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 popup-studio-ai · Agent Tool · ★ 591
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bkit-claude-code safe to install? View the security audit →
bkit - Vibecoding Kit PDCA methodology + CTO-Led Agent Teams + AI coding assistant mastery for AI-native development bkit is a Claude Code plugin that transforms how you build software with AI. It provides structured development workflows, automatic documentation, and intelligent code assistance through the PDCA (Plan-Do-Check-Act) methodology. What is Context Engineering? Context Engineering is the systematic curation of context tokens for optimal LLM inference—going beyond simple prompt crafting to build entire systems that consistently guide AI behavior. bkit is a practical implementation of Context Engineering, providing a systematic context management system for Claude Code. bkit's Context Engineering Architecture  using comman
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bkit-claude-code is bkit Vibecoding Kit - PDCA methodology + Claude Code mastery for AI-native development. It is categorized as a Agent Tool with 591 GitHub stars.
bkit-claude-code is primarily written in JavaScript. It covers topics such as ai-agents, ai-driven-work, ai-native-development.
You can find installation instructions and usage details in the bkit-claude-code GitHub repository at github.com/popup-studio-ai/bkit-claude-code. The project has 591 stars and 151 forks, indicating an active community.
bkit-claude-code is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to bkit-claude-code on Agent Skills Hub include bkit-claude-code, cad2data-Revit-IFC-DWG-DGN, OpenConstructionEstimate-DDC-CWICR. 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: