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 →
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🔒 Is dory safe to install? View the security audit →
Dory Stateless context management for LLM sessions. Long, continuous chat threads drain token quotas, dilute context, and tie your project state to ephemeral browser instances. Dory treats hosted LLM interfaces as disposable tools. By offloading state to your local environment and enforcing atomic sessions, you maintain a low baseline token usage and secure your codebase history. 🧠 Strategy 📂 Repository Structure Dory integrates directly into your project's version control. te
| Stars | 58 |
| Forks | 0 |
| Language | Python |
| Category | AI Skill |
| License | MIT |
| Quality Score | 58.0908896683372/100 |
| Last Updated | 2026-09-28 |
| Created | 2026-06-10 |
| Platforms | claude-code, gemini, python |
| Est. Tokens | ~14k |
These tools work well together with dory for enhanced workflows:
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dory is Dory treats hosted LLM interfaces as disposable tools by offloading state to your local environment and enforcing atomic sessions.. It is categorized as a AI Skill with 58 GitHub stars.
dory is primarily written in Python. It covers topics such as ai, ai-tools, awesome-ai.
You can find installation instructions and usage details in the dory GitHub repository at github.com/tjqscott/dory. The project has 58 stars and 0 forks, indicating an active community.
dory is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dory on Agent Skills Hub include claudepro-directory, graphmind, claude-historian-mcp. 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: