Monitor With Haoleme
HaolemeApp/Haoleme
Selectively monitor important long-running or resource-intensive commands with Haoleme by prefixing them with hao, so status, output, and completion notifications sync to the mobile app.
Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations.
Install Mlnet
$ npx skills add managedcode/dotnet-skills --skill mlnet -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install managedcode/dotnet-skills mlnet --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .claude/skills/mlnet && rm -rf skills-srcUse ~/.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/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .claude/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnetType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add managedcode/dotnet-skills --skill mlnet -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install managedcode/dotnet-skills mlnet --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .agents/skills/mlnet && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .agents/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add managedcode/dotnet-skills --skill mlnet -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install managedcode/dotnet-skills mlnet --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .cursor/skills/mlnet && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .cursor/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/managedcode/dotnet-skills.git --path catalog/Frameworks/ML.NET/skills/mlnet--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add managedcode/dotnet-skills --skill mlnet -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install managedcode/dotnet-skills mlnet --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .gemini/skills/mlnet && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .gemini/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install managedcode/dotnet-skills mlnetInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add managedcode/dotnet-skills --skill mlnet -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .github/skills/mlnet && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .github/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add managedcode/dotnet-skills --skill mlnet -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install managedcode/dotnet-skills mlnet --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/managedcode/dotnet-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/catalog/Frameworks/ML.NET/skills/mlnet .opencode/skills/mlnet && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mlnet" agent skill from https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/ML.NET/skills/mlnet into .opencode/skills/mlnet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mlnet", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mlnet- GitHub stars
- 486
- Token cost
- ~559 tokens
- SKILL.md length
- 199 words
- Files
- 4 (incl. references)
- Skills in repo
- 81
- Repo updated
- First seen
- Licence
- MIT
At a glance
Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations.
- Works in 6 steps: Start from the prediction task and data… → Separate training code from inference… → Review feature engineering,… → …
- : ML.NET integration
- SKILL.md covers Trigger On, Workflow, Deliver and Validate, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mlnet is an agent skill from managedcode/dotnet-skills. Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when…
Its SKILL.md is about 560 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `manifest.json`, `references/examples.md` and `references/patterns.md`). Compatibility notes: Requires ML.NET, Model Builder, or ML.NET CLI scenarios.
It sits in Data & Analytics, covering Machine learning, Fine-tuning and Codebase knowledge for agents. It works with .NET. The repository describes itself as: Installable .NET skill catalog and CLI for Codex, Claude Code, GitHub Copilot, and Gemini. The licence is MIT.
When your agent uses it
- : ML.NET integration
- Local model training
- Inference pipelines
- Deployment review
Example prompts
- “/mlnet”
Requirements
- Compatibility (from SKILL.md): Requires ML.NET, Model Builder, or ML.NET CLI scenarios.
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Start from the prediction task and data quality, not the algorithm or package list.
- Separate training code from inference code so the production path stays lean and predictable.
- Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
- Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production…
- Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
- Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.
What it can do on your machine
Read from SKILL.md and the folder at commit 535dd55. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
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
Requires ML.NET, Model Builder, or ML.NET CLI scenarios.
From compatibility in the SKILL.md frontmatter.
Context cost
Mlnet loads about 559 tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 135 tokens; SKILL.md has 199 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from managedcode/dotnet-skills at commit 535dd55, republished under its MIT licence (© managedcode). 199 words, ~559 tokens.
Download SKILL.mdSave it as
.claude/skills/mlnet/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- mlnet
- description
- Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
- compatibility
- Requires ML.NET, Model Builder, or ML.NET CLI scenarios.
ML.NET
Trigger On
- integrating machine learning into a .NET application
- training or retraining ML.NET models from local data
- reviewing inference pipelines, model loading, or AutoML-generated code
Workflow
- Start from the prediction task and data quality, not the algorithm or package list.
- Separate training code from inference code so the production path stays lean and predictable.
- Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
- Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.
- Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
- Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.
Deliver
- ML.NET pipelines that fit the prediction task
- production-usable inference integration
- evaluation evidence tied to the business scenario
Validate
- model quality is measured, not assumed
- training and inference responsibilities are separated
- deployment and versioning expectations are explicit
References
- patterns.md - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns
- examples.md - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML
© managedcode, 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 (references) in catalog/Frameworks/ML.NET/skills/mlnet of managedcode/dotnet-skills.
- SKILL.md
- manifest.json
- references/examples.md
- references/patterns.md
Open the folder on GitHubat commit 535dd55
Compare with similar skills
Mlnet 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mlnet this skillmanagedcode/dotnet-skills | 486 | — | ~559 | Automated safety check: Pass | MIT | |
| Monitor With HaolemeHaolemeApp/Haoleme | 157 | — | ~1.3k | Automated safety check: Pass | AGPL-3.0 | |
| Mle Workflowaffaan-m/ECC | 275k | 1 repos | ~5.6k | Automated safety check: Pass | MIT | |
| Univariate Multivariable Cox Regressionaipoch/medical-research-skills | 2k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Senior Data Scientistborghei/Claude-Skills | 881 | — | ~1.7k | Automated safety check: Pass | MIT | |
| ML Antipattern Validatoraiskillstore/marketplace | 430 | — | ~1.1k | Automated safety check: Pass | None |
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Questions about Mlnet
What does Mlnet do?
Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. Mlnet is an agent skill from managedcode/dotnet-skills.NET applications with realistic data preparation, inference, and deployment expectations.
When should I use Mlnet?
Mlnet fits situations like: : ML.NET integration; local model training; inference pipelines; deployment review.
How do I install Mlnet in Claude Code?
Run `npx skills add managedcode/dotnet-skills --skill mlnet -a claude-code`. Or copy the skill folder (catalog/Frameworks/ML.NET/skills/mlnet in managedcode/dotnet-skills) into .claude/skills/mlnet in your project. Claude Code loads it when a task matches its description.
How do I install Mlnet in Codex?
Run `npx skills add managedcode/dotnet-skills --skill mlnet -a codex`. Or copy the skill folder (catalog/Frameworks/ML.NET/skills/mlnet in managedcode/dotnet-skills) into .agents/skills/mlnet in your project. Codex loads it when a task matches its description.
Can I use Mlnet 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 managedcode/dotnet-skills --skill mlnet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mlnet, .gemini/skills/mlnet, .github/skills/mlnet and .opencode/skills/mlnet in your project.
What does Mlnet need to run?
SKILL.md names no scripts, command-line tools or credentials: Mlnet is instructions for the agent only. Compatibility (from SKILL.md): Requires ML.NET, Model Builder, or ML.NET CLI scenarios..
Does Mlnet 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 Mlnet 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. Review the folder before installing.
What licence does Mlnet use?
Mlnet is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mlnet use?
About 559 tokens (SKILL.md is roughly 2.2k 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 12k tokens, read only when the agent opens those files.
What are the alternatives to Mlnet?
Skills that share tags, products or a category with Mlnet: Monitor With Haoleme (HaolemeApp/Haoleme, 157 stars), Mle Workflow (affaan-m/ECC, 275k stars), Univariate Multivariable Cox Regression (aipoch/medical-research-skills, 2k stars) and Senior Data Scientist (borghei/Claude-Skills, 881 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mlnet?
managedcode (a GitHub organization) maintains it in managedcode/dotnet-skills, which has 486 GitHub stars. The repository holds 81 skills in this directory. The repository was last updated on October 7, 2026.
Source: managedcode/dotnet-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.