No, not directly, because the README only provides basic authentication and directory persistence, explicitly warns about default credentials, and does not document HTTPS, secret management, encryption at rest, or fine-grained permissions.

• The default Web username and password are admin/admin123, and the README requires changing them in production.
• state/ stores login-state cookies, while prompts/, logs/, and images/ remain filesystem directories; product images are normally removed after a task ends.
• The Web UI validates credentials through the login page and POST /auth/status, with login state stored locally in the browser.
• The README recommends a reverse proxy, HTTPS, and restricting port 8000, but does not describe built-in TLS, RBAC, auditing, or database encryption.

It can serve as a controlled internal tool, but the README does not establish that it meets complete security requirements for public exposure or sensitive-data environments.

It depends, because the project provides multiple accounts, proxy rotation, failure protection, and multi-channel notifications, but these mechanisms do not guarantee bypassing Goofish risk controls or make high-frequency, large-scale collection suitable.

• Account management supports importing, updating, and deleting login states; each task can bind an account or let the system select one.
• Configuration includes PROXY_POOL, rotation mode, retry limit, blacklist TTL, task failure threshold, and pause duration.
• Notifications support Telegram Bot Token and Chat ID, as well as generic Webhooks.
• The README explicitly requires compliance with the user agreement and robots.txt and warns against excessive requests; high concurrency or frequent account/proxy switching can cause login invalidation or account restrictions.

Thus, it can match several low-frequency monitoring tasks, but it is unsuitable for bulk collection or bypassing platform restrictions.

Yes, because the project can generate analysis criteria from natural language and send both product text and images to an image-capable model; however, the output should be treated as screening rather than authentication.

• The AI mode accepts detailed requirements, and a background job generates analysis criteria before creating the task.
• The workflow fetches product and seller details, downloads images, calls the AI, and stores results, matching unstructured judgments about condition and accessories.
• Keyword, price, publication-time, and regional filters can still be combined to reduce the result set first.
• The README requires an image-capable model and notes that image quality, incomplete descriptions, and prompts can cause false positives or negatives.

It is therefore suitable for discovering and ranking candidates, but original details, seller information, and authenticity still require verification before a transaction.

Yes, because the documented deployment model, API requirements, and scheduling features match your constraints, although it is primarily intended for single-machine or small-to-medium self-hosting.

• The Docker image includes Chromium, so no extra browser installation is required on the host.
• AI configuration explicitly requires OPENAI_API_KEY, OPENAI_BASE_URL, and an OPENAI_MODEL_NAME that supports image input.
• Tasks support Cron, keyword, price, publication-time, and province/city/district filters, helping reduce irrelevant results and AI calls.
• SQLite stores tasks, results, and price history, with ./data:/app/data mounted by default; limits under high concurrency or large historical datasets are not documented.

It can therefore provide a complete scheduled monitoring pipeline, but the README does not describe distributed scheduling or high availability for larger deployments.

Yes, provided your constraint is a single machine and small-to-medium scale, because the project has migrated tasks, results, and price history to SQLite and the results page queries the database directly.

• The default database is data/app.sqlite3, and Docker mounts ./data to /app/data.
• On startup, the application creates the database and tables and attempts to import legacy data from config.json, jsonl/, and price_history/.
• The README states that result viewing and export now query SQLite rather than scanning JSONL files.
• Images are normally stored under images/task_images_/ and cleaned after a task ends, so a complete image archive is not created automatically.

The design matches result and price-history retention, but the README provides no strong guarantee for long-term image review or high-concurrency multi-user operation.

Yes, because the README documents the backend, frontend, browser prerequisites, and test commands, while the FastAPI and Vue/TypeScript boundaries are clear enough for separate debugging.

• The development requirements include Python 3.10+, Node.js with npm, Playwright CLI, and Chromium.
• The backend can run with python -m src.app or Uvicorn; the frontend runs npm run dev in web-ui, with Vite proxying /api, /auth, and /ws to port 8000.
• start.sh checks browser prerequisites, installs dependencies, builds the frontend, and starts the backend for end-to-end verification.
• Validation includes both Python pytest and the frontend npm run build.

However, scraping changes still depend on Goofish page structure and login state. The README does not describe end-to-end fixtures, mock pages, or a stable scraping abstraction.