catboost

catboost is a skill for Claude Code, Codex from G1Joshi/Agent-Skills. It costs 17 tokens per session (211 once invoked), scanned A, original, MIT.

A machine-learning library that trains gradient-boosted decision trees, which combine many small decision trees to make predictions. CatBoost is designed to work directly with categorical data such as names, labels, and IDs.

In plain words
What is it for?
Use it for prediction or classification on tabular datasets containing many categorical columns, with optional CPU or GPU training.
Why use it?
It avoids manually converting categorical values into numeric columns and uses ordered training to reduce target leakage, where training data accidentally reveals the answer.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for prediction or classification on tabular datasets containing many categorical…

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Install with agentmods
npx agentmods add skills/g1joshi/agent-skills/catboost
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 G1Joshi/Agent-Skills --skill catboost
Clone the repo
git clone --depth 1 https://github.com/G1Joshi/Agent-Skills

Made for: Claude Code, Codex.

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.

agentmods badge for catboost

README.md
[![agentmods](https://agentmods.dev/badge/skills/g1joshi/agent-skills/catboost.svg)](https://agentmods.dev/skills/g1joshi/agent-skills/catboost)
Your own site
<a href="https://agentmods.dev/skills/g1joshi/agent-skills/catboost"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/catboost.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 211 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00017 $0.00211
Opus 5 $0.00009 $0.00105
Sonnet 5 $0.00003 $0.00042
Haiku 4.5 $0.00002 $0.00021

Measured 6d ago against content hash f6fb6423cc48, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

catboost 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 6d 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/ai-ml/catboost/SKILL.md · 39 lines

What it actually says

CatBoost

CatBoost (Yandex) is arguably the easiest boosting library to use because it handles Categorical Features automatically and perfectly without tuning.

When to Use

  • Categorical Data: If you have many strings/IDs, CatBoost is king.
  • Default Params: Works incredibly well out of the box.

Core Concepts

Ordered Boosting

A technique to avoid target leakage (overfitting) during training.

Symmetric Trees

Builds balanced trees, which are faster at inference time.

Best Practices (2025)

Do:

  • Use pool: Pool() is efficient for data loading.
  • Use GPU: CatBoost's GPU implementation is highly optimized.

Don't:

  • Don't One-Hot Encode: Let CatBoost handle it natively.

References

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.

  1. 6d ago First seen · 39 lines · 17 tokens per session scan A f6fb6423cc48

Subscribe to this mod's changes

catboost is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 211 once invoked, about $0.0001 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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