{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-sansan0--trendradar","slug":"sansan0--trendradar","name":"TrendRadar","type":"webapp","url":"https://sansan0.github.io/TrendRadar/","page_url":"https://unfragile.ai/sansan0--trendradar","categories":["data-analysis","observability"],"tags":["ai","bark","data-analysis","docker","hot-news","llm","mail","mcp","mcp-server","news","ntfy","python","rss","trending-topics","wechat","wework"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-sansan0--trendradar__cap_0","uri":"capability://data.processing.analysis.multi.platform.trending.topic.aggregation.with.unified.feed.normalization","name":"multi-platform trending topic aggregation with unified feed normalization","description":"Crawls 11+ Chinese social platforms (Zhihu, Weibo, Bilibili, Douyin, etc.) and RSS feeds simultaneously, normalizing heterogeneous data schemas into a unified NewsItem model with platform-agnostic metadata. Uses platform-specific adapters that extract title, URL, hotness rank, and engagement metrics, then merges results into a single deduplicated feed ordered by composite hotness score (rank × 0.6 + frequency × 0.3 + platform_hot_value × 0.1).","intents":["Monitor trending topics across all major Chinese social platforms without visiting each app individually","Detect new trending topics in real-time with 🆕 markers to distinguish fresh vs. persistent trends","Aggregate RSS feeds alongside social platform data for comprehensive information coverage","Normalize disparate platform APIs and web scraping results into consistent data structures"],"best_for":["Investors and traders monitoring stock-related news across multiple platforms","Content creators tracking trending topics for viral content opportunities","PR professionals and brand managers monitoring public sentiment in real-time"],"limitations":["Platform-specific crawlers may break if target platforms change HTML structure or API endpoints without notice","RSS feed reliability depends on feed provider uptime and update frequency","Deduplication uses fuzzy title matching which may miss semantically identical but textually different articles","Rate limiting on source platforms may cause incomplete data collection during high-traffic periods"],"requires":["Python 3.9+","Network access to 11+ social platforms and RSS feed sources","Platform-specific headers/user-agents to bypass basic bot detection"],"input_types":["Platform URLs (Zhihu, Weibo, Bilibili, etc.)","RSS feed URLs","Configuration specifying which platforms to monitor"],"output_types":["Normalized NewsItem objects with fields: title, url, platform, hotness_rank, frequency, timestamp","Deduplicated feed sorted by composite hotness score","JSON/CSV export of aggregated trends"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_1","uri":"capability://data.processing.analysis.keyword.based.content.filtering.with.regex.and.logical.operators","name":"keyword-based content filtering with regex and logical operators","description":"Filters aggregated news against user-defined keyword lists (frequency_words.txt) using regex pattern matching and boolean logic (required keywords AND, excluded keywords NOT). Implements a scoring engine that weights matches by keyword frequency tier and calculates relevance scores. Supports regex patterns, case-insensitive matching, and multi-language keyword sets. Articles matching filter criteria are retained; non-matching articles are discarded before analysis and notification stages.","intents":["Filter news to only topics matching investor's stock watchlist (e.g., 'BYD', 'Tesla', 'EV')","Exclude noise keywords (e.g., 'advertisement', 'sponsored') to reduce false positives","Create multiple keyword profiles for different monitoring use cases (stocks, brands, competitors)","Dynamically adjust keyword sensitivity without restarting the system"],"best_for":["Investors with specific stock/sector watchlists","Brand managers monitoring specific product categories or competitor mentions","Content creators tracking niche topics without general news noise"],"limitations":["Regex-based matching is language-agnostic but may miss semantic synonyms (e.g., 'EV' vs. '电动车')","Boolean logic is AND/NOT only; no OR operator for alternative keywords in single rule","Keyword frequency tiers are static and require manual adjustment for seasonal trends","No machine learning-based relevance ranking; purely rule-based filtering"],"requires":["frequency_words.txt configuration file with keyword list","Understanding of regex syntax for advanced pattern matching","Python 3.9+ for regex engine support"],"input_types":["Aggregated NewsItem objects from multi-platform crawler","Keyword configuration file (frequency_words.txt) with regex patterns","Boolean filter rules (required/excluded keywords)"],"output_types":["Filtered NewsItem list matching keyword criteria","Relevance scores per article (0-1 scale)","Filter match statistics (matched keywords, match count)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_10","uri":"capability://data.processing.analysis.real.time.new.topic.detection.with.markers.and.trend.velocity.calculation","name":"real-time new topic detection with 🆕 markers and trend velocity calculation","description":"Detects newly trending topics by comparing current aggregated feed against historical baseline (previous execution results). Marks new topics with 🆕 emoji and calculates trend velocity (rate of rank change) to identify rapidly rising topics. Implements configurable sensitivity thresholds to distinguish genuine new trends from noise. Stores historical snapshots to enable trend trajectory analysis and prediction.","intents":["Identify newly trending topics for early-stage investment or content opportunities","Distinguish new trends from persistent topics to avoid redundant notifications","Calculate trend velocity to identify rapidly rising topics with high momentum","Track trend lifecycle (emerging, peak, declining) for predictive analysis"],"best_for":["Investors wanting early-stage trend detection for trading signals","Content creators seeking emerging topics before mainstream coverage","Trend analysts studying trend lifecycle and momentum"],"limitations":["New topic detection requires historical baseline; first execution has no baseline for comparison","Sensitivity thresholds are static; may miss subtle trends or generate false positives","Trend velocity calculation assumes linear trend progression; may fail for volatile topics","Historical snapshots require persistent storage; in-memory storage loses history on restart","No machine learning-based anomaly detection; purely rule-based thresholding"],"requires":["Historical trend data from previous executions","Configurable sensitivity thresholds in config.yaml","Persistent storage for historical snapshots"],"input_types":["Current aggregated news feed","Historical trend snapshots from previous executions","Sensitivity threshold configuration"],"output_types":["Marked new topics with 🆕 emoji","Trend velocity scores (rate of rank change)","Trend lifecycle classification (emerging, peak, declining)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_11","uri":"capability://data.processing.analysis.multi.region.display.configuration.with.platform.specific.content.filtering","name":"multi-region display configuration with platform-specific content filtering","description":"Supports region-specific content filtering and display preferences (e.g., show only Mainland China trends, exclude Hong Kong/Taiwan content, or vice versa). Implements per-region keyword lists and notification channel routing (e.g., send Mainland China trends to WeChat, international trends to Telegram). Allows users to configure multiple region profiles and switch between them based on monitoring focus.","intents":["Monitor trends specific to Mainland China, Hong Kong, Taiwan, or international markets separately","Route region-specific content to appropriate notification channels","Create separate monitoring profiles for different geographic markets","Filter out irrelevant regional content to reduce notification noise"],"best_for":["Multinational investors monitoring multiple geographic markets","Teams with region-specific responsibilities (e.g., China team, international team)","Organizations needing to comply with regional content policies"],"limitations":["Region detection relies on platform metadata or URL parsing; may be inaccurate for cross-regional content","Per-region keyword lists require manual maintenance and duplication","No automatic region classification; requires manual configuration","Channel routing logic is static; no dynamic routing based on content characteristics"],"requires":["Region configuration in config.yaml with platform-to-region mappings","Per-region keyword lists (frequency_words_cn.txt, frequency_words_intl.txt, etc.)","Channel routing configuration specifying region-to-channel mappings"],"input_types":["Aggregated news feed with platform metadata","Region configuration and keyword lists","Channel routing rules"],"output_types":["Region-filtered news feed","Region-specific notifications routed to appropriate channels","Region statistics (article count, trend count per region)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_12","uri":"capability://automation.workflow.execution.mode.abstraction.supporting.github.actions.docker.and.local.python.deployment","name":"execution mode abstraction supporting github actions, docker, and local python deployment","description":"Abstracts deployment environment differences through unified execution mode interface. Detects runtime environment (GitHub Actions, Docker container, local Python) and applies mode-specific configuration (storage backend, notification channels, scheduling mechanism). Supports seamless migration between deployment modes without code changes. Implements environment-specific error handling and logging (e.g., GitHub Actions annotations for CI/CD visibility).","intents":["Deploy TrendRadar on GitHub Actions without modifying code","Run TrendRadar in Docker container with local or cloud storage","Execute TrendRadar as local Python script for development and testing","Migrate between deployment modes (e.g., GitHub Actions to Docker) without reconfiguration"],"best_for":["Teams wanting flexibility in deployment options","Organizations with heterogeneous infrastructure (some services on GitHub Actions, others on Docker)","Developers needing consistent behavior across dev/staging/production environments"],"limitations":["Execution mode detection is heuristic-based; may misidentify environment in edge cases","Mode-specific optimizations may not be optimal for all use cases","Error messages and logging format differs by mode; complicates debugging across modes","Some features may not be available in all modes (e.g., hot-reload not supported in GitHub Actions)"],"requires":["Environment detection logic in application startup","Mode-specific configuration templates","Support for all three execution modes in codebase"],"input_types":["Runtime environment variables (GITHUB_ACTIONS, DOCKER_CONTAINER, etc.)","Mode-specific configuration files"],"output_types":["Detected execution mode","Applied configuration for detected mode","Mode-specific logs and error messages"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_2","uri":"capability://text.generation.language.ai.powered.news.analysis.and.summarization.via.litellm.multi.provider.abstraction","name":"ai-powered news analysis and summarization via litellm multi-provider abstraction","description":"Sends filtered news articles to LiteLLM, which abstracts over multiple LLM providers (OpenAI, Anthropic, Ollama, local models, etc.) to generate structured analysis including sentiment classification, key entity extraction, trend prediction, and executive summaries. Uses configurable system prompts and temperature settings per provider. Results are cached to avoid redundant API calls and formatted as structured JSON for downstream processing and notification delivery.","intents":["Generate AI-powered summaries of trending news for quick consumption","Classify sentiment (positive/negative/neutral) of trending topics for investor decision-making","Extract key entities (companies, people, locations) from news articles automatically","Predict trend direction (rising/falling/stable) based on article content and historical patterns"],"best_for":["Teams using multiple LLM providers and wanting unified interface","Organizations with cost constraints who want to switch between OpenAI, Anthropic, and open-source models","Investors needing AI-generated sentiment analysis for trading signals"],"limitations":["LiteLLM abstraction adds ~200-500ms latency per API call due to provider routing and retry logic","Analysis quality depends on underlying LLM provider; no guarantee of consistent output across providers","Caching is in-memory only; no persistent cache across application restarts","Structured output parsing may fail if LLM returns malformed JSON, requiring fallback to raw text","Cost scales with article volume; no built-in rate limiting or budget controls"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","LiteLLM Python library (included in dependencies)","Network access to chosen LLM provider endpoints","Configuration of provider credentials in config.yaml"],"input_types":["Filtered NewsItem objects with title, URL, content","System prompt template for analysis instructions","Provider configuration (model name, temperature, max_tokens)"],"output_types":["Structured JSON with fields: summary, sentiment, entities, trend_direction, confidence_score","Plain text summaries for human-readable output","Analysis metadata (provider used, latency, token count)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_3","uri":"capability://text.generation.language.ai.powered.multilingual.translation.with.provider.fallback","name":"ai-powered multilingual translation with provider fallback","description":"Translates article titles and summaries from Chinese to English (or other target languages) using LiteLLM-abstracted LLM providers with automatic fallback to alternative providers if primary provider fails. Maintains translation cache to avoid redundant API calls for identical content. Supports batch translation of multiple articles in single API call to reduce latency and cost. Integrates with notification system to deliver translated content to non-Chinese-speaking users.","intents":["Translate Chinese trending news to English for international investors and teams","Support multilingual teams monitoring Chinese market trends without language barriers","Reduce translation API costs by batching multiple articles per request","Ensure translation availability with automatic fallback if primary LLM provider is unavailable"],"best_for":["International teams monitoring Chinese market trends","Multinational companies with non-Chinese-speaking stakeholders","Organizations wanting to reduce translation service costs via LLM-based approach"],"limitations":["Translation quality varies by LLM provider; no guarantee of accuracy for technical or domain-specific terms","Batch translation may timeout if article count exceeds provider token limits","Cache is in-memory only; translations lost on application restart","Fallback provider switching adds latency (200-500ms per retry)","No human review loop; mistranslations propagate directly to end users"],"requires":["API keys for at least one LLM provider supporting translation","LiteLLM library with translation-capable model (GPT-4, Claude, etc.)","Network access to LLM provider endpoints","Target language configuration in config.yaml"],"input_types":["Article titles and summaries in Chinese","Batch size configuration (number of articles per translation request)","Target language specification (English, Spanish, etc.)"],"output_types":["Translated article titles and summaries","Translation metadata (source language, target language, provider used, latency)","Cache hit/miss statistics"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_4","uri":"capability://automation.workflow.multi.channel.notification.delivery.with.atomic.message.batching","name":"multi-channel notification delivery with atomic message batching","description":"Distributes filtered and analyzed news to 9+ notification channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.) using channel-specific adapters. Implements atomic message batching to group multiple articles into single notification payloads, respecting per-channel rate limits and message size constraints. Supports channel-specific formatting (Markdown for Slack, card format for WeWork, plain text for Email). Includes retry logic with exponential backoff for failed deliveries and delivery status tracking.","intents":["Push trending news to team communication platforms (WeWork, Feishu, Slack) for collaborative monitoring","Send mobile notifications (Bark, ntfy) for time-sensitive trending topics","Deliver email digests to non-technical stakeholders","Batch multiple articles into single notification to reduce notification fatigue"],"best_for":["Teams using WeChat/WeWork/Feishu for internal communication","Organizations wanting to consolidate notifications across multiple platforms","Mobile-first users preferring push notifications (Bark, ntfy) over email"],"limitations":["Each channel has different rate limits and message size constraints; batching logic must account for all","Channel-specific formatting requires maintenance as platforms update their APIs","Delivery status is not guaranteed; failed notifications may not be retried if application crashes","No persistent delivery queue; messages in-flight are lost on restart","WeChat/WeWork require valid credentials and may have IP whitelist restrictions"],"requires":["Channel-specific credentials (WeChat token, WeWork webhook, Telegram bot token, etc.)","Network access to all configured notification channels","Configuration of channel endpoints and formatting preferences in config.yaml"],"input_types":["Analyzed NewsItem objects with summary, sentiment, entities","Channel configuration specifying enabled channels and formatting preferences","Batch size and rate limit settings per channel"],"output_types":["Formatted notification messages (Markdown, card format, plain text)","Delivery status per channel (success, failed, rate-limited)","Delivery latency metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_5","uri":"capability://tool.use.integration.model.context.protocol.mcp.server.for.conversational.trend.analysis.and.querying","name":"model context protocol (mcp) server for conversational trend analysis and querying","description":"Exposes TrendRadar's aggregated news and analysis data as an MCP server with tool definitions for natural language queries. Allows AI agents and LLM-based applications to query trending topics, filter by keywords, retrieve sentiment analysis, and generate trend reports via conversational interface. Implements tools like 'analyze_trends', 'get_trending_topics', 'search_by_keyword' that accept natural language parameters and return structured JSON responses. Integrates with Claude and other MCP-compatible clients for seamless agent-based analysis.","intents":["Query trending topics conversationally without writing code or using CLI","Integrate TrendRadar data into AI agent workflows for automated decision-making","Enable Claude and other LLM clients to access trend data via MCP protocol","Generate custom trend reports by combining multiple analysis tools in conversation"],"best_for":["AI agent developers building trend-aware applications","Teams using Claude or other MCP-compatible LLM clients","Non-technical users wanting conversational access to trend data"],"limitations":["MCP server must be running separately; adds deployment complexity","Tool definitions are static; adding new analysis tools requires code changes","Natural language parameter parsing relies on LLM client's understanding; ambiguous queries may fail","No built-in authentication; requires network isolation or API key validation","Response latency includes LLM inference time for parameter extraction plus data retrieval"],"requires":["MCP server running (Python process or Docker container)","MCP-compatible client (Claude, custom LLM application)","Network connectivity between client and MCP server","Configuration of MCP server endpoint in client application"],"input_types":["Natural language queries from LLM client","Tool invocation requests with parameters (topic, keyword, date range, etc.)","MCP protocol messages (JSON-RPC 2.0)"],"output_types":["Structured JSON responses with trending topics, sentiment analysis, entities","Trend reports with statistics and visualizations","Tool definitions and schema for MCP client discovery"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_6","uri":"capability://automation.workflow.scheduled.execution.with.timeline.based.configuration.and.github.actions.integration","name":"scheduled execution with timeline-based configuration and github actions integration","description":"Orchestrates periodic execution of the entire news aggregation and analysis pipeline using Python scheduler (APScheduler or similar) configured via timeline.yaml. Supports multiple execution schedules (e.g., hourly for stock monitoring, daily for general trends) with timezone awareness. Integrates with GitHub Actions for cloud-based scheduling without local infrastructure, storing results in cloud storage (S3, GitHub Releases, etc.). Implements graceful shutdown and error recovery to ensure reliable scheduled execution.","intents":["Run news aggregation and analysis on fixed schedule (hourly, daily, weekly) without manual intervention","Deploy TrendRadar on GitHub Actions for serverless execution without maintaining local infrastructure","Store results in cloud storage for historical trend analysis and reporting","Configure different schedules for different monitoring use cases (stock market hours vs. 24/7 monitoring)"],"best_for":["Teams wanting serverless deployment without managing servers","Organizations with GitHub repositories who want integrated CI/CD-based scheduling","Investors needing periodic trend reports during market hours"],"limitations":["GitHub Actions has 6-hour maximum job runtime; long-running aggregations may timeout","Scheduled execution is subject to GitHub Actions queue delays during high-traffic periods","Cloud storage costs scale with execution frequency and result size","Timezone handling requires careful configuration; daylight saving time transitions may cause missed executions","No persistent state between executions; requires external storage for trend history"],"requires":["GitHub repository with Actions enabled","Cloud storage credentials (S3, GitHub Releases, etc.)","timeline.yaml configuration with schedule definitions","Python 3.9+ with APScheduler or similar scheduling library"],"input_types":["timeline.yaml with schedule definitions (cron expressions, timezone)","Cloud storage configuration (bucket name, credentials)","Execution mode (GitHub Actions vs. local Python)"],"output_types":["Scheduled execution logs","Results stored in cloud storage (JSON, CSV, HTML reports)","GitHub Actions workflow status and artifacts"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_7","uri":"capability://data.processing.analysis.local.and.cloud.storage.abstraction.with.multi.backend.support","name":"local and cloud storage abstraction with multi-backend support","description":"Provides unified storage interface supporting multiple backends: local filesystem (Docker volumes, mounted directories), cloud storage (S3, GitHub Releases, Alibaba OSS), and database storage (SQLite, PostgreSQL). Implements automatic data persistence for aggregated news, analysis results, and execution logs. Supports both streaming writes (for real-time updates) and batch writes (for periodic reports). Includes data retention policies and automatic cleanup of old data based on configurable TTL.","intents":["Store aggregated news and analysis results persistently across application restarts","Archive historical trend data for long-term analysis and reporting","Support both local deployment (Docker volumes) and cloud deployment (S3) without code changes","Implement data retention policies to manage storage costs and comply with data governance"],"best_for":["Organizations with data governance requirements needing persistent storage","Teams deploying on Docker wanting local volume storage","Cloud-native deployments using S3 or other object storage"],"limitations":["Storage backend switching requires application restart; no hot-swap capability","Cloud storage latency (S3 API calls) adds 100-500ms per write operation","Data retention policies are time-based only; no size-based or event-based cleanup","No built-in data encryption; requires external encryption for sensitive data","Cross-backend queries (e.g., joining local and cloud data) not supported"],"requires":["Storage backend credentials (S3 access key, GitHub token, OSS credentials)","Storage configuration in config.yaml specifying backend type and location","Sufficient disk space (local) or cloud storage quota (S3, OSS)"],"input_types":["Aggregated NewsItem objects","Analysis results (sentiment, entities, summaries)","Execution logs and metadata"],"output_types":["Persisted data in backend format (JSON files, database records, S3 objects)","Data export in multiple formats (JSON, CSV, HTML reports)","Storage usage statistics and cleanup logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_8","uri":"capability://automation.workflow.docker.containerization.with.orchestrated.multi.service.deployment","name":"docker containerization with orchestrated multi-service deployment","description":"Packages TrendRadar as Docker image with docker-compose orchestration for multi-service deployment including news crawler, AI analysis service, notification service, and optional MCP server. Implements health checks, automatic restart policies, and volume mounts for persistent storage. Supports environment variable configuration for secrets management (API keys, credentials). Includes pre-built images for common deployment scenarios (GitHub Actions, local Docker, cloud platforms).","intents":["Deploy TrendRadar as containerized application without managing Python dependencies","Orchestrate multiple services (crawler, analyzer, notifier) with docker-compose","Manage secrets and configuration via environment variables","Scale individual services independently (e.g., run multiple crawler instances)"],"best_for":["DevOps teams deploying on Kubernetes or Docker Swarm","Organizations with containerization standards","Teams wanting reproducible deployments across environments"],"limitations":["Docker image size is large (~500MB) due to Python dependencies; slow to pull on slow networks","Container startup time includes Python interpreter initialization (~5-10 seconds)","Volume mount performance on Docker Desktop (Mac/Windows) is slower than native filesystem","Secrets management via environment variables is less secure than external secret stores","No built-in container orchestration; requires external tools (Kubernetes, Docker Swarm)"],"requires":["Docker 20.10+ and docker-compose 2.0+","Docker Hub account or private registry for image storage","Sufficient disk space for image and volume storage","Environment variables for API keys and configuration"],"input_types":["Dockerfile with build instructions","docker-compose.yaml with service definitions","Environment variable configuration (.env file)"],"output_types":["Docker image (published to registry)","Running containers with health status","Container logs and metrics"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-sansan0--trendradar__cap_9","uri":"capability://automation.workflow.visual.configuration.editor.with.real.time.validation.and.preview","name":"visual configuration editor with real-time validation and preview","description":"Provides web-based UI for editing configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation, syntax highlighting, and preview of filter results. Implements live keyword filter testing against sample articles to show matching results before deployment. Supports configuration versioning and rollback to previous versions. Integrates with main application to apply configuration changes without restart (hot-reload for certain settings).","intents":["Configure TrendRadar without editing YAML files manually","Test keyword filters against sample articles before deployment","Manage multiple configuration profiles for different monitoring use cases","Rollback to previous configuration if changes cause issues"],"best_for":["Non-technical users who prefer UI over YAML editing","Teams wanting to test configuration changes before deployment","Organizations with multiple monitoring profiles needing easy switching"],"limitations":["Web UI adds deployment complexity; requires separate web server process","Real-time validation requires parsing YAML and testing filters; may be slow for large keyword lists","Hot-reload is not supported for all settings; some changes still require restart","No built-in authentication; requires external reverse proxy for security","Preview results are based on cached sample articles; may not reflect current trends"],"requires":["Web server (Flask, FastAPI, etc.) running alongside main application","Network access to web UI (localhost or internal network)","Browser with JavaScript support for interactive features"],"input_types":["Configuration file content (YAML, text)","Sample articles for filter testing","User input via web form"],"output_types":["Validated configuration files","Filter test results with matching articles","Configuration version history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Network access to 11+ social platforms and RSS feed sources","Platform-specific headers/user-agents to bypass basic bot detection","frequency_words.txt configuration file with keyword list","Understanding of regex syntax for advanced pattern matching","Python 3.9+ for regex engine support","Historical trend data from previous executions","Configurable sensitivity thresholds in config.yaml","Persistent storage for historical snapshots","Region configuration in config.yaml with platform-to-region mappings"],"failure_modes":["Platform-specific crawlers may break if target platforms change HTML structure or API endpoints without notice","RSS feed reliability depends on feed provider uptime and update frequency","Deduplication uses fuzzy title matching which may miss semantically identical but textually different articles","Rate limiting on source platforms may cause incomplete data collection during high-traffic periods","Regex-based matching is language-agnostic but may miss semantic synonyms (e.g., 'EV' vs. '电动车')","Boolean logic is AND/NOT only; no OR operator for alternative keywords in single rule","Keyword frequency tiers are static and require manual adjustment for seasonal trends","No machine learning-based relevance ranking; purely rule-based filtering","New topic detection requires historical baseline; first execution has no baseline for comparison","Sensitivity thresholds are static; may miss subtle trends or generate false positives","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4359168249039644,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:56:56.344Z","last_commit":"2026-04-30T02:09:11Z"},"community":{"stars":56206,"forks":24009,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=sansan0--trendradar","compare_url":"https://unfragile.ai/compare?artifact=sansan0--trendradar"}},"signature":"jS9/yEN8bZcfd/L27W5mDptWcmE81nGWI8bq1AppLl449Ngom6TdbMP0B6UVdiKOnuR3eAcaMEWkNwQqRPcADQ==","signedAt":"2026-06-20T19:01:59.097Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sansan0--trendradar","artifact":"https://unfragile.ai/sansan0--trendradar","verify":"https://unfragile.ai/api/v1/verify?slug=sansan0--trendradar","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"}}