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 PaulDuvall · Agent Tool · ★ 653
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ai-development-patterns safe to install? View the security audit →
AI Development Patterns A comprehensive collection of patterns based on my experience for building software with AI assistance, organized by implementation maturity and development lifecycle phases. These patterns are subject to change as the field evolves. mermaid graph TB %% ROW 1: Foundation start (left to right) RA([ReadinessAssessment]) -- CR([CodifiedRules]) CR -- SS([SecuritySandbox]) SS -- DL([DeveloperLifecycle]) DL -- TI([ToolIntegration]) %% ROW 2: Operations & branches (loops back) TI -- BM([Baseline<
| Stars | 653 |
| Forks | 53 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67.1324999989218/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-14 |
| Created | 2025-06-15 |
| Platforms | python |
| Est. Tokens | ~25k |
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ai-development-patterns is A comprehensive collection of AI development patterns for building software with AI assistance, organized by implementation maturity and development lifecycle phases. Includes Foundation, Development,. It is categorized as a Agent Tool with 653 GitHub stars.
ai-development-patterns is primarily written in Python. It covers topics such as ai-assisted-coding, ai-development, ai-tools.
You can find installation instructions and usage details in the ai-development-patterns GitHub repository at github.com/PaulDuvall/ai-development-patterns. The project has 653 stars and 53 forks, indicating an active community.
ai-development-patterns is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ai-development-patterns on Agent Skills Hub include awesome-agv, slowql, claude-code-skills. 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: