TandemLLM: An environment where open models work as agents: a first-level harness with routing, skills, memory

by 0xBakeer · Agent Tool · ★ 74

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

Security audit verdict: SAFE · quality 58/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agentsdgx-sparkexl3inference-enginellm-inferencelocal-ainvfp4quantizationqwenself-hosted

Quick Facts

Stars74
Forks13
LanguagePython
CategoryAgent Tool
LicenseAGPL-3.0
Quality Score58/100
Open Issues19
Last Updated2026-11-04
Created2026-09-28
Platformspython
Est. Tokens~22k

About TandemLLM

TandemLLM <img src="https://img.shields.io/github/stars/0xBakeer/TandemLLM?style=social" alt="sta

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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