| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Provides access to a manually curated collection of prompts extracted from top-ranked GPTs in OpenAI's official GPT Store, organized by popularity ranking (1st, 2nd, 3rd, etc.) and functional category. The repository maintains markdown files containing the actual system prompts used by high-performing GPTs, enabling developers to inspect and reuse proven prompt patterns without reverse-engineering or API inspection.
Unique: Maintains a manually curated index of actual system prompts from OpenAI's official GPT Store ranked by real-world adoption metrics, rather than generic prompt databases. Organizes prompts hierarchically by category and popularity rank, enabling developers to identify which prompt patterns correlate with high user engagement.
vs alternatives: Differs from generic prompt databases (e.g., PromptBase) by focusing exclusively on proven, top-ranked GPTs from the official store with transparent ranking data, rather than user-submitted prompts of variable quality.
Implements a hierarchical taxonomy organizing prompts across functional domains (Academic, Programming, Design, Productivity, Lifestyle/Entertainment, Education) with subcategories for specialized use cases (e.g., literature review tools, code automation, logo designers). The directory structure enables browsing and filtering prompts by domain without requiring keyword search, making it discoverable for developers seeking domain-specific prompt patterns.
Unique: Uses a multi-level directory taxonomy (Open GPTs → Category → Specialized Subcategory) combined with markdown file naming conventions to enable both programmatic and human-browsable discovery without requiring a search engine or database backend.
vs alternatives: Provides better discoverability than flat prompt lists by organizing around functional domains and real GPT Store categories, while remaining simpler to maintain than a full-featured prompt search platform.
Maintains a dedicated section for community-created prompts (e.g., Mr. Ranedeer, QuickSilver OS) submitted by users outside the official GPT Store, with a contribution workflow that allows developers to add, improve, and version control prompts collaboratively. This enables the repository to function as a community knowledge base where prompt engineering patterns are shared, iterated on, and attributed to contributors.
Unique: Implements a GitHub-based collaborative model where community prompts are version-controlled, attributed to contributors, and discoverable alongside official GPT Store prompts, treating prompt engineering as a collaborative software development practice rather than a static knowledge base.
vs alternatives: Enables community iteration and attribution in ways that centralized prompt marketplaces (PromptBase, OpenAI's own prompt sharing) do not, by leveraging git history and pull request workflows for transparency and collaborative improvement.
Aggregates academic research papers and technical documentation on advanced prompting methodologies including Chain-of-Thought (CoT), Tree-of-Thoughts (ToT), Graph-of-Thoughts (GoT), Skeleton-of-Thought (SoT), Algorithm-of-Thoughts (AoT), and Self-Consistency Improvement techniques. The papers/ directory serves as a curated research index bridging academic literature and practical prompt engineering, enabling developers to understand the theoretical foundations and implementation patterns for sophisticated reasoning prompts.
Unique: Curates a focused collection of peer-reviewed papers specifically on advanced prompting techniques (CoT, ToT, GoT, SoT, AoT) organized by technique type, serving as a bridge between academic research and practical prompt engineering rather than a general LLM research repository.
vs alternatives: Provides a curated, technique-focused research index that's more accessible than searching arXiv or Google Scholar, while remaining more rigorous and research-grounded than generic prompt engineering blogs or tutorials.
Maintains documentation and resources on prompt injection attacks, adversarial prompting, and prompt protection techniques, enabling developers to understand vulnerabilities in GPT-based systems and implement defensive measures. This capability addresses the security dimension of prompt engineering by collecting attack patterns, defense strategies, and mitigation approaches in a centralized, discoverable format.
Unique: Integrates prompt attack and defense resources into a prompt engineering repository, treating security as a first-class concern alongside prompt optimization. Provides attack patterns and defense strategies in a discoverable format rather than scattered across security blogs or research papers.
vs alternatives: Combines attack patterns and defenses in a single resource, whereas most prompt engineering guides focus only on optimization, and security resources are typically separate from prompt engineering communities.
Implements a lightweight, git-based storage system where prompts are maintained as markdown files in a GitHub repository, enabling version control, change tracking, collaborative editing, and attribution through native git workflows. Each prompt is stored as a standalone markdown file with metadata (rank, category, description) embedded or inferred from filename and directory structure, making prompts both human-readable and machine-parseable.
Unique: Uses git and markdown as the primary storage and versioning mechanism rather than a custom database or prompt management platform, leveraging existing developer workflows and tools while maintaining simplicity and transparency through readable file formats.
vs alternatives: Provides version control and collaboration benefits of git-based systems without requiring custom infrastructure, whereas dedicated prompt management platforms (e.g., Langchain Hub) require proprietary APIs and don't integrate as naturally with developer workflows.
Exposes prompts ranked by their corresponding GPT's position in the OpenAI GPT Store (1st, 2nd, 3rd, etc.), providing a popularity-based ranking signal that correlates with real-world user adoption and perceived effectiveness. Developers can browse prompts ordered by rank to identify which prompt patterns are most successful in the market, using ranking as a proxy for prompt quality and effectiveness.
Unique: Surfaces GPT Store ranking data as a discovery mechanism, treating rank as a quality signal and enabling developers to identify market-validated prompt patterns without requiring manual evaluation or performance testing.
vs alternatives: Provides ranking-based discovery that generic prompt databases lack, while remaining simpler than building a full competitive analysis platform with real-time GPT Store scraping.
Maintains a comprehensive library of prompt templates spanning diverse domains (Academic, Programming, Design, Productivity, Lifestyle/Entertainment, Education) with specialized subcategories (literature review, code automation, logo design, task automation, adventure games, homework help). This enables developers to find domain-specific prompt patterns without building from scratch, with templates covering both common use cases and specialized applications.
Unique: Organizes templates across six major domains with specialized subcategories, providing breadth across use cases while maintaining focus on real GPT Store applications rather than generic prompt templates.
vs alternatives: Covers more domains and real-world use cases than most prompt template libraries, while remaining more focused and curated than generic prompt databases.
+1 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 awesome-prompts at 35/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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