Kiln — security grade SAFE, quality 58/100

Security audit verdict: SAFE · quality 58/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by Kiln-AI · MCP Server · ★ 5.1k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About Kiln

A free app and open-source library to build better AI products. Highlights • Evals • Auto-Optimize • <a href="https://docs.kiln.tech/docs/d

aichain-of-thoughtcollaborationdataset-generationevalsevaluationevaluation-frameworkfine-tuningmachine-learningmacos

Quick Facts

Stars5,078
Forks380
LanguagePython
CategoryMCP Server
Quality Score58.2614371042702/100
Open Issues74
Last Updated2026-09-21
Created2024-07-23
Platformsmcp, python
Est. Tokens~16k

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Frequently Asked Questions

What is Kiln?

Kiln is Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.. It is categorized as a MCP Server with 5.1k GitHub stars.

What programming language is Kiln written in?

Kiln is primarily written in Python. It covers topics such as ai, chain-of-thought, collaboration.

How do I install or use Kiln?

You can find installation instructions and usage details in the Kiln GitHub repository at github.com/Kiln-AI/Kiln. The project has 5.1k stars and 380 forks, indicating an active community.

What are the best alternatives to Kiln?

The top alternatives to Kiln on Agent Skills Hub include sdk-python, tools, aide. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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