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dsh-habit

Unverified Max-Null

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2026-08-21Last push
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What it does

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

Our take
Unverified — not yet verified

Self-learning habit engine for the DeepSeek Harness - correction signals, threshold judgment, two-level human gate Not yet verified — install and test it yourself.

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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

Install

🧩 Let your agent install it (recommended)

Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:

dsh plugin add dshbase-catalog

Then say "install dsh-habit for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.

This plugin is GitHub source (not published to npm) — install it straight from the repo:

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

Test report

Not yet L3-verified — see failure note below if we already ran it.

Status: pending · last test 2026-08-26
Note: 验证: runtime-fail Browse all pending failures →
Security: not yet scanned — our daily static scan will cover it shortly.

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