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Agent skill

Tracking Model Versions

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

Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin.

MITAuto-check passedData & Analytics

Install Tracking Model Versions

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill tracking-model-versions -a claude-code

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace tracking-model-versions --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/tracking-model-versions .claude/skills/tracking-model-versions && 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
tracking-model-versions
GitHub stars
2.8k
Token cost
~1.3k tokens
SKILL.md length
577 words
Files
8 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin.

  • Works in 8 steps: Connect to the MLflow tracking server by… → Create or select an MLflow experiment… → Log a new model version: start an MLflow… → …
  • Asks to manage model versions
  • SKILL.md covers Overview, Prerequisites, Instructions and Examples, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Tracking Model Versions is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. it should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/example_mlflow_workflow.yaml` and `assets/model_card_template.md`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning and Git workflow. It works with MLflow. 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

  • Asks to manage model versions
  • Track model lineage
  • Log model performance
  • Implement version control f..

Example prompts

  • “/tracking-model-versions”

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

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

  1. Connect to the MLflow tracking server by setting MLFLOW_TRACKING_URI and verify connectivity with mlflow experiments list.
  2. Create or select an MLflow experiment for the model project using mlflow experiments create --experiment-name .
  3. Log a new model version: start an MLflow run, log parameters (learning rate, epochs, batch size), log metrics (accuracy, loss, F1 score)…
  4. Register the model in the MLflow Model Registry using mlflow.register_model() with the run URI and a descriptive model name.
  5. Transition the model version through stages: None -> Staging -> Production using client.transition_model_version_stage(). Archive previous…
  6. Compare model versions by querying metrics across runs with mlflow.search_runs() and generating comparison tables showing metric…
  7. Generate a model card from the registered model metadata, including training data description, evaluation metrics, intended use…
  8. Set up automated alerts for model performance degradation by comparing production metrics against baseline thresholds stored in the model…

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 2 files in scripts/ (Python), which the agent can run.

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

  • Network

    Links to these hosts (documentation or services it may open):

    • mlflow.org
    • dvc.org
    • modelcards.withgoogle.com

    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

Tracking Model Versions loads about 1.3k tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 577 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

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). 577 words, ~1,327 tokens.

Download SKILL.mdSave it as .claude/skills/tracking-model-versions/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
tracking-model-versions
description
Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. it should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.24.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, performance, tracking-model

Model Versioning Tracker

Overview

Track and manage AI/ML model versions using MLflow, DVC, or Weights & Biases. Log model metadata (hyperparameters, training data hash, framework version), record evaluation metrics (accuracy, F1, latency), manage model registry transitions (Staging, Production, Archived), and generate model cards documenting lineage and performance.

Prerequisites

  • MLflow tracking server running locally or remotely (mlflow server or managed MLflow)
  • Python 3.9+ with mlflow, pandas, and the relevant ML framework installed
  • Model artifacts accessible on the local filesystem or cloud storage (S3, GCS)
  • Write access to the MLflow tracking URI and artifact store

Instructions

  1. Connect to the MLflow tracking server by setting MLFLOW_TRACKING_URI and verify connectivity with mlflow experiments list.
  2. Create or select an MLflow experiment for the model project using mlflow experiments create --experiment-name <name>.
  3. Log a new model version: start an MLflow run, log parameters (learning rate, epochs, batch size), log metrics (accuracy, loss, F1 score), and log the model artifact with mlflow.<flavor>.log_model().
  4. Register the model in the MLflow Model Registry using mlflow.register_model() with the run URI and a descriptive model name.
  5. Transition the model version through stages: None -> Staging -> Production using client.transition_model_version_stage(). Archive previous production versions.
  6. Compare model versions by querying metrics across runs with mlflow.search_runs() and generating comparison tables showing metric improvements between versions.
  7. Generate a model card from the registered model metadata, including training data description, evaluation metrics, intended use, limitations, and ethical considerations. See ${CLAUDE_SKILL_DIR}/assets/model_card_template.md.
  8. Set up automated alerts for model performance degradation by comparing production metrics against baseline thresholds stored in the model registry.

See ${CLAUDE_SKILL_DIR}/assets/example_mlflow_workflow.yaml for a complete workflow configuration.

Examples

Tracking a new image classification model version: Log a ResNet-50 fine-tuned on a custom dataset. Record hyperparameters (lr=0.001, epochs=50, optimizer=Adam), metrics (val_accuracy=0.94, val_loss=0.18, inference_latency_ms=12), and the serialized model artifact. Register as version 3 in the model registry and transition to Staging for validation.

Comparing model versions before production promotion: Query MLflow for all versions of the sentiment-analysis model. Generate a comparison table showing accuracy improved from 0.87 (v2) to 0.91 (v3) while inference latency increased from 8ms to 15ms. Recommend promoting v3 to Production only if latency is acceptable for the use case.

Generating a model card for compliance review: Extract metadata from MLflow model registry version 5: training dataset (100K customer reviews), evaluation results (F1=0.89 on held-out test set), known limitations (struggles with sarcasm and multilingual input), and intended use (customer feedback classification). Output a structured Markdown model card.

Show full SKILL.md (171 more words)Show less

Output

  • MLflow run with logged parameters, metrics, and model artifact
  • Model registry entry with version number and stage assignment
  • Version comparison table with metric deltas across runs
  • Model card in Markdown format documenting lineage, performance, and limitations

Error Handling

ErrorCauseSolution
MLflow connection refusedTracking server not running or wrong URIVerify MLFLOW_TRACKING_URI is correct; start server with mlflow server --host 0.0.0.0 --port 5000
Artifact upload failedInsufficient permissions on artifact storeCheck S3/GCS bucket permissions; verify IAM role has write access to the artifact path
Model registration conflictModel name already exists with incompatible schemaUse a versioned model name or delete the conflicting registry entry
Metrics not loggedMLflow run ended before logging completedEnsure all log_metric() calls happen within the active run context (with mlflow.start_run():)
Stage transition deniedModel version already in target stageArchive the existing version in that stage first, then retry the transition

Resources

© 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 7 other files (scripts, references, assets) in skills/.curated/tracking-model-versions of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/example_mlflow_workflow.yaml
  • assets/model_card_template.md
  • assets/versioning-diagram-brief.md
  • references/README.md
  • scripts/README.md
  • scripts/version_control.py

Open the folder on GitHubat commit cfae287

Compare with similar skills

Tracking Model Versions 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.

Tracking Model Versions compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tracking Model Versions this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.3kAutomated safety check: PassMIT
ML Cv Specialistalirezarezvani/claude-cto-team117—~3.1kAutomated safety check: PassMIT
Senior Data Scientistalirezarezvani/claude-skills28k1 repos~2.3kAutomated safety check: PassMIT
Databricks ML Trainingdatabricks/databricks-agent-skills345—~4.6kAutomated safety check: PassCustom licence
MLflow Experiment TrackingOrchestra-Research/AI-Research-SKILLs13k2 repos~3.9kAutomated safety check: PassMIT
ML Pipeline ExpertJeffallan/claude-skills12k—~1.9kAutomated safety check: PassMIT

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Works with

Categories

Questions about Tracking Model Versions

What does Tracking Model Versions do?

Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. Tracking Model Versions is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin.

When should I use Tracking Model Versions?

Tracking Model Versions fits situations like: asks to manage model versions; track model lineage; log model performance; implement version control f..

How do I install Tracking Model Versions in Claude Code?

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

How do I install Tracking Model Versions in Codex?

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

Can I use Tracking Model Versions 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 tracking-model-versions -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tracking-model-versions, .gemini/skills/tracking-model-versions, .github/skills/tracking-model-versions and .opencode/skills/tracking-model-versions in your project.

What does Tracking Model Versions need to run?

Going by SKILL.md and its folder, Tracking Model Versions needs Python for the scripts in its folder. 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 Tracking Model Versions access the network?

SKILL.md names 3 domains. As links in the text: mlflow.org, dvc.org and modelcards.withgoogle.com. This is read from the text; nothing was executed.

Is Tracking Model Versions 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 Tracking Model Versions use?

Tracking Model Versions 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 Tracking Model Versions use?

About 1.3k tokens (SKILL.md is roughly 5.3k 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 Tracking Model Versions?

Skills that share tags, products or a category with Tracking Model Versions: ML Cv Specialist (alirezarezvani/claude-cto-team, 117 stars), Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), Databricks ML Training (databricks/databricks-agent-skills, 345 stars) and MLflow Experiment Tracking (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tracking Model Versions?

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.