by Scale3-Labs · LLM Plugin · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install langtrace-python-sdkTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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.
| Stars | 1,232 |
| Forks | 127 |
| Language | TypeScript |
| Category | LLM Plugin |
| License | AGPL-3.0 |
| Quality Score | 65/100 |
| Open Issues | 2 |
| Last Updated | 2025-11-17 |
| Created | 2024-03-30 |
| Platforms | node |
| Est. Tokens | ~261k |
Langtrace Open Source Observability for LLM Applications [ is an open-source, cloud-native runtime for agentic AI. Secure, modular,
A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonom
The only fully local production-grade Super SDK that provides a simple, unified, and powerful interface for ca
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
Zero-Config Code Flow for Claude code & Codex
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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: