Xianyu Auto Ops

Bilingual Xianyu (闲鱼) listing and lightweight operations workflow for second-hand goods, side-hustle products, and marketplace distribution. Use when the use...

Security Scan

The skill's files, instructions, and small CSV-normalization script are consistent with a Xianyu listing / batch-ops helper and do not request extra credentials or install remote code.

Purpose & Capability

Name and description match the included SKILL.md, playbook, templates, and the small scripts/batch_csv_to_brief.py helper; nothing requested (env, binaries, creds) appears unrelated to marketplace listing generation.

Instruction Scope

Runtime instructions stay within listing generation and batch CSV normalization. The SKILL.md only references reading user-provided CSVs and internal reference files; it does not instruct reading arbitrary system secrets or contacting external endpoints.

Install Mechanism

No install spec is present (instruction-only) and the included Python script is small, local, and self-contained. No downloads or archive extraction are requested.

Credentials

The skill requires no environment variables, credentials, or config paths. The requested inputs (CSV rows) are proportionate to the stated batch-listing purpose.

Persistence & Privilege

always:false and default invocation settings are used. The skill does not request persistent agent-level privileges or modifications to other skills or system config.

Assessment

This skill appears coherent and low-risk: it generates bilingual listing text and includes a harmless local CSV-normalizer. Before using, avoid passing files that contain sensitive data (personal IDs, payment credentials, private keys) in CSVs you submit; review any generated reply scripts and image prompts for content policy or privacy concerns. If you plan to run the included Python script locally, inspect it (it's short and self-contained) and ensure Python execution is acceptable in your environment.

Use this skill to turn rough product information into a repeatable Xianyu / Idle Fish operating package.

Default output language: bilingual Chinese + English.
Default business goal: faster listing, clearer positioning, better inquiry conversion.

Core workflow

Follow this sequence unless the user asks for only one part.

1. Clarify the offer

   • Identify product type, condition, target buyer, price band, and delivery method.
   • If key details are missing, make lightweight assumptions and label them clearly.

2. Choose the operating mode

   • Single listing mode: one product, one polished package.
   • Batch listing mode: multiple SKUs, concise per-item outputs.
   • Reply mode: buyer inquiry handling, objection answers, negotiation copy.
   • Optimization mode: improve an existing listing.
   • Poster mode: generate ad-style image prompts for listing cover or off-platform promo.

3. Pick a category template Use the closest category framing:

   • Digital / 数码: specs, condition, accessories, battery, warranty, authenticity cues.
   • Home / 家居: size, cleanliness, usage marks, pickup logistics, scene fit.
   • Fashion / 服饰: size, fabric, season, flaws, try-on expectation.
   • Virtual / 虚拟产品或服务: delivery scope, usage rights, support boundaries, trust language.
   • Training / Side-hustle / AI services: results, delivery format, onboarding, trust signals, support boundaries. Read references/ai-services-template.md when the product is training, installation, deployment, consulting, or AI service resale.

4. Produce the listing package Return, in this order when relevant:

   • Chinese title ×3
   • English title ×1
   • Core selling points / 卖点摘要
   • Chinese listing description
   • English summary description
   • Suggested tags / keywords
   • Suggested price anchor and negotiation room
   • Cover image prompt(s)
   • Buyer reply scripts
   • Posting checklist

5. Keep it platform-native

   • Prefer short, direct, benefit-led copy.
   • Avoid exaggerated claims that sound fake or risky.
   • Make the listing feel like a real seller wrote it, not a brand brochure.

Batch listing mode

When the user gives multiple items, default to a compact table-like structure using bullets, not markdown tables on chat surfaces.

For each SKU, include:

• SKU / item name
• Chinese title ×2
• One short Chinese description
• One short English summary
• Suggested listed price / expected close price
• One key reply note
• One image direction

If the user provides a CSV or spreadsheet, use scripts/batch_csv_to_brief.py <file> to normalize the rows first, then use the JSON output as batch input.

Expected columns can include: sku, name, category, brand, condition, price_target, flaws, accessories, city, delivery, notes

Chinese headers like 商品名 / 类目 / 成色 / 价格 / 瑕疵 / 配件 / 城市 / 发货 / 备注 are also supported.

Do not ask the user to perfect the data before starting. Fill gaps with assumptions and mark them.

Output rules

Titles

Write titles that are:

• easy to scan
• keyword-rich without obvious stuffing
• benefit-forward
• believable for Xianyu

Prefer this rough formula:

[brand/category] + [core item] + [condition / key value] + [buyer use case / bonus point]

Descriptions

For each Chinese description, keep this structure:

1. What it is
2. Why selling / product background
3. Condition / usage / delivery details
4. Why worth buying
5. Call to action

For English, provide a shorter mirror summary rather than a full literal translation unless the user asks for full bilingual parity.

Pricing

When suggesting price, provide three layers when possible:

• Listed price / 挂价
• Expected成交价 / expected closing price
• 最低可谈区间 / lowest negotiable band

Base suggestions on:

• condition
• urgency to sell
• scarcity / uniqueness
• bundled extras
• local delivery convenience

Buyer reply scripts

When generating reply scripts, include short ready-to-send messages for:

• “还在吗？” / “Is this still available?”
• “最低多少？” / “What’s your lowest price?”
• “有瑕疵吗？” / “Any flaws?”
• “包邮吗？” / “Is shipping included?”
• “怎么交易更稳妥？” / “How do we trade safely?”
• closing push / 成交推进

Keep replies short and human.

Poster prompts

When the user asks for ad visuals, output two prompt layers:

• Platform-safe cover prompt: more realistic, cleaner, product-led.
• Promotional poster prompt: more visual tension, more marketing feel, more negative space for title text.

Prefer 16:9 for article covers and 1:1 / 4:5 for feed-like visuals unless the user says otherwise.

Recommended response format

Use this template unless the user asks for a different one.

1. 商品定位 / Positioning

• Chinese:
• English:

2. 标题建议 / Title Options

• CN-1:
• CN-2:
• CN-3:
• EN-1:

3. 卖点提炼 / Selling Points

4. 商品文案 / Listing Copy

中文版本

English version

5. 配图建议 / Image Plan

• Cover idea:
• Detail shots:
• Platform-safe AI image prompt:
• Promotional poster prompt:

6. 价格建议 / Pricing Strategy

• Listed price:
• Expected close price:
• Lowest negotiable range:

7. 私聊回复模板 / Chat Reply Scripts

• 在的 / Available:
• 最低价 / Lowest price:
• 瑕疵说明 / Flaw disclosure:
• 安全交易 / Safe trade:
• 成交推进 / Closing push:

8. 发布清单 / Posting Checklist

• photos ready
• condition disclosed
• delivery method stated
• keywords included
• price strategy set
• reply script ready

Bilingual handling

When the user asks for bilingual output, do not translate mechanically.

• Chinese should sound like a native Xianyu seller.
• English should sound like a concise marketplace assistant summary.
• If the target buyers are Chinese users only, keep English shorter.

Safety and quality guardrails

• Do not fabricate certifications, warranties, invoices, or brand authorization.
• Do not hide material defects if the user explicitly mentions them.
• Do not promise impossible delivery times.
• Flag risky categories, compliance-sensitive products, or obvious fraud patterns.
• For virtual goods or services, clearly describe what is and is not delivered.

References

Read references/playbook.md when the user needs stronger title formulas, batch handling, category-specific patterns, buyer reply banks, or poster prompt templates.