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863683348/dsh-memory-setup

插件Plugin 原生Native ⭐ 2 cat_label.deepseek-harnesscat_label.deepseek-harness记忆Memorycat_label.personal-knowledgecat_label.personal-knowledge安全Security

解决AI金鱼脑：DeepSeek Harness的可审计个人记忆——偏好、项目约定、工作流程、错误教训。解决AI金鱼脑的本地可审计记忆层。

项目介绍Project Overview

由知微Agent(zhiwei)编辑Edited by Zhiwei Agent (zhiwei)

dsh-memory-setup 是 DeepSeek Harness 的本地个人记忆层插件，以纯 JSON 存储偏好、项目约定、工作流与纠错教训，并在会话启动时注入上下文。它支持记忆更新、项目约定提取、知识库检索、快照恢复、审计与导入导出。适合需要跨会话继承习惯、避免重复犯错时使用。注意：记忆插件属最高信任类型，数据虽本地存储，仍应审阅其安全说明。

dsh-memory-setup is a local personal memory plugin for DeepSeek Harness. It stores preferences, project conventions, workflows, and error lessons in plain JSON and injects relevant memory into sessions. It supports updates, project extraction, knowledge search, snapshots, restore, audit, tiers, and backup. Use it when context should persist across sessions or repeated mistakes should become conventions. Caveat: memory plugins are highest-trust; review SECURITY.md before use.

在 GitHub 查看View on GitHub

强烈推荐Recommended 发给你的 DSH 安装Send to your DSH 
请帮我了解并安装插件：【dsh-memory-setup】【https://github.com/863683348/dsh-memory-setup】

把上面这条消息直接发给当前会话里的 DSH，让它帮你了解并安装。安装命令不一定准确，发给 DSH 更稳。Send this message to DSH in your current session. CLI install commands may not be accurate across systems — DSH will figure it out for you.

或使用命令行安装（适合开发者）Or use CLI install (for developers)

命令行安装CLI Install 

dsh plugin --profile web add dsh-memory-setup

把 863683348/dsh-memory-setup 加入你的 DSH 配置（web profile）即可启用。

READMEREADME

6.7 KB · 缓存于 2026-08-27 05:42:12 · 6.7 KB

dsh-memory-setup

Solve the AI goldfish brain 🐠 — a local, auditable personal memory layer for DeepSeek Harness. Remembers your preferences, project conventions, workflows, and error lessons, and injects them back into every session.

解决 AI 的"金鱼脑"：本地、可审计的个人记忆层——偏好、项目约定、工作方式、纠错教训，会话间自动继承。

Install

dsh plugin --profile <profile> add dsh-memory-setup

Tools

Tool What it does
memory_setup One-time onboarding: language, code style, tools, conventions, workflows
memory_status Read current memory + changelog (also auto-injected guidance at boot)
memory_update Update one memory path (e.g. preferences.codeStyle) with a changelog entry
memory_project Auto-extract project conventions from workspace files (README / package.json / configs), preview or apply
memory_lesson Record an error lesson (error → fix → evidence) so the same mistake is not repeated
memory_review v0.2 — formalize an incident into a lesson with root cause; similar lessons are auto-merged (dedupe + hit counter)
memory_export v0.2 — export the full memory + changelog to a Markdown file for review/backup
knowledge_add v0.3 — add a knowledge entry (title/content/tags/source); similar titles auto-merge
knowledge_search v0.3 — keyword retrieval (title ×3 / tags ×2 / content ×1 scoring)
knowledge_list / knowledge_remove v0.3 — browse / delete knowledge entries
memory_diff v0.4 — diff current memory against memory.json.bak, optionally written to memory-diff.md
memory_review_session v0.5 — bulk incident review: submit many failures at once, dedupe per item
memory_snapshot / memory_list_snapshots / memory_restore v0.6 — snapshot the memory (keeps N), list, and restore with auto-backup of the current state
memory_troubleshoot v0.6 — given an error, search past lessons + knowledge base for a known fix
memory_stats v0.7 — aggregate stats across memory, knowledge base and snapshots
memory_promote v0.7 — promote recurring lessons (hits ≥ threshold) into standing conventions; auto-runs on save
memory_import / memory_merge v0.8 — import memory from JSON (auto-migrate) / merge two memories (newer or both)
kb_export / kb_import v0.8 — knowledge base JSON round-trip
memory_focus v0.9 — relevance-based injection: only memory matching a topic is injected
memory_tier v1.0 — hot/warm/cold tiers (hot is injected, cold is archived)
memory_audit v1.0 — sha256 integrity check + changelog audit report
memory_import_claude v1.1 — import conventions from CLAUDE.md
memory_export_all / memory_import_all v1.2 — full backup bundle (memory + KB + snapshots)
memory_annotate v1.2 — owner/purpose annotations on entries
knowledge_embed v0.5 — backfill embeddings for KB entries (needs embeddingEndpoint); enables semantic search

Storage & auditability

  • Location: <workspace>/.dsh-memory-setup/memory.json — plain JSON, easy to read/back up
  • Every mutation appends to changelog (when / what / why) — memory is auditable by design
  • Lessons carry an optional evidence field (file/command/observation) — no evidence, no lesson
  • Local-first: nothing leaves your machine

Config (optional)

Field Default Description
memoryDir .dsh-memory-setup memory dir relative to the session workspace
injectOnBoot true inject live memory into the system prompt (dynamic context, refreshed on save)
maxMemoryChars 6000 cap for rendered memory text
lessonTtlDays 90 lessons expire after this many days (0 disables)
changelogCap 100 max changelog entries kept
backupOnSave true write memory.json.bak before every save
reviewReminder true append self-review reminder to guidance
embeddingEndpoint (empty) OpenAI-compatible embeddings endpoint (enables semantic KB search)
embeddingKey (empty) Bearer key for the embeddings endpoint
embeddingModel text-embedding-3-small embeddings model name
snapshotKeep 10 max memory snapshots kept
troubleshootReminder true append troubleshoot/snapshot reminder to guidance

Roadmap

  • v0.2 ✅: incident review with dedupe (memory_review), lesson/convention expiry + changelog cap (auto-pruned on save), memory.json.bak backup on every save, Markdown export (memory_export)
  • v0.3 ✅: personal knowledge base (knowledge_*, keyword retrieval, title-merge dedupe); dynamic memory injection via a live systemPrompt.context() section — refreshed at boot (from the workspace path) and after every memory save (throttled 30s), with static guidance as fallback
  • v0.4 ✅: BM25 retrieval for the knowledge base (title ×3 / tags ×2 / content ×1, IDF-scaled — no embeddings, no deps), memory diff export (memory_diff vs backup), self-review reminder in the injected guidance
  • v0.5 ✅: optional embeddings provider (OpenAI-compatible endpoint; knowledge_embed backfill + cosine retrieval, BM25 fallback), bulk incident review (memory_review_session), own-tool fs failure tracking surfaced into the injected context
  • v0.6 ✅: memory snapshots & restore (memory_snapshot / memory_list_snapshots / memory_restore, capped, index-file based), fault troubleshooting (memory_troubleshoot — lessons + knowledge lookup), troubleshoot reminder in guidance
  • v0.7 ✅: KB included in snapshots (snapshot/restore both memory + knowledge), lesson auto-promotion (recurring lessons with hits ≥ threshold become standing conventions, auto-run on save — the memory literally learns from repeated mistakes), memory stats (memory_stats)
  • v0.8 ✅: schema v2 migration (auto on load), memory import/merge (memory_import/memory_merge, newer/both conflicts), knowledge base JSON round-trip (kb_export/kb_import)
  • v0.9 ✅: lesson health evaluation (failing/resolved/active — auto on save, ⚠️ markers in render), relevance-focused injection (memory_focus)
  • v1.0 ✅: tiers (hot/warm/cold), integrity audit (memory_audit, sha256), privacy redaction in exports (sensitiveKeys)
  • v1.1 ✅: optimistic locking (revision-based CAS — multi-session/multi-agent safe), CLAUDE.md import (memory_import_claude)
  • v1.2 ✅: full backup bundle (memory_export_all/memory_import_all — memory + KB + snapshots), annotations (memory_annotate, owner/purpose) — team-ready
  • v1.3+: lesson auto-detection (pending a tool-call event API), web stats view

Security

Memory plugins are the highest-trust plugin type — see SECURITY.md for the audit posture.

渲染基于仓库 863683348/dsh-memory-setup 的 README.md。 在 GitHub 查看原文 →

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