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dsh-habit

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2026-08-21最近推送
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功能简介

Self-learning habit engine for the DeepSeek Harness - correction signals, threshold judgment, two-level human gate

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未验证 — 尚未实测

Self-learning habit engine for the DeepSeek Harness - correction signals, threshold judgment, two-level human gate 尚未验证——请自行安装测试。

「未验证」表示我们的自动化 CI 尚未安装过该插件。功能描述与版本兼容性均为作者声明。这不是安全审计,也不代表对第三方代码的背书。

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README

@max-null/dsh-habit

本插件属于 @max-null/* 插件系列——这一系列共同构成 SSID(思灵 · Seek Soul in Darkness) 桌面体验。SSID 是整合它们的盒:dsh-capture · dsh-chat-rail · dsh-chinese-thinking · dsh-draft-polish · dsh-guardian · dsh-habit · dsh-header-unify · dsh-memory · dsh-node-appearance · dsh-plugin-center · dsh-skill-mcp-center · dsh-ssid-panels · dsh-ssid-zh-ui

This plugin belongs to the @max-null/* family — a set of plugins that together form the SSID (思灵 · Seek Soul in Darkness) desktop experience.

Self-learning habit engine for the DeepSeek Harness — observes user-correction
signals from session events, judges habits with a low-cost model on threshold,
and settles candidates behind a two-level human gate. No new agent role: the
judgment is an event-driven plugin, immune to context decay.

The loop

① observe   session/event → correction-signal detection (deterministic, zero-token)
② judge     >=3 signals in one session → one flash call (evidence slices + existing habits)
③ settle    candidate zone → user confirms → dsh-memory remember() (suggested)
            → user confirms again → auto → recall injection

Compose

- id: habit
  name: '@max-null/dsh-habit'

Requires storage and llm in the host composition (dsh-base ships both).
Installs as a bundle: dsh plugin --profile <name> add @max-null/dsh-habit.

Service

  • ctx.habit — the engine:
    • snapshot() → candidates (newest first)
    • confirm(id) / discard(id) → first-level human gate
    • (the second gate is dsh-memory's own suggested→auto confirmation)

Config

Field Default Meaning
signalThreshold 3 Correction signals before one judgment call
provider deepseek-official Judgment model provider
model deepseek-v4-flash Judgment model (cheap, deterministic)
storageRoot $DSH_HOME/storages/habit JSON storage root

Design notes

  • Deterministic observation, LLM on demand: correction detection is a
    fixed phrase list + length cap (task descriptions are not corrections);
    the LLM only runs when a session accumulates enough signals.
  • Two-level human gate: candidates must be confirmed in the UI AND then
    pass dsh-memory's own suggested→auto gate. The model can never promote its
    own habits.
  • Narrow input for quality: the judgment call gets at most 5 evidence
    texts plus the existing habit list — judgment quality comes from precise
    context, not volume.

Develop

npm install --legacy-peer-deps
npm test
npm run typecheck
npm run build

SSID 系列

安装

🧩 让 Agent 自动装(推荐)

装一次目录插件,之后本站所有插件都能让 DeepSeek Harness 自动找、自动装:

dsh plugin add dshbase-catalog

然后对 agent 说「帮我装 dsh-habit」,它会在目录里找到并自动安装。文档:dshbase-catalog · 已验证场景包

该插件是 GitHub 源码(未发 npm)——直接从仓库装:

Web profile:

dsh plugin --profile web add github:Max-Null/dsh-habit

Headless(CLI)profile:

dsh plugin --profile headless add github:Max-Null/dsh-habit

实测报告

尚未 L3 验证——若已跑过,见下方失败备注。

状态:pending · 最近测试 2026-08-26
备注:验证: runtime-fail 浏览全部待验证失败 →
安全:尚未扫描——我们的每日静态扫描将很快覆盖它。

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