by OpenAEC-Foundation · Agent Tool · ★ 55
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Frappe_Claude_Skill_Package safe to install? View the security audit →
61 deterministic skills enabling Claude AI to generate flawless Frappe/ERPNext code. Covers 95% of the Frappe Framework surface area across 7 categories. Built on the Agent Skills open standard. 🎯 Why This Exists Claude is powerful, but without domain-specific guidance it generates Frappe/ERPNext code that looks correct but fails in production. The #1 cause of AI-generated Frappe failures: python ❌ WRONG - This fails silently in Server Scripts from frappe.utils import nowdate today = n
git clone https://github.com/OpenAEC-Foundation/Frappe_Claude_Skill_Package.gitTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 55 |
| Forks | 16 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 62/100 |
| Open Issues | 1 |
| Last Updated | 2026-04-01 |
| Created | 2026-01-14 |
| Platforms | claude-code, python |
| Est. Tokens | ~217k |
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Frappe_Claude_Skill_Package is 60 deterministic Claude AI skills for Frappe Framework & ERPNext v14-v16 development and operations. It is categorized as a Agent Tool with 55 GitHub stars.
Frappe_Claude_Skill_Package is primarily written in Python. It covers topics such as agent-skills, agentskills, ai-skills.
The README on GitHub (github.com/OpenAEC-Foundation/Frappe_Claude_Skill_Package) gives: git clone https://github.com/OpenAEC-Foundation/Frappe_Claude_Skill_Package.git. Usage details are in the same README.
The top alternatives to Frappe_Claude_Skill_Package on Agent Skills Hub include Frappe_Claude_Skill_Package, mcp-erpnext, mcp-erpnext. 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: