dsh-essential-tools

dsh plugin --profile web add dsh-essential-tools

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<img src="docs/banner.svg" alt="DSHEssentialTools" width="100%">

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<b>DET · dsh-essential-tools</b> — the plugin that turns DeepSeek Harness into a usable engineering cockpit.<br>

<b>Let the AI drive your own logged-in browser</b> · <b>Branch, edit and retry any conversation</b> · <b>See a picture precisely, not approximately</b> · <b>Pay only for the tools this turn actually uses</b>

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<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green?style=flat-square" alt="MIT"></a>

<img src="https://img.shields.io/badge/DeepSeek%20Harness-0.2.1--rc.3%2B-blue?style=flat-square" alt="DSH">

<img src="https://img.shields.io/badge/type-permanent%20plugin-8c9eff?style=flat-square" alt="type">

<img src="https://img.shields.io/badge/version-2.14.1-8c9eff?style=flat-square" alt="version">

<img src="https://img.shields.io/badge/tools-44%20model%20tools%20%C2%B7%205%20at%20boot-7c4dff?style=flat-square" alt="tools">

<img src="https://img.shields.io/badge/platform-Windows%20%7C%20Edge%20%2F%20Chrome-F7DF1E?style=flat-square" alt="platform">

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<a href="#-quick-start-60-seconds"><b>Quick start</b></a> ·

<a href="#-alv--seeing-a-picture-precisely"><b>ALV</b></a> ·

<a href="#-adu--paying-only-for-what-you-use"><b>ADU</b></a> ·

<a href="#-feature-map"><b>Feature map</b></a> ·

<a href="docs/GUIDE.md"><b>用户指导手册</b></a> ·

<a href="CHANGELOG.md"><b>Changelog</b></a> ·

<a href="docs/SECURITY.md"><b>Security</b></a>

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中文一句话：DET 是 DSH 的永久插件 —— 让 AI 开你自己已登录的浏览器、任意消息开分支改问、跑/快照/回退真工程、管所有插件与每一笔 token；2.10 起再加两件事：ALV 给图片造坐标系（栅格·辅助线·边线坐标·本机 OCR），ADU 让工具按需装载（启动只装 5 个元工具）。

😤 The problem

If you hack on real code with an AI agent in the loop, you already know these five:

Pain · What everyone does today · What DET does

🧠 One wrong turn kills the thread — you want to re-ask from message #12, not start over · Copy-paste into a fresh chat and lose all context · VTD: edit / retry any message → a real hidden branch, switch back with <N>, and the workspace code rolls back with you

🌐 The AI can't see or click what you see — your logged-in dashboards, internal pages, file:// pages · Screenshot, paste, hand-hold, retry · DET Browser Extension: the model drives your already-logged-in Edge/Chrome through a localhost bridge you gate yourself (off / read / write / on)

👁 "I think the number is 41.00 but the crop is blurry" — vision models guess coordinates · Re-ask, re-screenshot, hope · ALV: grids, rulers, guide lines, edge positions and on-device OCR with per-word boxes — then let the model look

🛠 Build-run-look-check is a manual loop · Alt-Tab to the IDE and terminal 40 times an hour · Right toolbar: ▶ run · 🗎 file tree + preview/edit · 🕘 program snapshots & rollback

💸 You have no idea what a session costs — or which tools cost you context · Find out at the end of the month · Live balance + per-model price + per-turn cost, plus ADU: only the tools this turn needs are in the schema budget

DET (the plugin dsh-essential-tools) is the permanent DeepSeek Harness plugin that fixes all five — and adds a full plugin manager, a conversation-memory layer, and a security audit on top. It lives in your web profile, survives restarts, and shows up in Settings → Plugin inventory.

🎁 Zero config: without project paths, every conversation / token / plugin / image feature works out of the box. Add paths only when you want ▶ run.

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🆕 What changed in 2.10 / 2.11

Three answers to three long-standing problems:

Problem · Answer

A vision model looks at a picture, not a coordinate system · ALV — 8 tools that build the scaffolding: grid, ruler, guide lines, edge positions, on-device OCR, tiling, annotation, enhancement

Every turn paid for 30+ tool schemas while using one or two · ADU — boot with 5 meta tools, mount modules on demand (adu_list → adu_describe → adu_load)

Failures only produced one word: browser-timeout · Per-command budgets + structured failures + a failure ledger (_diagPush → detHealth panel)

A real ALV round trip, on a table screenshot:

alv_info   → 900×600, RGB, mean 241, dark 3.9%
alv_edges  → horizontalPositions: y=81(4) y=141(4) y=201(4) y=261(4) y=321(4) y=381(4)
alv_grid   → cell=60, guides drawn at y=470/540, x=430 → returns gridded copy + toSource mapping
alv_ocr    → 型号数量 / ALV-100 12 18.50 222.00 / … / TOTAL 471.00   (with per-line boxes)
alv_annotate→ 2 boxes + 1 cross drawn at exact coordinates, saved and re-read

🔍 ALV — seeing a picture precisely

ALV is geometry, not a better model: no ML, no network, no API key. You (or the model) state coordinates and thresholds; ALV draws and measures exactly that. Everything is offline; OCR uses the OS engine (Windows.Media.Ocr) so results are reproducible and carry word boxes.

Tool · What it gives you

alv_info · Size / mode / DPI / brightness / dark-pixel ratio / 16-bin histogram (optional noise). Decide whether to crop, upscale or binarize before wasting a look.

alv_grid · The coordinate system itself: grid + ruler labels in the margin + guide lines at any position (guides:{horizontal:[470],vertical:[430]}). Returns a new image and the toSource mapping.

alv_crop · Exact box crop, optional upscale (2–4×) for small print; omit box to auto-trim uniform borders. Returns the mapping back to the original.

alv_edges · Hardcoded edge-position extraction: Canny + probabilistic Hough → line segments and merged row/column positions (horizontalPositions:[{y,votes}]), plus an annotated copy labelled y= / x=. This is how you answer "which row line is where" without eyeballing.

alv_ocr · Hardcoded OCR: offline, word boxes. mode=lines merges

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