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 piercelamb · Claude Skill · ★ 222
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is deep-project safe to install? View the security audit →
/deep-project, a Claude Code plugin Blog posts: - The Deep Trilogy - How the three plugins work together - What I Learned - Technical lessons from plugin development transforms vague, high-level project requirements into well-scoped planning units through AI-assisted interview and decomposition. It ensures you've thought through every major component of the software you want to build and properly scoped them for thorough planning through . This plugin is the first step in the deep planning pipeline. After decomposition, each unit can be fed to for comprehensive planning with research, external LLM review, and a TDD approach. I built this after the first time I wanted to put a vague, broadly scoped software project through . I realized that at a certain level
| Stars | 222 |
| Forks | 31 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 72.1837529541919/100 |
| Last Updated | 2026-06-21 |
| Created | 2026-01-29 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
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deep-project is Claude Code plugin that transforms vague software ideas into individual, ready-to-be-planned components. It is categorized as a Claude Skill with 222 GitHub stars.
deep-project is primarily written in Python. It covers topics such as claude-code, claude-code-plugin, planning.
You can find installation instructions and usage details in the deep-project GitHub repository at github.com/piercelamb/deep-project. The project has 222 stars and 31 forks, indicating an active community.
deep-project is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to deep-project on Agent Skills Hub include antigravity-skills, flow-next, gmickel-claude-marketplace. 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: