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 nagisanzenin · Claude Skill · ★ 1.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is engram safe to install? View the security audit →
Claude can explain anything. Engram makes sure you still know it next month. Then, inside Claude Code: That's the whole onboarding. No config, no account, no cards to write. Requires (stock macOS/Linux one is fine — stdlib only). Wait — what is this? You already ask Claude to explain things. It explains beautifully. You nod, you feel smart, and ten days later it's gone — because a chat has no memory of you, no test of whether you really got it, and no plan for the forgetting that starts the moment you close the terminal. Engram is what's missing around the explanation: a tutor that makes you do the thinking, an examiner that checks you actually got it, and a scheduler that
| Stars | 1,377 |
| Forks | 101 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 62.5908708063342/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-27 |
| Created | 2026-07-05 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
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engram is Evidence-based learning engine — first-principles curricula, free-recall verification with receipts, FSRS-scheduled memory, and explorable artifacts. Learn anything; keep it.. It is categorized as a Claude Skill with 1.4k GitHub stars.
engram is primarily written in Python. It covers topics such as claude-code, claude-code-plugin, education.
You can find installation instructions and usage details in the engram GitHub repository at github.com/nagisanzenin/engram. The project has 1.4k stars and 101 forks, indicating an active community.
engram is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to engram on Agent Skills Hub include human-skill-tree, learn-faster-kit, antivibe. 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: