AskTheWay

Dsh Auto Memory — Design skill for Claude Code

Design community

Claude Code-style auto-memory plugin for DeepSeek Harness (dsh): typed memory files + MEMORY.md index auto-injected into the system prompt.

How to install Dsh Auto Memory

This entry records only its repository, not the path inside it, so there is no exact command to give. Open AskTheWay/dsh-auto-memory and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Dsh Auto Memory does

Claude Code-style auto-memory plugin for DeepSeek Harness (dsh): typed memory files + MEMORY.md index auto-injected into the system prompt. File-only, no external services.

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README

dsh-auto-memory

[![CI](https://github.com/AskTheWay/dsh-auto-memory/actions/workflows/ci.yml/badge.svg)](https://github.com/AskTheWay/dsh-auto-memory/actions/workflows/ci.yml) [![npm version](https://img.shields.io/npm/v/dsh-auto-memory)](https://www.npmjs.com/package/dsh-auto-memory) [![npm downloads](https://img.shields.io/npm/dm/dsh-auto-memory)](https://www.npmjs.com/package/dsh-auto-memory) [![License: MIT](https://img.shields.io/npm/l/dsh-auto-memory)](LICENSE) [![Node](https://img.shields.io/node/v/dsh-auto-memory)](package.json)

[English](README.md) | [中文](README.zh.md)

Your dsh agent forgets everything you tell it. Every. Single. Session.

**Fix it with one command.** Claude Code-style persistent memory for [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) — native, zero servers, zero embeddings, zero setup.

dsh plugin --profile demo add dsh-auto-memory

Say *"Remember: I'm a Python backend engineer preparing for interviews"* today — open a brand-new session tomorrow, ask *"what do you know about me?"*, and it **remembers**.

What's new in 0.3.0 (P2)

Pinned memories (pinned: true on memory_write): pinned entries lead the index, survive budget truncation, and are exempt from staleness eviction — a trust anchor the user controls.
Eval-driven fix: the injection budget now covers the whole section (index + guidance); it used to overshoot by ~800 bytes. Caught by the new deterministic evaluation layer on its first run.
Deterministic eval layer (evals/) in CI: injection budget curves, eviction zero-misfire, link-expansion bounds, and a signal-to-noise characterization — which pinned-priority truncation then improved from 38% → ≥80% probe retention under half-budget pressure. Same budget, better memories.