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 houtini-ai · MCP Server · ★ 118
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is houtini-lm safe to install? View the security audit →
@houtini/lm Houtini LM - Save Tokens by Offloading Tasks from Claude Code to Your Local LLM Server (LM Studio / Ollama) or a Cloud API Quick Navigation How it works Developer guide I built this because I kept leaving Claude Code running overnight on big refactors and the token bill was painful. A huge chunk of that spend goes on bounded tasks any decent model handles fine - generating boilerplate, code review, commit messages, format conversion. Stuff that doesn't need Claude's reasoning or tool
| Stars | 118 |
| Forks | 26 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 72.1667082858118/100 |
| Open Issues | 11 |
| Last Updated | 2026-09-22 |
| Created | 2025-09-04 |
| Platforms | claude-code, cli, mcp, node |
| Est. Tokens | ~20k |
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houtini-lm is MCP server that saves Claude Code tokens by delegating bounded tasks to local or cloud LLMs. Works with LM Studio, Ollama, vLLM, DeepSeek, Groq, Cerebras.. It is categorized as a MCP Server with 118 GitHub stars.
houtini-lm is primarily written in TypeScript. It covers topics such as ai-agents, claude, claude-mcp.
You can find installation instructions and usage details in the houtini-lm GitHub repository at github.com/houtini-ai/houtini-lm. The project has 118 stars and 26 forks, indicating an active community.
houtini-lm is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to houtini-lm on Agent Skills Hub include ollama-mcp-bridge, google-analytics-mcp, trace-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: