dsh-habit
未验证 Max-Null
功能简介
Self-learning habit engine for the DeepSeek Harness - correction signals, threshold judgment, two-level human gate
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。
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
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。