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engram

Evidence-based learning engine — first-principles curricula, free-recall verification with receipts, FSRS-scheduled memory, and explorable artifacts. Learn anything; keep it.

PluginProductivityKnowledgeMemoryFile

Verification

L3 · Install spec

Risk

Out of scope

Health

Stable

Trust

Silver

Install verification: No L4/L5 install test yetInstall verification details →

What it doesAI

Evidence-based learning engine for Claude Code with spaced-repetition scheduling and explorable artifacts.

First-principles curricula
Free-recall verification with receipts
FSRS-scheduled memory

productivityAImemoryAIfileAI

Capability tags are AI-inferred or rule-extracted from the README (see badge) — vocabulary-limited, not install-verified. AI = inferred; README = literal keyword from the readme.

Installation

dsh plugin --profile web add github:nagisanzenin/engram

verified 825cacb5c494a6c3c16548e59e345fad7ce9da02

Install method: GitHub · install spec verified (L3)

Verification & Compatibility

Grouped by plugin version: each card lists the dsh versions actually tested against that artifact version and their install verdicts (L5 wins). Untested combinations are omitted — absence means untested, never assumed compatible. L1–L3 are a plugin-level static archive (independent of the dsh version — the same verdict on every dsh row): they sit in each row's level grid next to L4/L5, marked "static", while their evidence is stored once per plugin inside that row's "checks & evidence" instead of being duplicated per version.

External credentials (plugin-level): none detected

Install compatibility (by plugin version, dsh versions within)

Legend:✓ L5 runtime verified○ ecosystem plugin◼ ecosystem app✗ failed

825cacgithub2026-08-24

https://github.com/nagisanzenin/engram

No runtime install test for this version (static scan only) — compatibility unknown, never assumed.

Author badges

Embed the official badges in your README to showcase security & install-test status:

Security Report

Automated static scan, not manual review.

DSH.SO VETMEDIUMcbd56c

Security reviewAutomated review · daily

MEDIUM

Vet verdict is suspicious: findings need human review (exfiltration endpoints, file deletion, etc.).

0 critical·2 high·8 medium

Version binding

plugin versionv1.14.0

scanned version1.14.0git2026-08-25

current version825cacb5c494version moved on

vet verdictsuspicious · npm

Vet is an AST static scan (npm artifact or git source).

Disclaimer: automated static analysis, not a security guarantee. Always review what you install.

Risk details

Risk	Rule	Location	Description
high	R2	harness.cjs:5	require('child_process') 危险内置模块（执行/网络能力触达）
high	R2	mock-llm.cjs:5	require('http') 危险内置模块（执行/网络能力触达）
medium	R9	install.ts:151	正则嵌套量词（ReDoS 风险：(a+)+ 类指数回溯）
medium	R2	harness.cjs:6	require('fs') 标准内置模块
medium	R2	harness.cjs:7	require('path') 标准内置模块

Heuristic static scan — may produce false positives. Review the source yourself before trusting.

Activity

Last commit 2026-08-27 · activity: Stable

• Repo created: 2026-07-05

• Stars: ★ 1,436 · Forks: 104

• Health: Stable — committed within 90 days

Source

GitHub: github.com/nagisanzenin/engram

Plugin notes

What is the engram DeepSeek Harness plugin?

engram is a DeepSeek Harness plugin maintained by @nagisanzenin. It is cataloged on dsh.so as a Productivity plugin for Memory tasks, with a public GitHub source, declared install command, and automated static security scan.

How do I install engram?

Run dsh plugin --profile web add github:nagisanzenin/engram in a DeepSeek Harness (dsh) environment. The command is parsed from the plugin manifest and its install spec is verified (L3); runtime sandbox install testing may not have run yet.

How does the security scan relate to a manual audit?

The scan is automated static analysis of the repository source — a scalable first line of defense that can produce false positives and miss context; it is not a manual security audit. Before using a plugin, review the repository and installed permissions, and combine the scan with the verification level (L4/L5) and health signals.

"DeepSeek Harness (dsh) is an open-source agent harness developed by DeepSeek AI. It uses an architecture where everything is a plugin, and is powered by Cordis."
— DeepSeek Harness README

Sources: GitHub repository · DeepSeek Harness README · security scan data · Maintained by dsh.so · Last updated 2026-09-26