| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Maintains dual persistence between a PostgreSQL database and a flat-file prompts.csv, enabling Git-based version control and contributor attribution tracking. The system uses a synchronization layer (scripts/generate-contributors.sh) that bidirectionally syncs changes, allowing community contributions via pull requests to automatically update the database while database mutations can be exported back to CSV for version control. This architecture enables both programmatic access (via API/database) and human-readable, version-controlled prompt management.
Unique: Uses a flat-file CSV as the source of truth for Git version control while maintaining a live PostgreSQL database, with bidirectional sync scripts that automatically attribute contributors based on Git history and CSV mutations. This dual-persistence model is uncommon — most prompt platforms use database-only or file-only storage.
vs alternatives: Enables true open-source collaboration (pull requests to CSV) while maintaining API-queryable database state, unlike GitHub-only prompt repos that lack structured querying or database-only platforms that lose Git history.
Provides a hierarchical discovery system supporting ChatGPT, Claude, Gemini, Llama, and other LLM models through category and tag-based filtering. The system uses a configuration-driven approach (prompts.config.ts) to define categories, tags, and discovery paths, with Server Components rendering filtered prompt lists (discovery-prompts.tsx) that support both curated and algorithmic discovery. The architecture separates content discovery logic from rendering, allowing different discovery strategies (homepage curation, category browsing, tag filtering) to coexist.
Unique: Uses a configuration-driven discovery system (prompts.config.ts) that decouples taxonomy definition from rendering logic, enabling self-hosted instances to customize discovery without code changes. The Server Component architecture (discovery-prompts.tsx) renders filtered lists server-side, reducing client-side JavaScript and enabling SEO-friendly discovery pages.
vs alternatives: More flexible than hardcoded discovery (like early ChatGPT prompt repos) because taxonomy is configuration-driven; more performant than client-side filtering because Server Components pre-filter on the server and send only relevant prompts to the browser.
Supports importing and exporting prompts in multiple formats (CSV, JSON, YAML, etc.) with automatic format conversion and validation. The system can bulk-import prompts from external sources (e.g., GitHub repos, CSV files) and export the library for backup or migration. Import validation checks for required fields and data integrity, with error reporting for invalid records.
Unique: Implements import/export as a core feature with support for multiple formats and automatic validation, enabling users to migrate prompts between platforms and backup their libraries. The bidirectional CSV sync (described earlier) is an extension of this capability for Git-based workflows.
vs alternatives: More flexible than platform-locked prompt repos because it supports multiple formats and enables migration; more robust than manual copy-paste because it includes validation and error reporting. Differs from generic data import tools by being tailored to prompt-specific schemas.
Enables creation of multi-step prompt workflows where the output of one prompt feeds into the next, with execution orchestration and state management across steps. The system supports conditional branching, loops, and error handling, allowing complex reasoning chains to be defined declaratively. Workflow state is persisted, enabling resumption and debugging of long-running chains.
Unique: Implements workflow chains as a declarative system where prompts are connected as nodes in a directed graph, with automatic state passing between steps. This enables complex reasoning patterns (like chain-of-thought) to be defined and reused without custom code.
vs alternatives: More integrated than external workflow tools (like Zapier) because workflows are defined within the prompt library; more flexible than rigid prompt templates because workflows support branching and loops. Differs from general-purpose workflow engines by being specialized for prompt execution and reasoning chains.
Provides educational resources for learning prompt engineering, including an interactive prompt writing guide and a kids learning game that teaches prompt concepts through gamification. The system includes structured lessons, interactive exercises, and progress tracking, with content tailored to different skill levels (beginner to advanced). The kids game uses game mechanics (points, badges, levels) to make learning engaging.
Unique: Integrates educational content and gamification into the prompt library platform, treating prompt engineering as a learnable skill with structured curriculum and interactive exercises. The kids game is a unique differentiator that makes AI concepts accessible to younger audiences.
vs alternatives: More engaging than static documentation because it includes interactive exercises and gamification; more accessible than academic courses because it's free and integrated into the platform. Differs from generic learning platforms by being specialized for prompt engineering.
Provides a command-line interface for managing prompts locally, including operations like search, create, edit, delete, and batch operations. The CLI can interact with both local files and remote instances (via API), enabling developers to manage prompts from their terminal without a web browser. The tool supports scripting and automation, with output formats suitable for piping to other tools (JSON, CSV).
Unique: Provides a full-featured CLI that mirrors web UI capabilities, enabling developers to manage prompts from their terminal and integrate prompt management into scripts and CI/CD pipelines. The CLI supports both local and remote operations, making it suitable for diverse workflows.
vs alternatives: More scriptable than web UI because CLI output is machine-readable and can be piped to other tools; more integrated than generic API clients because it's purpose-built for prompt operations. Differs from web-only platforms by providing a developer-friendly interface.
Provides browser extensions (for Chrome, Firefox, Safari) and desktop applications that enable prompt access and execution from any web page or application. The extensions allow users to highlight text and apply prompts without leaving the current page, with context-aware prompt suggestions based on the selected text. Desktop apps provide native UI and offline access to the prompt library.
Unique: Extends prompts.chat beyond the web platform with browser extensions and desktop apps, enabling prompt access from any application or web page. The context-aware suggestion system uses selected text to recommend relevant prompts, reducing friction in the prompt selection process.
vs alternatives: More integrated into user workflows than web-only platforms because extensions work on any website; more accessible than CLI tools because extensions provide visual UI. Differs from generic text processing tools by being specialized for prompt application.
Exposes the prompt library as a native MCP server, allowing IDEs like Cursor and Claude Desktop to query and execute prompts directly from the editor without leaving the development environment. The MCP integration (referenced in README.md 137-148) provides tool definitions that map to prompt CRUD operations and discovery endpoints, enabling AI assistants to access, search, and apply prompts as part of their reasoning loop. This architecture treats the prompt library as a first-class tool in the MCP ecosystem rather than a web-only resource.
Unique: Implements MCP as a first-class integration pattern, treating the prompt library as a queryable tool within the MCP ecosystem rather than a web service. This enables IDE-native prompt discovery and execution, positioning prompts.chat as infrastructure for AI-assisted development rather than just a web repository.
vs alternatives: Unlike browser-based prompt repos or simple API endpoints, MCP integration allows prompts to be discovered and applied by AI assistants during reasoning, enabling context-aware prompt selection. More integrated than copy-paste workflows because prompts are live-queried from the MCP server.
+7 more capabilities
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs prompts.chat at 44/100. prompts.chat leads on quality, while TrendRadar is stronger on adoption.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities