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 · ★ 104
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is deep-plan safe to install? View the security audit →
/deep-plan, a Claude Code plugin Blog posts: - Building /deep-plan - The story behind this plugin - The Deep Trilogy - How the three plugins work together - What I Learned - Technical lessons from plugin development transforms vague feature requests into detailed, production-ready implementation plans through AI-assisted research, stakeholder interviews, and multi-LLM review. , its companion plugin, takes these section files and implements them with TDD methodology, integrated code review, and atomic commits. For large projects with broad, vague requirements, use first to decompose into focused planning units before running on each.
| Stars | 104 |
| Forks | 19 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 72.5751844051748/100 |
| Open Issues | 7 |
| Last Updated | 2026-06-21 |
| Created | 2026-01-15 |
| Platforms | claude-code, python |
| Est. Tokens | ~18k |
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deep-plan is Claude Code plugin that transforms vague requirements into detailed implementation plans via research, interviews, and multi-LLM review.. It is categorized as a Claude Skill with 104 GitHub stars.
deep-plan is primarily written in Python. It covers topics such as architecture, claude-code, claude-code-plugin.
You can find installation instructions and usage details in the deep-plan GitHub repository at github.com/piercelamb/deep-plan. The project has 104 stars and 19 forks, indicating an active community.
deep-plan is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to deep-plan on Agent Skills Hub include alfred-dev, director-mode-lite, optimus-claude. 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: