{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-usagi-org--ai-goofish-monitor","slug":"usagi-org--ai-goofish-monitor","name":"ai-goofish-monitor","type":"workflow","url":"https://github.com/Usagi-org/ai-goofish-monitor","page_url":"https://unfragile.ai/usagi-org--ai-goofish-monitor","categories":["automation","observability"],"tags":["ai","ai-assistant","ai-tools","automation","gemini","goofish","open-source","openai","playwright","tool","xian-yu","xianyu","xianyu-bot"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-usagi-org--ai-goofish-monitor__cap_0","uri":"capability://automation.workflow.concurrent.multi.task.marketplace.monitoring.with.playwright.automation","name":"concurrent multi-task marketplace monitoring with playwright automation","description":"Executes parallel web scraping tasks against Xianyu marketplace using Playwright browser automation (spider_v2.py), with concurrent task execution managed through Python asyncio. Each task maintains independent browser sessions, cookie/session state, and can be scheduled via cron expressions or triggered in real-time. The system handles login automation, dynamic content loading, and anti-bot detection through configurable delays and user-agent rotation.","intents":["Monitor multiple product categories simultaneously without blocking on individual task completion","Schedule recurring marketplace checks at specific times while maintaining independent task state","Automate login and session management across multiple concurrent Xianyu accounts","Handle dynamic JavaScript-rendered content and lazy-loaded product listings"],"best_for":["Teams building marketplace monitoring agents for Chinese e-commerce platforms","Developers needing concurrent web scraping with stateful browser sessions","Non-technical users wanting to monitor Xianyu without manual checking"],"limitations":["Playwright browser instances consume 100-200MB RAM each; concurrent task count limited by available memory","Anti-bot detection may require periodic cookie refresh and session rotation","Xianyu API changes require parser updates in src/parsers.py; no built-in schema versioning","Task scheduling relies on system cron; no distributed task queue for multi-machine deployments"],"requires":["Python 3.10+","Playwright browsers (Chromium/Edge) installed via playwright install","2GB+ RAM for concurrent task execution","Valid Xianyu account credentials for login automation"],"input_types":["task configuration (JSON with keywords, price filters, cron schedules)","login credentials (username/password or session cookies)","search parameters (product categories, price ranges, condition filters)"],"output_types":["structured product data (title, price, images, seller info, condition)","task execution logs (timestamps, success/failure status, error traces)","raw HTML/DOM snapshots for debugging parser failures"],"categories":["automation-workflow","web-scraping"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_1","uri":"capability://text.generation.language.multimodal.ai.product.analysis.with.image.and.text.processing","name":"multimodal ai product analysis with image and text processing","description":"Analyzes scraped product listings using multimodal LLMs (OpenAI GPT-4V or Google Gemini) through src/ai_handler.py. Encodes product images to base64, combines them with text descriptions and task-specific prompts, and sends to AI APIs for intelligent filtering. The system manages prompt templates (base_prompt.txt + task-specific criteria files), handles API response parsing, and extracts structured recommendations (match score, reasoning, action flags).","intents":["Filter marketplace products using natural language descriptions instead of rigid keyword matching","Analyze product images for condition, authenticity, and visual appeal against user preferences","Generate AI-powered recommendations with reasoning for why a product matches user criteria","Combine multiple product attributes (price, condition, seller rating, images) into holistic match scoring"],"best_for":["Users with complex, subjective product preferences (e.g., 'vintage condition but not damaged')","Teams building intelligent marketplace agents with multimodal understanding","Developers needing flexible AI-driven filtering without hardcoded business logic"],"limitations":["API latency adds 2-5 seconds per product analysis; batch processing limited by rate limits (3-5 req/sec for OpenAI)","Image encoding to base64 adds ~100-300ms per product; no caching of encoded images across runs","Prompt engineering required for accurate filtering; no built-in prompt optimization or A/B testing framework","AI responses may be inconsistent across similar products; no confidence thresholds or validation logic","Multimodal analysis costs ~$0.01-0.03 per product with GPT-4V; no cost estimation or budget controls"],"requires":["OpenAI API key (GPT-4V) or Google Gemini API key","AsyncOpenAI client configured in src/config.py","Product images accessible as URLs or local files","Task-specific prompt files in prompts/ directory","Network connectivity for API calls"],"input_types":["product metadata (title, price, seller info, condition, URL)","product images (URLs or base64-encoded binary data)","task criteria (natural language description of desired products)","base prompts (system instructions for AI analysis)"],"output_types":["JSON-structured AI recommendations (match_score, reasoning, action_flags)","filtered product lists (matched vs rejected with explanations)","analysis logs (API calls, response times, token usage)"],"categories":["text-generation-language","image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_10","uri":"capability://automation.workflow.docker.containerization.with.multi.stage.builds.and.environment.isolation","name":"docker containerization with multi-stage builds and environment isolation","description":"Provides Docker configuration (Dockerfile, docker-compose.yml) for containerized deployment with isolated environment, dependency management, and reproducible builds. The system uses multi-stage builds to minimize image size, includes Playwright browser installation, and supports environment variable injection via .env file. Docker Compose orchestrates the service with volume mounts for config persistence and port mapping for web UI access.","intents":["Deploy the monitoring system in containerized environments (Docker, Kubernetes) without local setup","Ensure reproducible deployments across development, staging, and production","Isolate the monitoring system from host system dependencies and conflicts","Scale the system horizontally by running multiple container instances"],"best_for":["Teams deploying to cloud platforms (AWS, GCP, Azure) with Docker support","Developers wanting reproducible local development environments","Organizations requiring containerized workloads for compliance or operational reasons"],"limitations":["Docker image size is large (~1-2GB) due to Playwright browser dependencies; slow initial pull","Playwright browser requires --cap-add=SYS_ADMIN for sandboxing; may not work in restricted environments","Volume mounts for config persistence require host file system access; not suitable for ephemeral containers","No Kubernetes manifests provided; users must create custom YAML for K8s deployment","Multi-container orchestration (separate DB, cache) not included; single-container design limits scalability"],"requires":["Docker 20.10+ or Docker Desktop","Docker Compose 1.29+ (for docker-compose.yml)","At least 2GB available disk space for image storage",".env file with required environment variables","config.json for task definitions (optional; can be created via API)"],"input_types":["Dockerfile (container image definition)","docker-compose.yml (service orchestration)",".env file (environment variables)","config.json (task definitions)"],"output_types":["Docker image (built from Dockerfile)","running container (from docker-compose up)","container logs (stdout/stderr from service)","exposed ports (web UI on port 8000, configurable)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_11","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Implements resilient error handling throughout the system with exponential backoff retry logic for transient failures (network timeouts, API rate limits, temporary service unavailability). Playwright scraping includes retry logic for page load failures and element not found errors. AI API calls include retry logic for rate limit (429) and server error (5xx) responses. Failed tasks log detailed error traces for debugging and continue processing remaining tasks.","intents":["Automatically recover from transient network failures without manual intervention","Handle AI API rate limits gracefully by retrying with exponential backoff","Continue processing remaining tasks even when individual tasks fail","Provide detailed error logs for debugging and monitoring system health"],"best_for":["Production deployments requiring high availability and fault tolerance","Teams monitoring Xianyu with unreliable network connectivity","Developers building resilient marketplace monitoring agents"],"limitations":["Exponential backoff is hardcoded (no configuration); max retry attempts and backoff multiplier cannot be customized","Retry logic only handles transient errors; permanent errors (invalid API key, malformed request) fail immediately","No circuit breaker pattern; repeated failures to the same service don't trigger fast-fail","Error logs are in-memory; lost on service restart unless explicitly persisted","No alerting for repeated failures; users must manually check logs to detect systemic issues"],"requires":["Python 3.10+","Network connectivity for retries to succeed","Reasonable timeout values configured for each service (Xianyu, AI API, notification services)"],"input_types":["failed operations (Playwright page loads, AI API calls, notification deliveries)","error responses (HTTP status codes, exception messages)","retry configuration (max attempts, backoff multiplier, timeout)"],"output_types":["retry logs (attempt number, backoff delay, retry status)","error logs (exception type, message, stack trace)","final success/failure status (after all retries exhausted)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_12","uri":"capability://automation.workflow.system.health.monitoring.and.status.reporting","name":"system health monitoring and status reporting","description":"Provides health check endpoints (/api/health, /api/status/*) that report system status including API connectivity, configuration validity, last task execution time, and service uptime. The system monitors critical dependencies (OpenAI/Gemini API, Xianyu marketplace, notification services) and reports their availability. Status endpoint includes configuration summary, active task count, and system resource usage (memory, CPU).","intents":["Monitor system health and detect service degradation or failures","Verify API connectivity and configuration validity on startup","Track system resource usage and identify performance bottlenecks","Provide status information for external monitoring systems (Prometheus, Datadog, etc.)"],"best_for":["Teams running production monitoring systems requiring observability","Developers debugging deployment issues and configuration problems","Operations teams monitoring system health via external monitoring tools"],"limitations":["Health checks are synchronous; slow API responses block the health endpoint","No historical health data; status endpoint shows only current state","Resource usage metrics are basic (memory, CPU); no detailed profiling or bottleneck analysis","No alerting integration; health checks don't trigger notifications for failures","Health check frequency is fixed; no configurable check intervals or thresholds"],"requires":["FastAPI framework (included in requirements.txt)","Python 3.10+","Network connectivity to check external service availability"],"input_types":["health check requests (HTTP GET to /api/health or /api/status/*)","configuration state (for validation checks)"],"output_types":["health status JSON (service status, dependency availability, uptime)","configuration summary (API providers, notification channels, task count)","resource usage metrics (memory, CPU, disk space)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_2","uri":"capability://tool.use.integration.multi.channel.notification.delivery.with.intelligent.routing","name":"multi-channel notification delivery with intelligent routing","description":"Routes AI-generated product recommendations to users through multiple notification channels (ntfy.sh, WeChat, Bark, Telegram, custom webhooks) configured in src/config.py. Each notification includes product details, AI reasoning, and action links. The system supports channel-specific formatting, retry logic for failed deliveries, and notification deduplication to avoid spamming users with duplicate matches.","intents":["Deliver real-time product alerts to users across their preferred communication platforms","Send rich notifications with product images, prices, and AI reasoning in platform-native formats","Route different product categories to different notification channels based on user preferences","Implement notification deduplication to prevent duplicate alerts for the same product"],"best_for":["Users monitoring multiple product categories across different platforms (WeChat, Telegram, etc.)","Teams building marketplace agents with multi-channel notification requirements","Developers needing flexible notification routing without hardcoding channel logic"],"limitations":["WeChat notifications require valid WeChat bot token and user ID; no built-in WeChat authentication flow","Telegram rate limits to 30 messages/second per bot; no queue management for burst notifications","Webhook delivery is fire-and-forget; no guaranteed delivery or retry persistence across restarts","Notification deduplication uses in-memory cache; resets on service restart, may cause duplicate alerts","Rich media (images) not supported in all channels; fallback to text-only for some platforms"],"requires":["Notification service credentials (ntfy.sh topic, WeChat bot token, Telegram bot token, Bark device key)","Network connectivity to notification service endpoints","Valid webhook URLs if using custom webhook notifications","Configuration entries in .env or config.json for each enabled channel"],"input_types":["product data (title, price, images, URL, seller info)","AI analysis results (match score, reasoning, action flags)","user preferences (notification channels, format preferences, deduplication rules)"],"output_types":["formatted notifications (platform-specific text, images, action links)","delivery logs (timestamp, channel, success/failure status, retry attempts)","notification history (for deduplication and user audit trails)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_3","uri":"capability://tool.use.integration.task.lifecycle.management.via.rest.api.with.real.time.logging","name":"task lifecycle management via rest api with real-time logging","description":"Provides FastAPI-based REST endpoints (/api/tasks/*) for creating, reading, updating, and deleting monitoring tasks. Each task is persisted to config.json with metadata (keywords, price filters, cron schedule, prompt reference, notification channels). The system streams real-time execution logs via Server-Sent Events (SSE) at /api/logs/stream, allowing web UI to display live task progress. Task state includes execution history, last run timestamp, and error tracking.","intents":["Create and manage multiple monitoring tasks through a web interface without editing config files","View real-time task execution logs and progress in the web dashboard","Update task criteria (keywords, price ranges, AI prompts) without restarting the service","Track task execution history and debug failures through centralized logging"],"best_for":["Non-technical users managing multiple monitoring tasks through a web UI","Teams needing centralized task management across multiple monitoring agents","Developers building marketplace monitoring SaaS with multi-tenant task isolation"],"limitations":["Task persistence uses JSON file (config.json); no database, limiting scalability to ~100 tasks per instance","SSE logging has no message buffering; clients connecting after task start miss historical logs","No task versioning or rollback; updating a task overwrites previous configuration permanently","HTTP Basic Auth is the only built-in authentication; no role-based access control (RBAC)","Task execution is single-threaded per instance; no distributed task queue for multi-machine setups"],"requires":["FastAPI framework (included in requirements.txt)","Python 3.10+","HTTP client for API calls (curl, Python requests, JavaScript fetch)","Basic Auth credentials configured in .env (AUTH_USERNAME, AUTH_PASSWORD)"],"input_types":["task configuration (JSON with keywords, price filters, cron schedule, prompt reference)","task updates (partial or full task object)","task deletion requests (task ID)"],"output_types":["task objects (with ID, metadata, execution history)","real-time log streams (SSE format with timestamp, level, message)","task execution results (matched products, analysis results, notification status)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_4","uri":"capability://automation.workflow.scheduled.task.execution.with.cron.based.timing.and.real.time.triggering","name":"scheduled task execution with cron-based timing and real-time triggering","description":"Executes monitoring tasks on two schedules: (1) cron-based recurring execution (e.g., '0 9 * * *' for daily 9 AM checks) parsed and managed in spider_v2.py, and (2) real-time on-demand execution triggered via API or manual intervention. The system maintains a task queue, respects concurrent execution limits, and logs execution timestamps. Cron scheduling is implemented using APScheduler or similar, with task state persisted across restarts.","intents":["Run marketplace monitoring tasks at specific times (e.g., daily, hourly) without manual intervention","Trigger immediate product checks on-demand when users want fresh data","Manage concurrent task execution to avoid resource exhaustion from simultaneous scraping","Persist task schedules across service restarts without losing configuration"],"best_for":["Users wanting automated, hands-off marketplace monitoring on a schedule","Teams managing multiple monitoring tasks with different execution frequencies","Developers building always-on monitoring services with minimal operational overhead"],"limitations":["Cron scheduling is local to single instance; no distributed scheduling across multiple machines","No built-in task queue or priority system; tasks execute in FIFO order, potentially delaying high-priority tasks","Concurrent task limit is hardcoded or config-based; no dynamic scaling based on system load","Task execution history is in-memory; lost on service restart unless explicitly persisted","Timezone handling relies on system timezone; no explicit timezone configuration per task"],"requires":["APScheduler or similar cron scheduling library (included in requirements.txt)","Python 3.10+","System timezone correctly configured","Task configuration with valid cron expressions (e.g., '0 9 * * *')"],"input_types":["cron expressions (standard cron format: minute hour day month weekday)","task configuration (keywords, filters, notification channels)","on-demand execution requests (via API or manual trigger)"],"output_types":["execution logs (timestamp, task ID, status, duration, results)","task execution history (last run time, next run time, execution count)","product results (matched products from this execution)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_5","uri":"capability://tool.use.integration.web.based.dashboard.for.task.configuration.and.result.browsing","name":"web-based dashboard for task configuration and result browsing","description":"Provides a FastAPI-served web UI (HTML/CSS/JavaScript frontend) for managing tasks, viewing results, and monitoring system status. The dashboard includes task creation/editing forms, real-time log streaming, result filtering/search, and system health indicators. The UI communicates with the FastAPI backend via REST API calls, with authentication enforced via HTTP Basic Auth. Results can be filtered by task, date range, match score, and price range.","intents":["Configure and manage monitoring tasks through a visual interface without editing JSON files","Browse and filter scraped products with AI analysis results in a searchable table","Monitor real-time task execution progress and view live logs in the dashboard","View system status (API health, configuration, last execution times) at a glance"],"best_for":["Non-technical users managing marketplace monitoring without command-line access","Teams needing a centralized dashboard for monitoring multiple tasks","Developers prototyping marketplace monitoring workflows before building custom UIs"],"limitations":["Web UI is single-page application (SPA) with no server-side rendering; initial load requires JavaScript","Result pagination is client-side only; loading large result sets (>10k products) causes browser slowdown","No export functionality for results (CSV, JSON); users must copy-paste or use API directly","Dashboard has no multi-user support; all users share the same task configuration","UI styling is basic (no dark mode, responsive design is limited); not optimized for mobile"],"requires":["FastAPI framework (included in requirements.txt)","Modern web browser (Chrome, Firefox, Safari, Edge)","HTTP Basic Auth credentials for dashboard access","JavaScript enabled in browser"],"input_types":["task configuration form data (keywords, price filters, cron schedule, prompt selection)","filter parameters (task ID, date range, match score range, price range)","search queries (product title, seller name)"],"output_types":["rendered HTML dashboard with embedded CSS/JavaScript","JSON API responses (tasks, results, logs, system status)","real-time log streams (SSE format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_6","uri":"capability://data.processing.analysis.centralized.configuration.management.with.environment.variables.and.json.files","name":"centralized configuration management with environment variables and json files","description":"Manages system configuration through src/config.py, loading settings from .env file (environment variables) and config.json (task definitions). Environment variables include API keys (OpenAI, Gemini), service URLs (ntfy, WeChat, Telegram), and feature flags. config.json stores task definitions with keywords, price filters, cron schedules, and prompt references. The system validates configuration on startup and provides fallback defaults for optional settings.","intents":["Centralize API keys and service credentials in environment variables for secure deployment","Define monitoring tasks in version-controlled JSON files for reproducibility","Switch between AI providers (OpenAI, Gemini) without code changes","Configure notification channels and feature flags without restarting the service"],"best_for":["Teams deploying the system across multiple environments (dev, staging, production)","Developers needing to manage secrets securely without hardcoding in code","Users wanting to version-control task definitions in Git"],"limitations":["No built-in secret rotation; API keys must be manually updated in .env","config.json has no schema validation; invalid task definitions may cause runtime errors","Environment variables are loaded once at startup; changes require service restart","No configuration encryption; .env file contains plaintext secrets (requires file-level permissions)","No configuration versioning or rollback; updating config.json overwrites previous state"],"requires":[".env file with required environment variables (see .env.example)","config.json with task definitions (optional; can be created via API)","Python 3.10+","File system access to read .env and config.json"],"input_types":["environment variables (API keys, service URLs, feature flags)","JSON task definitions (keywords, price filters, cron schedules, prompt references)","configuration updates via API (task creation/update)"],"output_types":["parsed configuration object (src/config.py Config class)","validation errors (missing required variables, invalid JSON)","configuration status endpoint (/api/status/config)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_7","uri":"capability://data.processing.analysis.product.data.extraction.and.parsing.with.marketplace.specific.selectors","name":"product data extraction and parsing with marketplace-specific selectors","description":"Extracts structured product data from Xianyu marketplace HTML using CSS selectors and XPath expressions defined in src/parsers.py. Parses product title, price, seller info, condition, images, and listing URL from dynamic JavaScript-rendered content. The system handles multiple product card layouts (grid, list views) and gracefully degrades when selectors fail. Extracted data is normalized into a standard schema (ProductData class) for downstream processing.","intents":["Extract structured product information from Xianyu marketplace listings","Handle multiple product card layouts and HTML variations without manual intervention","Normalize extracted data into a consistent schema for AI analysis and storage","Debug parser failures by logging failed selectors and HTML snippets"],"best_for":["Developers building marketplace monitoring agents for Xianyu","Teams needing to extract product data from dynamic JavaScript-rendered content","Users monitoring Xianyu without API access"],"limitations":["CSS selectors are brittle; Xianyu HTML changes require parser updates in src/parsers.py","No automatic selector discovery; new product card layouts require manual selector definition","Image extraction relies on src attributes; lazy-loaded images may not be captured","Seller info extraction is incomplete (no seller ID, rating history); only basic seller name/link","Price parsing assumes Chinese currency (¥); no multi-currency support"],"requires":["Playwright browser instance with loaded Xianyu product page","CSS selectors or XPath expressions for target product card layout","Python 3.10+","BeautifulSoup or similar HTML parsing library (included in requirements.txt)"],"input_types":["HTML content (from Playwright page.content())","product card selectors (CSS or XPath)","search parameters (keywords, filters)"],"output_types":["structured product data (ProductData objects with title, price, seller, images, URL)","parsing logs (selector matches, extraction errors, fallback values)","raw HTML snippets (for debugging failed extractions)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_8","uri":"capability://image.visual.image.encoding.and.preprocessing.for.multimodal.ai.analysis","name":"image encoding and preprocessing for multimodal ai analysis","description":"Converts product images to base64-encoded format for transmission to multimodal AI APIs (GPT-4V, Gemini) through encode_image_to_base64() in src/ai_handler.py. Handles multiple image formats (JPEG, PNG, WebP), downloads images from URLs, and applies optional preprocessing (resizing, compression) to reduce payload size. The system validates image dimensions and file sizes before encoding to prevent API errors.","intents":["Prepare product images for multimodal AI analysis without manual conversion","Optimize image payloads to reduce API costs and latency","Handle multiple image formats and remote image URLs transparently","Validate images before sending to AI APIs to prevent errors"],"best_for":["Developers building multimodal AI analysis pipelines for product images","Teams needing to optimize image payloads for cost-sensitive AI API calls","Users analyzing product condition and authenticity through visual inspection"],"limitations":["Image download adds 500ms-2s per product (network latency); no caching across runs","Base64 encoding increases payload size by ~33%; no compression before encoding","Image resizing is optional; large images (>5MB) may exceed API limits","No image validation for quality (blur, darkness); garbage images are processed anyway","EXIF data is stripped during encoding; no metadata preservation for image analysis"],"requires":["Python 3.10+","PIL/Pillow library for image processing (included in requirements.txt)","Network connectivity to download images from URLs","Image URLs or local file paths accessible to the system"],"input_types":["image URLs (from product listings)","local image file paths","image format specifications (JPEG, PNG, WebP)","optional preprocessing parameters (max width, max height, quality)"],"output_types":["base64-encoded image strings (for API transmission)","image metadata (format, dimensions, file size)","preprocessing logs (resizing operations, compression ratios)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-usagi-org--ai-goofish-monitor__cap_9","uri":"capability://text.generation.language.prompt.template.management.with.task.specific.customization","name":"prompt template management with task-specific customization","description":"Manages AI prompts through a two-tier system: base prompts (prompts/base_prompt.txt) containing system instructions for product analysis, and task-specific criteria files (prompts/*.txt) containing user-defined product preferences. The system combines base prompt + task criteria + product data into a final prompt sent to the AI API. Prompt templates support variable substitution (e.g., {product_title}, {price_range}) for dynamic content injection.","intents":["Define reusable base prompts for consistent AI behavior across all tasks","Customize AI analysis criteria per task without modifying base prompts","Use variable substitution to inject dynamic product data into prompts","Version-control prompts in Git for reproducibility and audit trails"],"best_for":["Teams managing multiple monitoring tasks with different AI analysis criteria","Developers building prompt engineering workflows for marketplace analysis","Users wanting to customize AI behavior without code changes"],"limitations":["No prompt versioning; updating a prompt file affects all tasks using that prompt","Variable substitution is basic (string replacement); no conditional logic or loops","No prompt validation; invalid prompts may cause AI API errors or unexpected behavior","Prompt files are plain text; no schema or type checking for prompt structure","No A/B testing framework; no built-in way to compare prompt effectiveness"],"requires":["Prompt files in prompts/ directory (base_prompt.txt + task-specific files)","Variable names matching product data schema (e.g., {product_title}, {price})","Text editor for prompt editing (no UI for prompt management)"],"input_types":["base prompt template (system instructions for AI analysis)","task-specific criteria files (user preferences for product matching)","product data (for variable substitution)"],"output_types":["combined prompt string (base + criteria + product data)","prompt validation errors (missing variables, invalid syntax)","prompt usage logs (which prompts are used by which tasks)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Playwright browsers (Chromium/Edge) installed via playwright install","2GB+ RAM for concurrent task execution","Valid Xianyu account credentials for login automation","OpenAI API key (GPT-4V) or Google Gemini API key","AsyncOpenAI client configured in src/config.py","Product images accessible as URLs or local files","Task-specific prompt files in prompts/ directory","Network connectivity for API calls","Docker 20.10+ or Docker Desktop"],"failure_modes":["Playwright browser instances consume 100-200MB RAM each; concurrent task count limited by available memory","Anti-bot detection may require periodic cookie refresh and session rotation","Xianyu API changes require parser updates in src/parsers.py; no built-in schema versioning","Task scheduling relies on system cron; no distributed task queue for multi-machine deployments","API latency adds 2-5 seconds per product analysis; batch processing limited by rate limits (3-5 req/sec for OpenAI)","Image encoding to base64 adds ~100-300ms per product; no caching of encoded images across runs","Prompt engineering required for accurate filtering; no built-in prompt optimization or A/B testing framework","AI responses may be inconsistent across similar products; no confidence thresholds or validation logic","Multimodal analysis costs ~$0.01-0.03 per product with GPT-4V; no cost estimation or budget controls","Docker image size is large (~1-2GB) due to Playwright browser dependencies; slow initial pull","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3559457821993033,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:16.560Z","last_commit":"2026-04-27T10:53:57Z"},"community":{"stars":11531,"forks":1647,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=usagi-org--ai-goofish-monitor","compare_url":"https://unfragile.ai/compare?artifact=usagi-org--ai-goofish-monitor"}},"signature":"Ok8ekme4JmKSr/awAXx0fWcHSFjP9Fo42nE0DdjUL7W0VfjvYMowr+RzpDD2SG1OQnr3zeS8pRCIurR1tP8TBg==","signedAt":"2026-06-19T23:53:58.928Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/usagi-org--ai-goofish-monitor","artifact":"https://unfragile.ai/usagi-org--ai-goofish-monitor","verify":"https://unfragile.ai/api/v1/verify?slug=usagi-org--ai-goofish-monitor","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}