内容

<div align="center"> # Cross-Border E-Commerce AI Knowledge Base ### Read it as a book. Install it as agent capability. Run it as operations. **Every figure sourced, dated or marked as an estimate · Every prompt ends with a self-check · Every guide states where it stops working — all checked in CI** 🇺🇸 English · [🇨🇳 中文](README_ZH.md) · [🇯🇵 日本語](README_JA.md)  |  📖 [Read Online](https://kangise.github.io/ecommerce-ai-skills/en/)  |  📦 [Install for Agent](dist/) [![License: CC0](https://img.shields.io/badge/License-CC0_1.0-lightgrey.svg)](https://creativecommons.org/publicdomain/zero/1.0/) [![CI](https://github.com/kangise/ecommerce-ai-skills/actions/workflows/pages.yml/badge.svg)](https://github.com/kangise/ecommerce-ai-skills/actions/workflows/pages.yml) [![Stars](https://img.shields.io/github/stars/kangise/ecommerce-ai-skills?style=social)](https://github.com/kangise/ecommerce-ai-skills) [![AAAI China Chapter](https://img.shields.io/badge/AAAI_China_Chapter-Initiative-blue)](https://github.com/kangise/ecommerce-ai-skills) </div> <img src="assets/screenshots/briefing-en-dark.png" alt="Commerce Agent OS daily briefing: verified evidence trends with a sales chart, two pending approvals waiting for a human, and three agents that have finished their review" width="100%"> Commerce Agent OS on a seeded demo tenant — verified evidence, pending human approvals, and the agents that produced them, on one screen. **In Claude Code** — installs the 9 skills; only their names and descriptions load until one is used: ``` /plugin marketplace add kangise/ecommerce-ai-skills /plugin install ecommerce-ai-skills@ecommerce-ai-skills ``` **Run the operations UI locally:** ```bash pip install "ecommerce-ai-skills[mcp] @ git+https://github.com/kangise/ecommerce-ai-skills" opc-ecommerce demo-seed --db ./demo.sqlite && opc-ecommerce demo --db ./demo.sqlite --port 8788 # then open http://127.0.0.1:8788/app ``` <img src="assets/hero-en.svg" alt="One source, two consumption paths: 69 chapters through CI gates — one path builds a trilingual site for readers, the other an installable agent package" width="100%"> ## What This Is An AI operations knowledge base for cross-border e-commerce. **One source, three uses:** | Use | What it is | Entry | |---|---|---| | **Read it** | 69 chapters, sourcing to growth, complete in zh/en/ja | [Online site](https://kangise.github.io/ecommerce-ai-skills/en/) | | **Install it for your agent** | Knowledge + domain model + guarded capabilities: a Claude Code plugin, or one MCP config line for Claude Desktop / Cursor | [`dist/`](dist/) | | **Run it** | Commerce Agent OS — an operations runtime on real store data, with multi-agent review and human approval | `opc-ecommerce demo` | All three are held to the **same CI gates**. Gates fail, nothing ships. ## Run It — Commerce Agent OS The first two uses hand knowledge to an AI. The third **puts it to work** — the same ontology and skills inside a runtime with approval, audit, and failure recovery. ```bash pip install "ecommerce-ai-skills[mcp] @ git+https://github.com/kangise/ecommerce-ai-skills" opc-ecommerce demo-seed --db ./demo.sqlite # isolated demo tenant opc-ecommerce demo --db ./demo.sqlite --port 8788 # open http://127.0.0.1:8788/app ``` The demo tenant is marked `DEMO DATA` throughout and is physically isolated from real data. **What it does today** | Capability | Detail | |---|---| | Real data in | Amazon SP-API / Amazon Ads / Shopify connectors; Business Report, Ads, FBA, returns, and listing CSV/XLSX import once as durable Evidence | | Multi-agent review | Weekly Ops Council: an evidence agent and one specialist per marketplace review in parallel, then a cross-platform controller and store manager set priorities | | Human approval gate | Writes go through proposal → approval → execution; advertising actions carry an extra capability gate and are blocked without authorization | | Durable execution | Four worker classes (job / schedule / report-sync / daily-ops) resume after interruption, with idempotency leases and recovery | | Full audit trail | Every conclusion must cite its input source; tenant isolation, API key rotation, operation audit log | | Operations UI | `/app` with seven views — briefing, agents, evidence, approvals, connections, automation, audit; persisted zh/en/ja preference, light and dark themes | <img src="assets/screenshots/approvals-en-dark.png" alt="Approvals view: an action proposal form with operation, risk, rollback plan and expiry, and an executed human-review record below it" width="100%"> Approvals: every external write is a proposal with a rollback plan and an expiry; another authorized user approves before an operator can execute. **Model providers**: OpenAI by default; set `EAI_AGENT_PROVIDER=anthropic` to run on Claude. Both providers share one contract — credentials stay environment-only, the endpoint is pinned to the official host, and the audit record names the provider actually used. An unrecognised value fails at startup rather than rerouting silently. **What it deliberately refuses to do** — without real credentials or complete evidence it **fails explicitly** rather than producing plausible-looking placeholder results. Same principle as the data discipline in the knowledge layer. Integration details in [`integration/runtime-api.md`](integration/runtime-api.md). ## Who You Are → Where to Start <img src="assets/paths-en.svg" alt="Four entry paths: a solo seller 3 min, finding AI knowledge for an agent 5 min, a team wanting one unified SOP same-day, learning the capability-packaging method" width="100%"> ## Why Not Just Another Prompt Collection <img src="assets/guardrail-en.svg" alt="The same question: an ordinary prompt invents plausible numbers, while this library's prompt stops and asks you for the data because of its three guardrails" width="100%"> Ask an AI "roughly how much does this category sell per month?" and it will almost always hand you a plausible-looking number — **one it does not actually know**. Sourcing, restocking, and pricing have real money behind them. The agent era makes it worse: a model acts on numbers it doesn't know to adjust prices and place orders. **The difference isn't fancier prompts — it's drawing the line where AI stops.** ## 30-Second Demo Paste this into [ChatGPT](https://chatgpt.com/) or [Claude](https://claude.ai/): ``` <role>Cross-border sourcing consultant familiar with Amazon [US/DE/JP]</role> <my_conditions> - Startup capital: ¥[X]0K - Experience level: [beginner/experienced/veteran] - Preferred categories: [write your preference, or "no preference"] - Risk appetite: [conservative/medium/aggressive] </my_conditions> <tool_data> [Optional. Paste category data exported from Helium 10 / Jungle Scout. If empty, see data_discipline below] </tool_data> <task> Recommend 5 category directions, each with: 1. Category name and brief description 2. Why this may be an opportunity now (state the basis for your judgment) 3. What data I need to verify to confirm it (name the specific metrics and the tool to pull them from) 4. Main risks and mitigations 5. Order-of-magnitude read on startup capital (can my stated budget cover it?) 6. Recommended entry strategy (differentiation direction) </task> <data_discipline> - **Do not give specific monthly sales, price, or margin figures** unless they appear in <tool_data>. You do not have live market data, and an invented number leads me to stock the wrong product - When <tool_data> is empty, item 3 matters most: tell me what to look up rather than guessing the answer for me - Tag each conclusion: [tool data] or [category-level inference] - If you lack the basis for a judgment, ask me for the data before concluding </data_discipline> <constraints> - Don't recommend already-red-ocean categories (phone cases, cables) - Prioritize categories with room for differentiation - Respect my capital and experience limits </constraints> <output_format> Recommend exactly 5 category directions, each following the fixed 6-item structure from <task> above: 1. Category name and brief description (1–2 sentences) 2. Why this may be an opportunity now (basis for the judgment) 3. Data to verify (specific metrics + the tool to pull them) 4. Main risks and mitigations 5. Order-of-magnitude read on startup capital (can my budget cover it?) 6. Recommended entry strategy (differentiation direction) </output_format> <self_check> Before delivering, confirm: (1) no number appears that I didn't provide, (2) every category states what to verify next, (3) exactly 5 recommendations </self_check> Note: - Don't recommend already-red-ocean categories (phone cases, cables) - Prioritize categories with room for differentiation - Account for my capital and experience limits <copy_discipline> - Never write a feature, material, certification, or result the product doesn't have. Any attribute I didn't state above must not appear in the copy - For anything sent to a customer (replies, emails, templates), don't make commitments I haven't authorized: refund amounts, compensation, timelines, or exceptions to platform policy must be confirmed by me before they go in - Flag any claim touching efficacy, safety, environmental, or patent language separately for manual review </copy_discipline> ``` **Notice how it doesn't fabricate numbers** — it tells you exactly which figures to look up yourself. From [A1 Product Research · 3.7 Category Opportunity Discovery](src/a-operators/a1-product-research.md). ## Three Real Usage Scenarios ### 1 · Write an Amazon Listing for a new product (no coding required) Open [A2 Listing Optimization](src/a-operators/a2-listing-optimization.md), copy the "full-listing generation" prompt, and fill in your product info. You get a listing with **platform hard constraints** baked in: title ≤200 characters with the highest-search-volume keyword in the first 80, 5 bullets each ≤200 characters with no HTML, backend Search Terms ≤250 bytes per line. > These aren't rules we made up — they're Amazon's actual limits, stored in [`ontology/constraints.yaml`](ontology/constraints.yaml), and the prompt's `<self_check>` block verifies each one. Change a constraint once, and gate `O5` makes every prompt referencing it change together. ### 2 · Turn Claude Desktop into an e-commerce consultant (5 minutes) Install the server dependencies first: ```bash python3 -m pip install "ecommerce-ai-skills[mcp] @ git+https://github.com/kangise/ecommerce-ai-skills" ``` ```json { "mcpServers": { "opc-ecommerce": { "command": "opc-ecommerce", "args": ["mcp"] } } } ``` > `opc-ecommerce` is the command the pip install created; the knowledge package ships inside it, so no clone is needed. If Claude Desktop cannot find the command, put the absolute path from `which opc-ecommerce` in `command`. Working from a clone instead: `"command": "python3", "args": ["/path/to/ecommerce-ai-skills/integration/mcp-server.py", "--dist", "/path/to/ecommerce-ai-skills/dist"]`. After install: | You ask | What it does | |---|---| | "ACOS is up to 40%, what do I do?" | Routes to `ecom-advertising` — a diagnostic path, not generic advice | | "Should I use AI for demand forecasting?" | Routes to `ecom-applicability` — **answers "not with under a year of data"**, because it has every chapter's failure boundaries installed | | "How do I reply to a complaint that the product doesn't match the photos?" | Routes to `ecom-customer-service` — reply templates with copy discipline | See [dist/integration/mcp.md](dist/integration/mcp.md). ### 3 · Cross-platform launches for your team, no rules to memorize The same "title" is a different thing on each of three platforms — look it up in [`ontology/constraints.yaml`](ontology/constraints.yaml): ```yaml amazon.listing.title.max_length: 200 characters shopify.product_page.title.max_length: 70 characters tiktok_shop.product.title.max_length: 80 characters ``` When the team asks in the group chat again, just share the link. Or use the `ecom-listing` skill to generate compliant variants for all three platforms in one go. ## Not Just a Book — Four Layers | Layer | Contents | Scale | For | |---|---|---|---| | **Knowledge Base** | 69 chapters, trilingual (zh/en/ja) | 69 chapters | Human reading · agent retrieval | | **Ontology** | E-commerce domain model | 100 entities · 322 constraints · 78 relations · 8 processes | Shared contract between agents | | **Skills + Prompts** | Guarded executable capabilities | 878 prompts · 9 installable skills | Agent direct invocation | | **Runtime** | Multi-agent operations runtime + UI | 54 API endpoints · 7 views · 3 platform connectors | Day-to-day operation of a real store | The first three layers are **knowledge**; the fourth **connects them to real data and executes**. The first three ship in `dist/`; the fourth is the `pip install` runtime package. `dist/` directory structure: ``` dist/ SKILL.md ← Agent entry point — read it to know how to route requests ontology.json ← E-commerce domain model (entities, relations, constraints) prompts.json ← Guardrailed prompts, trilingual skills/ ← 9 domain skills, each with manifest + playbook + boundaries knowledge/ ← Structured index of the 69 chapters integration/ ← MCP server setup guide ``` The MCP server exposes 8 resources (the five ontology files + knowledge index + full chapter text + glossary) and 5 tools (`route_query` / `get_constraints` / `search_knowledge` / `read_chapter` / `list_skills`). Check it yourself: ```bash python3 integration/mcp-server.py --cli ``` Point it at a running runtime (`OPC_RUNTIME_URL` + `OPC_RUNTIME_API_KEY`) and four **read-only** ops tools appear: briefing, metric observations, pending actions, imported evidence. The agent then knows not only what to do about a 40% ACOS, but what the ACOS actually is. Approval is not exposed. Writes go proposal → human approval → execution, and making `approve` a tool would hand the model the key to the gate. An agent can see what is waiting; a person still approves it. ## Why Trust This Content Not because "we're careful" — because of **43 CI gates**. Every gate must pass or the build stops: | Gate | What it checks | |---|---| | `M1` | Every hard number in the text has a source, a verification date, a hedge word, or an explicit flag | | `M2` | Every how-to chapter has a "when this doesn't work" section | | `M4` | Every external link has been probed and isn't dead | | `M7` | `verified` markers older than 18 months auto-expire with an error | | `N3` `N4` | Every prompt has a self-check block and an output format | | `O5` | Constraint values written in the text must match the ontology | | `parity` | All three language files exist and have matching structure | Run it yourself: ```bash python3 scripts/verify_all.py ``` > The full gate list and design rationale are in [`scripts/README.md`](scripts/README.md). Known open items are written down in [`CONTRIBUTING.md`](CONTRIBUTING.md), not hidden. ## Where to Start | You are | Start here | |--------|---------| | Wanting to know what AI can actually do | [AI Landscape Assessment](src/0-foundations/ai-landscape.md) — 30 minutes on maturity per step | | An operator, ready to use it today | [A1 Product Research](src/a-operators/a1-product-research.md) · [A2 Listing](src/a-operators/a2-listing-optimization.md) · [A3 Advertising](src/a-operators/a3-advertising.md) | | Already using AI, want automation | [A14 Agentified Operations](src/a-operators/a14-operations-agent.md) — decide which steps are worth it first | | Technical, building your own | [B4 Agent Workflow](src/b-developers/b4-agent-workflow.md) · [B6 MCP Integration](src/b-developers/b6-mcp-agentic-workflow.md) | | Facing compliance right now | [Tariffs & de minimis](src/a-operators/a11-financial-analysis.md) · [EU AI Act](src/a-operators/a6-compliance.md) | ## Other Things You Might Care About - **Content that doesn't rot in three months** — chapters only describe capability tiers; model ids and prices live on one [model matrix](src/resources/model-matrix.md) page with verification dates, and `M7` errors when they expire - **Built for the agent era** — not just prompts, but [how to migrate them into skill files](src/0-foundations/f2-prompt-engineering.md) and [which actions must never go to an agent](src/a-operators/a14-operations-agent.md) - **CC0** — take it, no attribution required, no need to tell me --- ## Content Index | Domain | Topics | |--------|--------| | AI Foundations | [AI Evolution](src/0-foundations/f1-ai-evolution.md) · [Prompt Engineering](src/0-foundations/f2-prompt-engineering.md) · [RAG](src/0-foundations/f3-rag-knowledge.md) · [Agents](src/0-foundations/f4-agent-automation.md) · [RPA](src/0-foundations/f5-rpa-automation.md) · [Tool Comparison](src/0-foundations/f6-ai-tools-comparison.md) · [AI Landscape](src/0-foundations/ai-landscape.md) | | Product Research | [Product Research](src/a-operators/a1-product-research.md) · [Pricing Strategy](src/a-operators/a8-pricing-strategy.md) · [IP Protection](src/a-operators/a12-ip-protection.md) | | Supply Chain | [Inventory & Supply Chain](src/a-operators/a5-inventory.md) | | Content & Conversion | [Listing Optimization](src/a-operators/a2-listing-optimization.md) · [Visual Content](src/a-operators/a7-visual-content.md) · [Brand Building](src/a-operators/a10-brand-building.md) | | Traffic & Acquisition | [Advertising](src/a-operators/a3-advertising.md) · [SEO / GEO](src/a-operators/a9-seo-geo.md) · [Growth Hacking](src/a-operators/a13-ai-growth-hack.md) | | Social Media | [Instagram / Facebook](src/e-social-media/e1-instagram-facebook-ai-guide.md) · [YouTube](src/e-social-media/e2-youtube-ai-guide.md) · [Xiaohongshu (RED)](src/e-social-media/e3-xiaohongshu-ai-guide.md) · [Pinterest](src/e-social-media/e4-pinterest-ai-guide.md) · [WhatsApp](src/e-social-media/e5-whatsapp-business-ai-guide.md) · [Reddit](src/e-social-media/e6-reddit-ai-guide.md) · [Cross-Channel](src/e-social-media/e7-social-media-cross-channel.md) | | Customer Operations | [Customer Service & After-Sales](src/a-operators/a4-customer-service.md) | | Compliance & Finance | [Compliance & Risk](src/a-operators/a6-compliance.md) · [Financial Analysis](src/a-operators/a11-financial-analysis.md) · [AI Risk Governance](src/c-managers/c4-ai-risk-governance.md) | | Marketplaces — Shelf | [Walmart](src/d-platforms/d4-walmart-ai-guide.md) · [eBay](src/d-platforms/d9-ebay-ai-guide.md) · [AliExpress](src/d-platforms/d10-aliexpress-ai-guide.md) · [Temu](src/d-platforms/d5-temu-seller-guide.md) · [Faire](src/d-platforms/d12-faire-wholesale-ai-guide.md) | | Marketplaces — DTC | [Shopify](src/d-platforms/shopify-ai-guide.md) | | Marketplaces — Short Video | [TikTok Shop](src/d-platforms/tiktok-shop-ai-guide.md) | | Marketplaces — APAC | [Southeast Asia](src/d-platforms/d6-southeast-asia-ai-guide.md) · [Japan (Rakuten)](src/d-platforms/d8-rakuten-japan-ai-guide.md) · [Korea (Coupang)](src/d-platforms/d11-coupang-korea-ai-guide.md) | | Marketplaces — EU & LatAm | [Mercado Libre](src/d-platforms/d7-mercado-libre-ai-guide.md) · [Otto / Zalando](src/d-platforms/d13-europe-marketplaces-guide.md) | | Cross-Platform Strategy | [Cross-Platform Synergy](src/d-platforms/cross-platform-strategy.md) · [Platform Comparison](src/d-platforms/platform-comparison.md) | | Building AI Systems | [Data Pipeline](src/b-developers/b1-data-pipeline.md) · [Prediction Models](src/b-developers/b2-prediction-models.md) · [RAG Knowledge Base](src/b-developers/b3-rag-knowledge-base.md) · [Agent Workflow](src/b-developers/b4-agent-workflow.md) · [Local Deployment](src/b-developers/b5-local-model-deploy.md) · [MCP](src/b-developers/b6-mcp-agentic-workflow.md) · [Review NLP](src/b-developers/b7-review-nlp-system.md) · [Dashboard](src/b-developers/b8-ecommerce-dashboard.md) · [Image Pipeline](src/b-developers/b9-ai-image-pipeline.md) | | Team & Management | [AI Assessment](src/c-managers/c1-ai-assessment.md) · [Team Building](src/c-managers/c2-team-building.md) · [ROI Evaluation](src/c-managers/c3-roi-evaluation.md) · [Competitive Intelligence](src/c-managers/c5-competitive-intelligence.md) | --- ## Six Tracks | Track | Who It's For | Coding? | What You Get | |-------|-------------|---------|--------------| | **[0 · AI Foundations](src/0-foundations/)** (7 guides) | Everyone | No | A working mental model of LLMs, prompts, RAG, agents | | **[A · Operators](src/a-operators/)** (13 guides) | Product research / ops / ads / CS | No | A reusable AI workflow for every step from sourcing to growth, Amazon-first | | **[B · Developers](src/b-developers/)** (9 guides) | Engineering / data / BI | Python | Deployable systems: pipelines, forecasts, RAG, agents, dashboards | | **[C · Managers](src/c-managers/)** (5 guides) | Team leads / founders | No | An AI adoption roadmap: assessment, training, ROI, risk governance | | **[D · Marketplaces](src/d-platforms/)** (14 guides) | Multi-platform sellers | No | Platform-specific playbooks: Shopify, TikTok Shop, Walmart, and 10 more | | **[E · Social Media](src/e-social-media/)** (7 guides) | Content & growth | No | Channel strategies that map to the buyer journey, from discovery to decision | --- ## Top 10 Prompts (Ready to Use) Hand-picked from the guides — copy into ChatGPT / Claude and get results instantly. **1. Competitor Review Pain Point Analysis** — Extract product improvement ideas from negative reviews ``` You are a senior Amazon product manager. I'll give you a set of 1-3 star competitor reviews. Analyze and output: Top 5 user pain points (by frequency), representative review quotes, improvement suggestions, and difficulty rating. Present in table format. [Paste negative reviews here] ``` [Full guide →](src/a-operators/a1-product-research.md#31-竞品-review-痛点分析) **2. Market Feasibility Quick Assessment** — 5-dimension scoring to decide if a product is worth pursuing ``` You are a cross-border e-commerce product research expert. Assess this product: Product: [product name] Target market: Amazon [US/DE/JP] Analyze across 5 dimensions (score 1-5 each): market demand, competition intensity, profit margin, supply chain difficulty, compliance risk. Give a final recommendation: Enter / Proceed with caution / Pass. ``` [Full guide →](src/a-operators/a1-product-research.md#32-市场可行性快速评估) **3. Full Listing Generation** — Title, bullet points, description, and search terms in one go ``` You are an Amazon Listing optimization expert for the [target market]. Product: [name] Selling points: [point 1/2/3] Keywords: [keyword list] Generate: Title (≤200 chars), 5 Bullet Points, Product Description (≤200 words), Backend Search Terms (5 lines). Integrate keywords naturally, highlight differentiation. ``` [Full guide →](src/a-operators/a2-listing-optimization.md#31-listing-全套生成标题--五点--描述--search-terms) **4. Multilingual Localization** — Not translation, but market adaptation ``` You are an Amazon Listing localization expert fluent in [target language]. [Paste English listing] Localize to [target language]: match local search habits, replace with local keywords, reorder selling points for local priorities, annotate all localization changes with reasons. ``` [Full guide →](src/a-operators/a2-listing-optimization.md#32-多语言本地化不是直译) **5. Competitor Listing Strategy Breakdown** — Compare and find differentiation opportunities ``` Analyze these 3 competitor Amazon Listings and compare their strategies: [Competitor A/B/C titles and bullet points] Output: Each competitor's core positioning, shared selling points, differentiation opportunities, keyword coverage comparison table, and positioning recommendations for my listing. ``` [Full guide →](src/a-operators/a2-listing-optimization.md) **6. Search Term Report Analysis** — Find ad spend waste and optimization opportunities ``` You are an Amazon PPC advertising expert. Here's my search term report (past 30 days): [Paste data] Output: High-converting keywords TOP 10, high-spend low-conversion TOP 10, low CTR analysis, negative keyword suggestions, budget reallocation plan. ``` [Full guide →](src/a-operators/a3-advertising.md#31-搜索词报告分析) **7. Ad Copy A/B Testing** — 5 headline styles for Sponsored Brands ``` Product: [description] Key selling point: [main benefit] Generate 5 Sponsored Brands Headlines (≤50 chars each): feature-driven, scenario-driven, emotion-driven, data-driven, problem-solving. Annotate expected impact and target audience for each. ``` [Full guide →](src/a-operators/a3-advertising.md#32-广告文案-ab-测试) **8. Bulk Negative Review Analysis** — Categorize issues and create action plans ``` You are an e-commerce product quality analyst. Here are all 1-3 star reviews from the past 60 days. Categorize by type (quality/functionality/shipping/usability/expectation mismatch), calculate frequency %, list 3 representative reviews per category, provide short-term + long-term solutions, and prioritize. [Paste reviews] ``` [Full guide →](src/a-operators/a4-customer-service.md) **9. Account Appeal Letter (Plan of Action)** — Professional reinstatement appeal ``` You are an Amazon account appeal expert. My account was suspended for: [Paste violation notice] Write a Plan of Action: Root Cause (acknowledge the issue), Immediate Actions (steps already taken), Preventive Measures (long-term prevention). Professional and sincere tone, specific action items in each section. ``` [Full guide →](src/a-operators/a6-compliance.md#36-amazon-政策违规应对) **10. Multi-Market Compliance Comparison** — Generate compliance checklists fast ``` I want to sell [product type] on Amazon [US/DE/JP]. Generate a compliance comparison table: required certifications per market, packaging & labeling requirements, special category requirements, estimated costs & timelines, common compliance pitfalls. Note information currency and recommend confirming with certification bodies. ``` [Full guide →](src/a-operators/a6-compliance.md#31-多市场合规对比深化版) --- ## Notebook Lab 18 Jupyter notebooks that run directly on Google Colab — zero setup required: [Product Research](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a1-product-research.ipynb) · [Multilingual Listing](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a2-multilingual-listing.ipynb) · [Advertising](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a3-advertising.ipynb) · [Negative Reviews](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a4-negative-review-analysis.ipynb) · [Inventory Reorder](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a5-inventory-reorder.ipynb) · [Compliance Checker](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a6-compliance-checker.ipynb) · [Price Tracker](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a8-price-tracker.ipynb) · [GEO Audit](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a9-geo-audit.ipynb) · [Brand Audit](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a10-brand-audit.ipynb) · [Profit Calculator](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a11-profit-calculator.ipynb) · [Patent Search](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/a12-ip-patent-search.ipynb) · [Data Pipeline](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/b1-data-pipeline.ipynb) · [Sales Forecast](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/b2-sales-forecast.ipynb) · [Review NLP](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/b7-review-analysis.ipynb) · [Dashboard](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/b8-dashboard-demo.ipynb) · [ROI Evaluation](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/c3-roi-evaluation.ipynb) · [Cross-Platform Content](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/d3-cross-platform-content.ipynb) · [Social Calendar](https://colab.research.google.com/github/kangise/ecommerce-ai-skills/blob/main/notebooks/e1-social-content-calendar.ipynb) ## Case Studies [AI Listing Optimization](src/case-studies/ai-listing-optimization.md) — 4 hours → 45 minutes per SKU [AI PPC Optimization](src/case-studies/ai-ppc-optimization.md) — ACOS 35% → 18% [Review-Driven Product Development](src/case-studies/ai-review-to-product.md) — 4.6★ vs. competitor's 4.2★ [All case studies →](src/case-studies/) --- ## Community ecommerce-ai-skills is an open-source project under the **AAAI China Chapter**, dedicated to the practical application of AI in cross-border e-commerce. - **Star** this repo to stay updated - [Submit an issue](https://github.com/kangise/ecommerce-ai-skills/issues) to report problems or suggest improvements - [Submit a PR](https://github.com/kangise/ecommerce-ai-skills/pulls) to contribute prompts, notebooks, or case studies ## Contributing We especially welcome: 1. **Prompt templates** — battle-tested prompts that work in real business scenarios (note which AI tools you tested with) 2. **Notebooks** — hands-on tutorials that run on Google Colab free tier 3. **Case studies** — how you solved an e-commerce problem with AI, and the results 4. **Tool reviews** — pros and cons of AI tools you've actually used 5. **Fixes** — broken links, outdated content See [CONTRIBUTING.md](CONTRIBUTING.md) for details. --- [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) — free to use, no attribution required · [Disclaimer](DISCLAIMER.md) · [Privacy](PRIVACY.md) · [Security](SECURITY.md) · *An AAAI China Chapter Initiative*

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