dsh-prompt-injector

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Generic per-turn context injection for the DeepSeek Harness (dsh) web profile. Manage a list of prompts in the settings page; every conversation round injects each enabled prompt into the model context as a compact collapsed "Context injection" notice line (the leading label and the plugin name are added by the UI; the row title is your prompt title) — the exact mechanism used by memory plugins, made reusable for any rule you want the model to actually follow.

[context injection | dsh-prompt-injector] 图谱·Wiki 提醒  ← one notice line per enabled prompt,
[context injection | dsh-prompt-injector] 回复结构 1-2-3   ← every round, right before the model
[context injection | dsh-prompt-injector] 安全红线清单     ← plans its answer

[!IMPORTANT] Design intent. The plugin reminds, it never executes: it calls no tools and checks nothing itself. A good prompt writes down when to check, when to skip, and what to run — the model still decides per round. See Writing good prompts.

It ships as a standard dsh bundle plugin: dsh plugin add to install, dsh plugin remove to uninstall. It changes no dsh source code.

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Table of Contents

• Why
• Features
• How it works
• Requirements
• Installation
• Configuration
• Writing good prompts
• Development & testing
• License

Why

Rules written only in the persona/system layer are unreliable: models reliably lose track of them in long sessions, even when the rules are explicit ("refresh the code graph before coding", "check the wiki first for factual questions" — measured to silently not happen session after session).

Per-round injection, on the other hand, reaches the model's context every turn and is rendered as a visible notice line in the UI (collapsed summary readable at a glance). This is the mechanism dsh-mem0-plugins uses for its memory-recall reminder — this plugin generalizes it: instead of writing a new plugin for every discipline (memory, code graphs, wiki lookup, safety redlines, reply style…), add a prompt in the settings page.

Features

Capability	Behavior
Per-round injection	On agent/pre-step, when the round carries fresh user input, every enabled prompt is appended to this round's context as a form:'notice' plugin-source message — one "Context injection" line each.
Trigger modes	Each prompt row picks everyTurn (default) or postCompaction: injected exactly once on the next substantive round after a context compaction (counts committed compaction/summary events per session; no repeat within the same compaction; zero LLM cost).
Master switch	enabled turns injection on/off globally (settings page or config).
Trivial-round filtering	skipTrivial (default on) skips brief acknowledgements/greetings/continuations (好的/嗯/收到/继续/ok/thanks…), so reminders don't nag; substantive input (e.g. "继续帮我看看那个报错") still gets them.
Prompt management UI	Add / delete / edit prompts, per-row enable switch, per-row title + body, all in the settings page.
Remind only, never execute	The plugin calls no tools and runs no checks; judgment stays with the model.
Zero intrusion	No dsh source changes; only two small runtime deps (@deepseek-ai/dsh-settings, @deepseek-ai/schemastery); standard bundle install/uninstall.

How it works

• Injection point: agent/pre-step → the plugin appends to decision.messages (same hook chain as dsh-mem0-plugins). One message per enabled prompt, role: user, source: { kind: 'plugin', form: 'notice', summary: '<title>' } — the UI renders it as a collapsed notice line whose summary is visible without expanding.
• Hooking: listens to agent/created for new agents and backfills pre-existing agents at apply time (a WeakSet guards against double registration).
• Trivial detection: acknowledgement/greeting/continuation lexicon (ported from dsh-mem0-plugins, itself from hermes is_trivial_prompt, MIT) + slash-command pattern; input carrying real content is never miscategorized.
• Compaction generations: a plugin-level session/event listener counts committed compaction/summary events per session; postCompaction rows fire once per generation (applied-generation tracked per prompt, cleared on session/disposed). Old configs without trigger behave as everyTurn — zero migration.
• Persistence: prompts live in the dsh settings store (user layer), editable in the settings page, saving immediately without a restart.

Requirements

• DeepSeek Harness (dsh) web profile, Node ≥ 22.
• The prompts you write may reference your own tooling (code graph services, wiki search commands, …) — those are just text; nothing is executed by the plugin.

Installation

dsh plugin --profile web add /path/to/dsh-prompt-injector
# restart dsh

Uninstall: dsh plugin --profile web remove dsh-prompt-injector

After restart, open Settings → Plugins → "Context injection" to manage prompts. The prompt list starts empty — nothing is injected until you add your own.

Configuration

Key	Type	Default	Meaning
enabled	boolean	true	Master switch.
skipTrivial	boolean	true	Skip trivial rounds (ack/greeting/continuation).
prompts	array	[] (empty)	Prompt list: [{ id, title, text, enabled, trigger }]; trigger is everyTurn (default) or postCompaction.

The settings page edits these; the user layer wins over the composition defaults. To override via the profile patch:

# ~/.dsh/profiles/web/cordis.patch.yml
- insert:
    - id: prompt-injector
      name: dsh-prompt-injector
      config:
        enabled: true
        skipTrivial: true
        prompts:
          - id: my-rule
            title: My rule
            text: |
              [my-rule] Before answering, judge: does this round need it?
              ...
            enabled: true

Prompt examples (not built in)

The plugin ships with an empty list — the mechanism is generic, the content is yours. Two examples from real deployments you can copy and adapt. "图谱·Wiki 提醒" (everyTurn; code graph + wiki reminder) demonstrates the intended "judge first" style:

[graph-wiki requirement] 本轮开始，先判断是否需要查图谱/Wiki，再决定是否执行——不是每轮都要查：
① 编码任务（本轮要改代码）→ 消费图谱：目标仓库先 `code-review-graph update` 刷新（无图谱则自动建库），然后 `crg search/impact/stats` 查询（多仓库自动发现：--repo <别名|路径> > 当前目录 .git 根 > 兜底 mem0_falkordb；`crg xsearch` 全仓搜索），深层结构用 graphify 查询（graphify-mcp 5566）。只改文档/纯叙述/无代码改动则跳过刷新。
② 技术事实类问题（版本/行为/配置/术语/流程步骤）→ 先 `gmcp search '{"query":"..."}'` 查 wiki（score≥0.45 取前 3 页，读页后再答，标注 [[wikilink]] 来源）。
③ 纯闲聊、纯算术、无事实成分的简单操作 → 跳过，直接回答。
若不确定属于哪类：宁可查一次（gmcp 或 crg 成本低），不要凭记忆给过时答案。

The commands referenced (crg, graphify, gmcp) belong to that deployment's own graph/wiki tooling — replace them with whatever your environment actually has.

"压缩后提醒" (postCompaction) fires once after each compaction, when early transcript is unrecoverable:

[上下文已压缩] 本轮之前发生过 compaction，早期原文不可恢复。
涉及历史事实、报错原文、文件路径、此前决定时，先 mem0_search 或重读相关文件核实，勿凭印象引用。

Writing good prompts

The plugin's value comes from how you phrase each prompt. Advice that worked in practice:

1. State the judgment condition, not just the action. "Before coding, refresh the graph" alone will over-trigger; add the skip branch: "…unless this round only touches docs/narration".
2. Explicitly allow skipping. Rounds without factual content or code work should be told "just answer directly" — otherwise a mandatory-sounding prompt burns tokens and attention every round.
3. Give a fallback for uncertainty. "If unsure which category, one lookup is cheap — don't answer from stale memory."
4. Keep it short. One screen or less per prompt; a wall of text gets skimmed, not followed.
5. Consider frequency. An everyTurn prompt is injected every round. If a rule is only relevant to a specific task type, that's fine (the model filters by the judgment text) — but don't stack many long prompts. Rules that only matter after a context compaction should use postCompaction.

Development & testing

node --test test/smoke.mjs        # host logic: defaults, normalization, injection decision, generations
node --test test/entry-smoke.mjs  # host entry load + pre-step/compaction end-to-end
node test/client-smoke.mjs        # client bundle: slot contract, locale, card rendering, trigger select

Layout:

• src/index.js — plugin entry: installSettingsSection + agent/pre-step injection + agent hooks/backfill.
• src/logic.js — zero-dependency pure logic (defaults, normPrompts, isTrivialPrompt, makePromptMessage, shouldInject, selectPrompts generation selector).
• lib/client.js — settings card (master switch + trivial toggle + prompt list editor), PInj_ prefixed styles.

License

MIT