dsh-shop-assistant 中文 | English For shop owners, CS leads, and operators — you do not need to write code. You do not need to know English tool names. Follow the cases step by step. ## In one minute After you install this DeepSeek Harness (dsh) plugin, you can do three jobs in chat with plain language: 1. Batch bad-review replies — put an exported review spreadsheet in a folder; get many paste-ready replies that follow your return policy. 2. New listing copy from a competitor page — paste a public product URL; the assistant summarizes the page, then drafts titles, bullets, and FAQs. 3. Go / No-Go before listing — give cost, price, and 1–5 scores; a fixed formula computes profit and a recommendation (not a made-up guess). This is not “just another chatbot.” Versus pasting into a web AI chat, you get whole-table handling, stable policy wording, reproducible math, and less copy-paste. --- ## Install 1. Run DeepSeek Harness (e.g. npx @deepseek-ai/dsh web). 2. Install this plugin: sh dsh plugin --profile web add dsh-shop-assistant # or dsh plugin --profile web add github:pengzhou267-ai/dsh-shop-assistant 3. Restart the Web UI or open a new session. 4. Pick a workspace folder (next section), then chat. --- ## Before you start: where do files go? ### What is the “workspace”? It is the folder you select when you start dsh Web chat. The assistant reliably reads tables and docs inside that folder only. Suggested layout: text my-shop-files/ ├── reviews.csv ← your exported reviews ├── after-sales-policy.md ← your return rules └── (optional) products.csv ### Try without your own data first? Copy samples from this package into the workspace: | File | Use | |------|-----| | examples/reviews.csv | Fake reviews for case 1 | | examples/products.csv | Fake products | | examples/score-inputs.csv | Numbers for scoring | | kb/sample/售后政策.md | Sample return policy (edit before real use) | Header formats: examples/README.zh.md (Chinese; table headers are the same). --- ## Case 1: Batch bad-review replies (daily) ### How people usually do it Copy reviews one by one from the seller console → paste into ChatGPT / DeepSeek web → re-explain return rules every time → paste replies back. Long threads blow up; wording drifts. ### Prepare 1. Export reviews from Taobao / Pinduoduo / etc. Save as CSV UTF-8 if needed. 2. Prefer columns like: order id, rating, review text, date, SKU (see examples/reviews.csv). 3. Put the file in the workspace, e.g. reviews.csv. 4. Put return rules in after-sales-policy.md (start from kb/sample/售后政策.md). ### Steps 1. Open dsh Web; set workspace to that folder. 2. Confirm you can see reviews.csv and the policy file. 3. Paste and send: text The workspace has reviews.csv and after-sales-policy.md (or 售后政策.md). Please use the “read review spreadsheet” feature to open reviews.csv (use the Taobao-style column mapping if headers look like a Taobao export). Do not ask me to paste the table into chat. Then: 1) Group bad reviews by reason (shipping delay, color mismatch, damage, size, …); 2) Write paste-ready replies for each group; 3) Strictly follow the policy file — no promises that are not written there. (You may see tools like shop_csv_preview in the UI — you do not type those names yourself.) ### What you get Grouped, copy-paste replies keyed by reason / order id, aligned with your policy. ### Compare | | Web AI chat | This plugin | |--|--|--| | Input | Paste into the dialog | Whole CSV in the workspace | | Many rows | Context overflow | Whole-table pass | | Policy | Re-typed every turn | Fixed policy file | --- ## Case 2: New listing copy (weekly / campaigns) ### How people usually do it Open competitor tabs → hand-copy titles → paste into an AI for polish. Slow; prices get wrong or invented. ### Prepare Copy a public product URL from the browser address bar (buyer-visible page, not a login-only seller console). ### Steps Send something like: text First, fetch information from this public product page (title, description summary, visible price clues). Do not ask me to log into a seller console, and do not invent stock or promotions. URL: https://paste-a-real-public-product-url-here Then follow the “new listing copy” flow and output: 1) 5 title options (with rough length); 2) five bullet points; 3) a detail-page outline; 4) 5–8 FAQs. Our channel is Taobao. Core selling points: … In plain words: the assistant summarizes the public page (shop_page_snapshot), then follows the built-in listing playbook (shop-listing). You only paste Chinese/English instructions and the link. ### Compare | | Web AI chat | This plugin | |--|--|--| | Competitor info | You copy by hand | Paste public URL | | Prices | Easy to invent | Prefer page price clues | --- ## Case 3: Pre-list profit check (weekly–monthly) ### How people usually do it Ask “cost 35, sell at 99 — how much do I make?” Numbers change every time. ### Prepare | Field | Meaning | Example | |------|---------|---------| | cost | Unit cost | 35 | | sell price | Your price | 99 | | competitor price (optional) | Peers | 109 | | demand / competition / ops / risk / timing | Scores 1–5 | see prompt | See also examples/score-inputs.csv. ### Steps text Please use the “profit scoring / product score” feature (fixed formula, no verbal guesses) and explain in plain language: unit profit, margin rate, total score, and whether to strongly recommend / caution / not recommend. Cost 35, sell price 99, competitor 109; demand 4, competition 3, ops difficulty 2, risk 2, timing 4. You are asking the assistant to run the plugin formula (shop_product_score). You do not memorize the English name. Same inputs → same outputs. ### Compare | | Web AI chat | This plugin | |--|--|--| | Math | Improvised | Fixed formula | | Repeat asks | Numbers may drift | Stable | --- ## Appendix | Shop-owner wording | What to say in chat | Internal name (optional) | |--------------------|---------------------|---------------------------| | Read CSV | “Use read-spreadsheet on xxx.csv” | shop_csv_preview | | Fetch public page | “Fetch this public product page first” | shop_page_snapshot | | Formula score | “Use profit scoring” | shop_product_score | ### Your own policy file Copy kb/sample/售后政策.md, edit it, mention the path in the prompt. Advanced: set kbRelativeDir in the bundle config. ### Contributing / license See CONTRIBUTING.md and docs/EXTENDING.md. MIT.