| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Converts unified Markdown-based agent definitions (with YAML frontmatter) into tool-specific formats via a two-stage bash/PowerShell pipeline (convert.sh → install.sh). The convert stage parses raw agent files and transforms them into 14+ target formats (.mdc for Cursor, .json for Kiro, aggregated files for Aider/Windsurf, rules for Claude Code/Copilot). The install stage auto-detects local tool installations and deploys converted agents to the correct configuration directories (~/.claude/agents/, .cursor/rules/, etc.), eliminating manual file placement.
Unique: Implements a declarative, tool-agnostic agent definition format (Markdown + YAML) with automated format transpilation and filesystem-aware installation detection. Unlike tool-specific agent builders, this approach treats agent definitions as infrastructure-as-code, enabling version control, CI/CD validation, and cross-tool portability without vendor lock-in.
vs alternatives: Outperforms manual agent creation workflows by eliminating per-tool reformatting; more flexible than tool-native agent stores because agents remain portable and auditable in git.
Organizes 211 pre-built AI agent personas across 18 professional departments (Engineering, Design, Marketing, Sales, Support, Testing, Finance, Legal, HR, Academic, Game Development, Supply Chain, etc.) with 46 agents specifically designed for Chinese platforms (Xiaohongshu, Douyin, WeChat, Feishu, DingTalk). Each agent is defined as a Markdown file containing YAML metadata (name, department, tools, version) and a structured body (identity/mission/rules/deliverables). The library is indexed and linted via CI/CD to ensure consistency and completeness.
Unique: Combines a structured, version-controlled agent library with deep Chinese market specialization (46 original agents for Xiaohongshu, Douyin, WeChat, Feishu, DingTalk) and a standardized YAML+Markdown definition format that enables both human readability and machine parsing. Unlike generic prompt repositories, this library enforces consistent structure (identity/mission/rules/deliverables) and department taxonomy, making agents discoverable and composable.
vs alternatives: Provides 211 pre-built agents vs. starting from scratch; Chinese market agents are unavailable in generic libraries like Awesome Prompts; standardized format enables automated validation and cross-tool deployment.
Integrates with OpenClaw (a workspace management tool) to enable unified deployment and management of agents across multiple tools and projects. OpenClaw provides a centralized interface for selecting, configuring, and deploying agents to local development environments. The integration leverages the conversion pipeline to automatically deploy agents to the correct tool-specific formats and directories. This reduces friction for teams that use multiple tools and want a single point of control for agent deployment.
Unique: Provides a centralized workspace interface for agent deployment, treating agent management as a workspace concern rather than a per-tool concern. This approach simplifies deployment for teams using multiple tools and enables centralized governance.
vs alternatives: More convenient than manual per-tool deployment; enables team-wide standardization on agent definitions; provides a single point of control for agent versions and configurations.
Provides pre-defined scenario runbooks (e.g., 'xiaohongshu-launch', 'product-development', 'infrastructure-deployment') that orchestrate multiple agents through a complete workflow. Each runbook specifies the sequence of agents, handoff protocols, validation checkpoints, and expected outputs. Runbooks are defined in Markdown and can be executed via the NEXUS orchestration framework or manually. This enables teams to standardize on repeatable workflows without building orchestration logic from scratch.
Unique: Treats common workflows as first-class artifacts, providing pre-built runbooks that encode best practices and institutional knowledge. Unlike ad-hoc agent chaining, runbooks are documented, version-controlled, and repeatable, making them suitable for team-wide standardization.
vs alternatives: More structured than manual agent chaining; more flexible than hard-coded workflows because runbooks are text-based and customizable; enables non-technical users to execute complex workflows.
Provides standardized templates and contribution guidelines for adding new agents to the library. Contributors create a Markdown file with YAML frontmatter (metadata) and a structured body (identity, mission, rules, deliverables) following the template. The contribution process includes validation via the linter, peer review, and integration into the appropriate department. The framework ensures consistency across all 211 agents and makes it easy for community members to contribute without understanding the entire codebase.
Unique: Treats agent contribution as a structured, templated process rather than ad-hoc submissions. The framework lowers the barrier to entry for contributors while ensuring quality and consistency through automated validation and peer review.
vs alternatives: More accessible than contributing to generic prompt repositories because templates guide contributors; more consistent than ad-hoc contributions because templates enforce structure; enables community-driven library growth.
Implements a 7-phase orchestration framework (Phases 0–6) for coordinating multiple agents on complex tasks. Each phase defines entry conditions, agent responsibilities, handoff protocols, and validation checkpoints. Agents communicate via standardized handoff templates that specify context, constraints, and expected outputs. The framework includes scenario runbooks (pre-defined workflows for common patterns like 'product launch', 'content creation', 'infrastructure deployment') and agent coordination templates that define who hands off to whom and under what conditions. Implemented via the agency-orchestrator NPM package for programmatic control.
Unique: Defines a formal 7-phase lifecycle with explicit handoff protocols and scenario runbooks, treating multi-agent coordination as a first-class concern rather than an afterthought. Unlike simple agent chaining (e.g., 'call Agent A, then Agent B'), NEXUS enforces validation checkpoints, context preservation, and role-based routing, making workflows auditable and repeatable.
vs alternatives: More structured than LangChain's sequential chains (which lack formal phase definitions); more flexible than rigid state machines because phases can branch based on validation results; includes pre-built runbooks for common scenarios (product launch, content creation).
Converts agent definitions into .mdc (Markdown with Code) rule files for Cursor, Trae, and OpenCode, which use a rules-based system to inject agent personas into the IDE's code completion and suggestion engine. The conversion process extracts the agent's identity, mission, and rules from the Markdown definition and formats them as a .cursor/rules/ file that the IDE loads at startup. Rules are applied contextually — the IDE evaluates them against the current file, selection, and command to determine which agent persona should influence suggestions. This enables IDE-native agent switching without leaving the editor.
Unique: Treats IDE rule files as a deployment target for agent definitions, enabling IDE-native agent personas without external API calls. The conversion process preserves agent semantics (identity, mission, rules) while adapting them to the IDE's rule syntax, making agents portable across different rule-based IDEs.
vs alternatives: Faster than external agent APIs because rules are evaluated locally in the IDE; more flexible than hard-coded IDE behaviors because rules are version-controlled and updatable; enables agent switching without IDE restart.
Provides agent definitions formatted as copy-paste system prompts for Claude Code and GitHub Copilot, which do not support external rule files or configuration. The conversion process extracts the agent's identity, mission, and rules from the Markdown definition and formats them as a plain-text system prompt that can be pasted directly into Claude Code's system prompt field or GitHub Copilot's settings. This is the simplest integration path but requires manual copy-paste and does not support dynamic agent switching within a session.
Unique: Provides the simplest possible integration path for tools that don't support external configuration — plain-text system prompts that can be copy-pasted. This approach prioritizes transparency and simplicity over automation, making it ideal for users who want to inspect and customize prompts.
vs alternatives: More transparent than automated integrations because users can see the exact prompt being used; simpler to set up than rule-based integrations; works with any tool that accepts system prompts.
+5 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 agency-agents-zh at 46/100. agency-agents-zh leads on quality, while TrendRadar is stronger on adoption and ecosystem.
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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