A command-line toolkit for machine learning, the process of training computers to find patterns in data and make predictions. It covers data checks, preparation, model training, evaluation, reports, monitoring, and serving models through web APIs.

Use it to profile datasets, detect anomalies or data drift, forecast time series, check fairness, tune models, generate HTML reports, save reusable model packages, and expose models as REST APIs.

It groups common machine-learning tasks into one workflow and can automate model selection, tuning, reports, and monitoring instead of requiring each step to be built separately.

scomp-link — Skill for Claude Code by GiacomoSaccaggi

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add GiacomoSaccaggi/scomp_link --skill scomp-link

Clone the repo

git clone --depth 1 https://github.com/GiacomoSaccaggi/scomp_link

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/giacomosaccaggi/scomp_link/scomp-link.svg)](https://agentmods.dev/skills/giacomosaccaggi/scomp_link/scomp-link)

Your own site

<a href="https://agentmods.dev/skills/giacomosaccaggi/scomp_link/scomp-link"><img src="https://agentmods.dev/badge/skills/giacomosaccaggi/scomp_link/scomp-link.svg" alt="Measured on agentmods" height="20"></a>

Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,139 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00074	$0.03139
Opus 5	$0.00037	$0.01570
Sonnet 5	$0.00015	$0.00628
Haiku 4.5	$0.00007	$0.00314

Measured 7d ago against content hash c70d90ef31ed, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

scomp-link scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/scomp-link/SKILL.md · 286 lines

How it starts

The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.

scomp-link — End-to-End ML Toolkit

Overview

scomp-link automates the complete ML workflow: data profiling → preprocessing → feature engineering → model selection → training → validation → explainability → monitoring → deployment.

Use scomp-link instead of raw sklearn when you need:

Zero-code ML via CLI (26 commands)
Automated model selection based on data characteristics
Persistent artifacts (.scomp format: model + preprocessor + config + metrics)
HTML reports with embedded interactive charts
Production monitoring (drift + anomaly + fairness)
One-command hyperparameter tuning (Optuna/Halving)

Use raw sklearn when you need:

Custom model architectures not in the factory
Fine-grained control over every preprocessing step
Research workflows requiring full flexibility

Installation

pip install scomp-link

Decision Tree: Which Command to Use

I have data and want to...
├─ Understand it quickly          → scomp-link describe --data file.csv
├─ Full quality report (HTML)     → scomp-link quality --data file.csv --output report.html
├─ Engineer features              → scomp-link engineer --data file.csv --target y --interactions --log-transform
├─ Train a model
│  ├─ Regression                  → scomp-link run --data file.csv --target y --task regression
│  ├─ Classification              → scomp-link run --data file.csv --target y --task classification
│  ├─ Text classification         → scomp-link text --data file.csv --text-col msg --target label
│  ├─ Clustering                  → scomp-link cluster --data file.csv --n-clusters 5
│  └─ Full pipeline from YAML     → scomp-link pipeline --config pipeline.yaml
├─ Tune hyperparameters           → scomp-link tune --data file.csv --target y --task regression --method optuna
├─ Predict with saved model       → scomp-link predict --artifact model.scomp --data new.csv
├─ Validate on test data          → scomp-link validate --artifact model.scomp --data test.csv --target y
├─ Explain model decisions        → scomp-link explain --artifact model.scomp --data test.csv
├─ Monitor production
│  ├─ Drift only                  → scomp-link drift --reference train.csv --current prod.csv
│  ├─ Full monitoring             → scomp-link monitor --reference train.csv --current prod.csv --artifact model.scomp
│  └─ Anomaly detection           → scomp-link anomaly --data prod.csv --methods iforest,lof,tabnet,transformer
├─ Check fairness/bias            → scomp-link fairness --data preds.csv --target y_true --predicted y_pred --sensitive gender
├─ Forecast time series           → scomp-link forecast --data series.csv --column value --horizon 30
├─ Compare models                 → scomp-link compare --artifacts v1.scomp v2.scomp
├─ Generate HTML report           → scomp-link report --data file.csv --output report.html
├─ Serve as REST API              → scomp-link serve --artifact model.scomp --port 8080
├─ Export to ONNX/pickle          → scomp-link export --artifact model.scomp --format onnx
├─ Scaffold a new project         → scomp-link init my_project
├─ Configure branding defaults    → scomp-link init-config
└─ Use declarative >> DSL         → see Pipeline DSL section below

Read the full file on GitHub · 286 lines

Files

What ships with it

10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 286 lines · 74 tokens per session scan A c70d90ef31ed

Subscribe to this mod's changes

scomp-link is a skill published in the GitHub repository GiacomoSaccaggi/scomp_link (12 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 3,139 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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