Setuprevfactory/harness-1001.3k—~1.4kAutomated safety check: PassApache-2.0

Similar skills

  • Senior Data Scientist

    alirezarezvani/claude-skills

    World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics.

    28k GitHub starsUsed in 1 repo~2.3k tokens
    Data & AnalyticsAuto-check passed

  • MLflow Experiment Tracking

    Orchestra-Research/AI-Research-SKILLs

    Tracks ML experiments, versions models in the MLflow registry and covers deployment and reproducibility, with autologging for common frameworks.

    13k GitHub starsUsed in 2 repos~3.9k tokens
    DevOps & CloudAuto-check passed

  • ML Pipeline Expert

    Jeffallan/claude-skills

    Designs ML pipeline infrastructure: experiment tracking with MLflow or Weights & Biases, Kubeflow and Airflow orchestration, Feast feature stores and model validation gates.

    12k GitHub stars~1.9k tokensUpdated 7 days ago
    DevOps & CloudAuto-check passed

  • LaminDB Biological Data Management

    davila7/claude-code-templates

    Manages biological datasets with LaminDB: versioned artifacts, run lineage, ontology-based annotation, schema validation and links to workflow managers and ML tools.

    33k GitHub starsUsed in 12 repos~3.6k tokens
    Research & ScienceAuto-check passed

  • Experiment Tracking Setup

    revfactory/harness-100

    Guide for experiment tracking tool setup (MLflow, Weights & Biases, etc.), reproducibility assurance, model registry, and experiment comparison methodology.

    1.3k GitHub stars~1.4k tokensUpdated 6 mo ago
    Research & ScienceAuto-check passed

  • Scikit Learn

    zLanqing/codex-claude-academic-skills

    Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.

    4.7k GitHub starsUsed in 16 repos~3.9k tokens
    Data & AnalyticsAuto-check passed

More from jeremylongshore/tons-of-skills-marketplace

All 3,342 skills in this repo

  • Performing Security Code Review

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to conduct a security-focused code review using the security-agent plugin.

    2.8k GitHub starsUsed in 2 repos~1.3k tokens
    Auto-check: notes

  • Adapting Transfer Learning Models

    jeremylongshore/tons-of-skills-marketplace

    Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed

  • Agent Context Loader

    jeremylongshore/tons-of-skills-marketplace

    Execute proactive auto-loading: automatically detects and loads agents.md files.

    2.8k GitHub stars~1.1k tokensUpdated today
    Auto-check passed

  • Aggregating Performance Metrics

    jeremylongshore/tons-of-skills-marketplace

    Aggregate and centralize performance metrics from applications, systems, databases, caches, and services.

    2.8k GitHub stars~1.2k tokensUpdated today
    Auto-check passed

  • Analyzing Capacity Planning

    jeremylongshore/tons-of-skills-marketplace

    Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth.

    2.8k GitHub stars~947 tokensUpdated today
    Auto-check passed

  • Analyzing Database Indexes

    jeremylongshore/tons-of-skills-marketplace

    Process use when you need to work with database indexing. An agent skill from jeremylongshore/tons-of-skills-marketplace.

    2.8k GitHub stars~2k tokensUpdated today
    Auto-check passed

Works with

Categories

Questions about Setting Up Experiment Tracking

What does Setting Up Experiment Tracking do?

Implement machine learning experiment tracking using MLflow or Weights & Biases. Setting Up Experiment Tracking is an agent skill from jeremylongshore/tons-of-skills-marketplace. Implement machine learning experiment tracking using MLflow or Weights & Biases.

When should I use Setting Up Experiment Tracking?

Setting Up Experiment Tracking fits situations like: asked to setup experiment tracking; initialize MLflow; with relevant phrases based on skill purpose.

How do I install Setting Up Experiment Tracking in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill setting-up-experiment-tracking -a claude-code`. Or copy the skill folder (skills/.curated/setting-up-experiment-tracking in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/setting-up-experiment-tracking in your project. Claude Code loads it when a task matches its description.

How do I install Setting Up Experiment Tracking in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill setting-up-experiment-tracking -a codex`. Or copy the skill folder (skills/.curated/setting-up-experiment-tracking in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/setting-up-experiment-tracking in your project. Codex loads it when a task matches its description.

Can I use Setting Up Experiment Tracking in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill setting-up-experiment-tracking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/setting-up-experiment-tracking, .gemini/skills/setting-up-experiment-tracking, .github/skills/setting-up-experiment-tracking and .opencode/skills/setting-up-experiment-tracking in your project.

What does Setting Up Experiment Tracking need to run?

SKILL.md names no scripts, command-line tools or credentials: Setting Up Experiment Tracking is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Setting Up Experiment Tracking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Setting Up Experiment Tracking safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Setting Up Experiment Tracking use?

Setting Up Experiment Tracking is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Setting Up Experiment Tracking use?

About 954 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17 tokens, read only when the agent opens those files.

What are the alternatives to Setting Up Experiment Tracking?

Skills that share tags, products or a category with Setting Up Experiment Tracking: Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), MLflow Experiment Tracking (Orchestra-Research/AI-Research-SKILLs, 13k stars), ML Pipeline Expert (Jeffallan/claude-skills, 12k stars) and LaminDB Biological Data Management (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Setting Up Experiment Tracking?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.