| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Applies a multi-intensity rule engine (Lite/Full/Ultra modes) that surgically removes linguistic filler—articles, hedging phrases, pleasantries—while preserving code blocks, technical terminology, and safety-critical information. Uses a single-source-of-truth SKILL.md configuration file that defines transformation rules across all host environments (Claude Code, Codex, Gemini CLI), achieving ~75% token reduction without sacrificing technical accuracy through a 'Smart Caveman' principle that protects machine-critical data.
Unique: Implements a three-tier intensity system (Lite/Full/Ultra) with a 'Smart Caveman' principle that differentiates between human-centric filler and machine-critical data, using a declarative SKILL.md single-source-of-truth that synchronizes behavior across Claude Code, Codex, and Gemini CLI without requiring code changes per platform. This contrasts with generic prompt-injection approaches by maintaining explicit whitelist/blacklist rules for technical terms and safety-critical operations.
vs alternatives: Achieves 75% token savings while maintaining 100% technical accuracy through linguistic rule-based filtering, whereas generic prompt compression (e.g., 'be concise') often loses technical precision or requires manual prompt engineering per use case.
Distributes caveman as a portable 'Skill' artifact across heterogeneous AI agent platforms (Claude Code plugin marketplace, Codex CLI, Gemini CLI) using a unified SKILL.md configuration format. Provides platform-specific installation hooks (shell scripts for macOS/Linux/WSL, PowerShell for Windows) that auto-merge configuration into host environment settings (~/.claude/settings.json, Codex config, etc.), enabling single-source-of-truth behavior across all platforms without duplicating rule definitions.
Unique: Uses a declarative SKILL.md single-source-of-truth that auto-syncs across Claude Code, Codex, and Gemini CLI via GitHub Actions CI/CD pipeline, with platform-specific installation hooks (shell/PowerShell scripts) that auto-merge into native environment configs. This eliminates the need for separate plugin codebases per platform while maintaining platform-native integration patterns.
vs alternatives: Simpler distribution than maintaining separate plugins for each platform (e.g., VS Code extension + CLI tool + web app) because SKILL.md defines behavior once and CI/CD handles platform-specific packaging; faster than manual installation because hooks auto-configure environment settings.
Exposes three discrete compression intensity levels (Lite, Full, Ultra) that users can toggle per session, each applying progressively aggressive linguistic transformation rules. Lite mode removes only obvious filler (articles, some hedging); Full mode aggressively compresses prose while preserving code and technical terms; Ultra mode maximizes compression by removing even more linguistic scaffolding. Implementation uses a rule registry in SKILL.md that maps intensity levels to specific transformation patterns, allowing users to trade off readability vs. token savings without code changes.
Unique: Implements three discrete intensity levels (Lite/Full/Ultra) as first-class configuration options in SKILL.md, allowing users to toggle compression aggressiveness per session without code changes. Each level maps to a specific rule subset, enabling progressive compression that trades readability for token savings in a predictable, testable manner.
vs alternatives: More granular than binary 'on/off' compression (e.g., generic prompt compression) because users can tune intensity to their specific task; more predictable than adaptive compression because rules are explicit and intensity levels are well-defined.
Implements a whitelist-based protection mechanism that exempts code blocks (markdown fences), technical terminology (e.g., useMemo, shallow comparison), and safety-critical operations (e.g., rm -rf) from compression rules. Uses pattern matching and AST-aware detection to identify protected regions, ensuring that compression never degrades technical accuracy or introduces ambiguity in destructive commands. This 'Smart Caveman' principle is enforced via explicit rules in SKILL.md that define protected patterns and categories.
Unique: Implements a 'Smart Caveman' principle via explicit whitelist rules in SKILL.md that protect code blocks (markdown fences), technical terminology, and safety-critical operations from compression. This is more sophisticated than naive compression because it uses pattern matching and category-based rules to distinguish between human-centric filler (safe to compress) and machine-critical data (must preserve).
vs alternatives: Guarantees 100% technical accuracy while achieving 75% token savings, whereas generic compression tools often sacrifice accuracy for brevity; more maintainable than hardcoded protection logic because rules are declarative in SKILL.md.
Provides a Python-based benchmarking suite (benchmarks/run.py) that measures caveman's token savings, compression ratios, generation speed, and technical accuracy across multiple intensity levels and test prompts. Generates quantitative metrics (e.g., ~75% token savings, ~46% input compression, ~3x speed increase) and supports custom benchmark prompts. Results are published to GitHub Pages documentation, enabling transparent performance tracking and user-facing proof of efficiency gains.
Unique: Provides a reproducible, open-source benchmarking suite (benchmarks/run.py) that measures token savings, speed, and accuracy across intensity levels, with results published to GitHub Pages. This enables transparent, user-verifiable performance claims rather than marketing assertions.
vs alternatives: More rigorous than anecdotal claims because benchmarks are reproducible and published; more comprehensive than single-metric reporting because it measures tokens, speed, and accuracy simultaneously.
Automatically generates and publishes comprehensive documentation to GitHub Pages via CI/CD pipeline, including installation guides, intensity level explanations, linguistic rules, trigger/command reference, plugin architecture details, and benchmark results. Documentation is derived from SKILL.md and repository metadata, ensuring single-source-of-truth consistency. Provides both human-readable guides and technical deep-dives for developers integrating caveman into custom workflows.
Unique: Implements automated documentation generation from SKILL.md and repository metadata via GitHub Actions, publishing to GitHub Pages with single-source-of-truth consistency. This eliminates manual wiki maintenance and ensures documentation stays synchronized with code changes.
vs alternatives: More maintainable than manually-edited wikis because documentation is auto-generated from source; more discoverable than README-only documentation because it provides structured, searchable pages.
Provides explicit command-based activation mechanism (e.g., /caveman, /caveman lite, /caveman full, /caveman ultra) that users invoke to enable compression for a specific session. Activation is session-scoped (not persistent across Claude Code instances) and can be toggled on/off mid-conversation. Implementation uses Claude Code's command/trigger system to intercept user input and apply caveman rules to model output, without requiring permanent configuration changes.
Unique: Implements session-scoped, command-based activation (/caveman, /caveman lite, /caveman full, /caveman ultra) that allows users to toggle compression on-demand without persistent configuration. This provides explicit user control and enables A/B testing within single conversations.
vs alternatives: More flexible than always-on compression because users can selectively enable caveman; more discoverable than configuration-file-based activation because commands are explicit and visible in chat history.
Implements a declarative rule registry in SKILL.md that defines linguistic transformation patterns (e.g., 'The reason is' → 'Reason:', delete articles 'a'/'an'/'the', remove hedging phrases). Rules are organized by category (grammar, articles, filler, safety-critical) and intensity level (Lite/Full/Ultra), enabling pattern-based text transformation without hardcoded logic. Uses regex or string-matching patterns to identify and replace linguistic elements, with explicit exceptions for code blocks and technical terms.
Unique: Implements a declarative rule registry in SKILL.md that defines linguistic transformation patterns organized by category and intensity level, enabling non-engineers to understand, audit, and customize compression rules without code changes. This is more transparent than hardcoded compression logic.
vs alternatives: More maintainable than hardcoded transformation logic because rules are declarative and version-controlled; more auditable than black-box compression because rules are explicit and human-readable.
+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 caveman at 40/100.
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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