ML Engineer
RightNow-AI/openfang
Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps
Build an ML pipeline — from data to trained model to serving endpoint.
Install Cortex Model
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-model --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/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .claude/skills/cortex-model && 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 "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .claude/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-modelType 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 jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-model --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .agents/skills/cortex-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .agents/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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 jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-model --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .cursor/skills/cortex-model && 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 "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .cursor/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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/jeremylongshore/tons-of-skills-marketplace.git --path plugins/ai-agency/tonone/skills/cortex-model--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 jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-model --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .gemini/skills/cortex-model && 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 "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .gemini/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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 jeremylongshore/tons-of-skills-marketplace cortex-modelInstalls 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 jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .github/skills/cortex-model && 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 "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .github/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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 jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace cortex-model --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/ai-agency/tonone/skills/cortex-model .opencode/skills/cortex-model && 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 "cortex-model" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/cortex-model into .opencode/skills/cortex-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cortex-model", 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
cortex-model- GitHub stars
- 2.8k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 469 words
- Files
- 2
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build an ML pipeline — from data to trained model to serving endpoint.
- Works in 9 steps: Detect Environment → Define Success Metric → Build Simplest Baseline First → …
- Asked to build ML model
- SKILL.md covers Steps and Delivery
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cortex Model is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `.claude-plugin/plugin.json`).
It sits in Data & Analytics, covering Machine learning and MLOps. 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 build ML model
- Prediction pipeline
Example prompts
- “build ML model”
- “train a model”
- “prediction pipeline”
- “/cortex-model”
Requirements
- Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion
Workflow steps
9 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 80f86df. 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:
ReadWriteEditBashGlobGrepWebFetchWebSearchTaskTodoWrite
…and 1 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
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.
Context cost
Cortex Model loads about 1.2k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 469 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: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion
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 jeremylongshore/tons-of-skills-marketplace at commit 80f86df, republished under its MIT licence (© jeremylongshore). 469 words, ~1,215 tokens.
Download SKILL.mdSave it as
.claude/skills/cortex-model/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- cortex-model
- description
- Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".
- allowed-tools
- Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion
- version
- 0.6.4
- author
- tonone-ai <hello@tonone.ai>
- license
- MIT
Build an ML Pipeline
You are Cortex — the ML/AI engineer on the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Scan the project to understand the ML stack:
# Check for training scripts, ML dependencies, model configs
ls -la *.py train* model* 2>/dev/null
cat requirements.txt 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|keras|jax"
cat pyproject.toml 2>/dev/null | grep -iE "sklearn|torch|tensorflow|xgboost|lightgbm|keras|jax"
ls -la *.yaml *.yml *.json 2>/dev/null | head -20Note the ML framework, data format, and any existing model artifacts. If nothing is detected, ask the user what they're building.
Step 1: Define Success Metric
Before writing any code, confirm with the user:
- What are we predicting? (classification, regression, ranking, generation)
- What metric matters? (accuracy, F1, RMSE, AUC, latency, cost)
- What's the baseline? (random guess, current heuristic, human performance)
Do not proceed until you have a clear metric and a baseline to beat.
Step 2: Build Simplest Baseline First
Start simple. A logistic regression in production beats a transformer in a notebook.
- Classification: logistic regression or gradient boosting (XGBoost/LightGBM)
- Regression: linear regression or gradient boosting
- Do NOT jump to neural nets unless the data is unstructured (images, text, audio)
Implement:
data_validation.py — schema checks, null handling, type validation
features.py — feature engineering pipeline (same code for train and serve)
train.py — training script with experiment tracking
evaluate.py — evaluation against the success metricStep 3: Data Validation
Before any training, validate the data:
- Check for nulls, duplicates, and schema violations
- Verify feature distributions (look for data leakage)
- Split data properly (time-based for time series, stratified for imbalanced classes)
- Log dataset statistics (row count, feature stats, label distribution)
Step 4: Feature Engineering
Build a feature pipeline that works identically for training and serving:
- Extract features in a reusable function/class
- Document each feature (what it is, why it matters)
- Watch for training/serving skew — this is the #1 silent killer
- Version the feature pipeline alongside the model
Step 5: Training Script
Implement the training script with:
- Reproducibility: set random seeds, log hyperparameters
- Experiment tracking: log metrics, parameters, and artifacts
- Model serialization: save the trained model in a portable format (joblib, ONNX, or framework-native format)
- Cross-validation or proper holdout evaluation
Show full SKILL.md (171 more words)Show less
Step 6: Evaluation
Evaluate against the success metric from Step 1:
- Compare to baseline — if you can't beat the baseline, the model isn't ready
- Error analysis — what is the model getting wrong? Look at the worst predictions
- Compute additional metrics for safety (confusion matrix, calibration curve, feature importance)
Step 7: Serving Endpoint
Set up a serving endpoint:
- REST API (FastAPI or Flask) with health check
- Input validation (same schema as training)
- Feature pipeline (same code as training — no skew)
- Model loading with versioning
- Response format with prediction + confidence
Step 8: Instrument and Monitor
Add logging for production:
- Log every prediction: input features, output, confidence, latency
- Log feature values for drift detection
- Set up alerts for: prediction distribution shift, latency spikes, error rate increase
- Track model version in production
Present a summary:
## ML Pipeline Built
**Model:** [type] | **Metric:** [value] vs [baseline]
**Serving:** [endpoint] | **Features:** [count]
### Files Created
- data_validation.py — input validation
- features.py — feature pipeline
- train.py — training script
- evaluate.py — evaluation
- serve.py — serving endpoint
### Next Steps
- [ ] Set up scheduled retraining
- [ ] Add A/B testing capability
- [ ] Monitor prediction driftDelivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
© 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 1 other file in plugins/ai-agency/tonone/skills/cortex-model of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- .claude-plugin/plugin.json
Open the folder on GitHubat commit 80f86df
Compare with similar skills
Cortex Model 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 |
|---|---|---|---|---|---|---|
| Cortex Model this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.2k | Automated safety check: Notes | MIT | |
| ML EngineerRightNow-AI/openfang | 18k | — | ~987 | Automated safety check: Pass | Apache-2.0 | |
| Plot ML Figureprobabl-ai/skills | 138 | — | ~785 | Automated safety check: Pass | BSD-3-Clause | |
| Research ML Practiceprobabl-ai/skills | 138 | — | ~1.1k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data Scientistborghei/Claude-Skills | 886 | — | ~1.7k | Automated safety check: Pass | MIT | |
| ML Pipelinehashgraph-online/awesome-codex-plugins | 1.3k | — | ~3.2k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Cortex Model
What does Cortex Model do?
Build an ML pipeline — from data to trained model to serving endpoint. Cortex Model is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build an ML pipeline — from data to trained model to serving endpoint.
When should I use Cortex Model?
Cortex Model fits situations like: asked to build ML model; prediction pipeline.
How do I install Cortex Model in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a claude-code`. Or copy the skill folder (plugins/ai-agency/tonone/skills/cortex-model in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/cortex-model in your project. Claude Code loads it when a task matches its description.
How do I install Cortex Model in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill cortex-model -a codex`. Or copy the skill folder (plugins/ai-agency/tonone/skills/cortex-model in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/cortex-model in your project. Codex loads it when a task matches its description.
Can I use Cortex Model 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 cortex-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cortex-model, .gemini/skills/cortex-model, .github/skills/cortex-model and .opencode/skills/cortex-model in your project.
What does Cortex Model need to run?
SKILL.md names no scripts, command-line tools or credentials: Cortex Model is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep, WebFetch, WebSearch, Task, TodoWrite, AskUserQuestion.
Does Cortex Model 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 Cortex Model safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Cortex Model use?
Cortex Model 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 Cortex Model use?
About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cortex Model?
Skills that share tags, products or a category with Cortex Model: ML Engineer (RightNow-AI/openfang, 18k stars), Plot ML Figure (probabl-ai/skills, 138 stars), Research ML Practice (probabl-ai/skills, 138 stars) and Senior Data Scientist (borghei/Claude-Skills, 886 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cortex Model?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 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.