by hegelai · Agent Tool · ★ 3.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install prompttools
git clone https://github.com/hegelai/prompttools.gitTaken 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 | 3,055 |
| Forks | 256 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 70/100 |
| Open Issues | 43 |
| Last Updated | 2026-02-11 |
| Created | 2023-06-25 |
| Platforms | python |
| Est. Tokens | ~2207k |
PromptTools :wrench: Test and experiment with prompts, LLMs, and vector databases. :hammer: Welcome to created by Hegel AI! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground.
Looking for a prompttools alternative? If you're comparing prompttools with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
🌟 A curated collection of free, high quality AI tools 🤖, APIs 🔗, datasets 📊, and learning resources 📚 cov
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation,
Semantic Search & Call Graphs for AI Agents (100% Local)
🦙 Integrating LLMs into structured NLP pipelines
Build agents which are controlled by LLMs
🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from promp
Explore other popular agent tool tools:
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: