This repository serves as the central hub for aggregating, processing, and synthesizing knowledge for the elizaOS project. It employs a series of automated workflows and scripts to gather data from various sources, generate insights using LLMs, and disseminate information through different channels.

All major directories now contain detailed README documentation! Each README provides:

• Technical specifications and data formats
• Integration patterns and data flow diagrams
• Usage examples with practical bash commands
• Quality metrics and performance characteristics
• Troubleshooting guides and best practices

Directory Documentation:

• Core Processing: Data aggregation, fact extraction, strategic analysis
• AI Intelligence: Multi-source news aggregation (1,640+ files)
• GitHub Analytics: Development activity tracking (900+ files)
• Community Data: Discord AI news collection (25,000+ lines)
• Generated Content: LLM-powered content creation and backup
• Visual Assets: Automated poster generation (rolling 7-day archive)
• Character System: AI-powered character illustration pipeline
• Episode Database: Business pitch show scripts (31 episodes)
• Historical Archive: 7+ months of preserved data (1,813+ files)

Quick Links to Explore Data:

Explore

• Browse all content via GitHub Pages root: https://elizaos.github.io/knowledge/
• Eliza Daily HackMD Book: https://hackmd.io/@elizaos/book

Updated daily (Data for these links is typically refreshed by 02:30 UTC each day.)

• Latest aggregated data: https://elizaos.github.io/knowledge/the-council/aggregated/daily.json
• Latest council briefing: https://elizaos.github.io/knowledge/the-council/council_briefing/daily.json
• Latest ElizaOS AI news summary: https://elizaos.github.io/knowledge/ai-news/elizaos/json/daily.json
• Latest extracted facts: https://elizaos.github.io/knowledge/the-council/facts/daily.json
• Contributor profiles: https://elizaos.github.io/knowledge/github/contributors/
• Eliza.how docs (llms-full.txt): https://eliza.how/llms-full.txt

RSS Feeds

• Daily Intelligence Feed: https://elizaos.github.io/knowledge/rss/feed.xml
• Council Briefings Feed: https://elizaos.github.io/knowledge/rss/council.xml

For Developers & Researchers:

1. Start with the-council/README.md - Understand the core data processing pipeline
2. Explore ai-news/README.md - Learn about multi-source intelligence gathering
3. Review scripts/README.md - See automation tools and deployment guides
4. Check CLAUDE.md - Development guidelines and system architecture

For Data Analysis:

• github/README.md - 900+ files of development activity analytics
• daily-silk/README.md - 25,000+ lines of community AI news
• archive/README.md - 7+ months of historical data (1,813+ files)

For Content Creation:

• hackmd/README.md - LLM-generated content creation and management
• posters/README.md - Automated visual content generation (448+ images)

This repository includes a Model Context Protocol (MCP) server that exposes the knowledge base to AI assistants like Claude. See mcp-server/README.md for full documentation.

cd mcp-server && npm install && npm run build

Add to your Claude Desktop MCP settings:

{
  "mcpServers": {
    "elizaos-knowledge": {
      "command": "node",
      "args": ["/path/to/knowledge/mcp-server/dist/index.js"],
      "env": {
        "KNOWLEDGE_BASE_PATH": "/path/to/knowledge"
      }
    }
  }
}

Once connected, you can ask Claude:

• "What happened in the ElizaOS community today?"
• "Search for discussions about MCP integration"
• "Get the council briefing from yesterday"

The system follows a structured pipeline to transform raw data into actionable intelligence:

• 01:00 - External Data Ingestion (.github/workflows/sync.yml)
• 01:15 - Context Aggregation (.github/workflows/aggregate-daily-sources.yml)
• 01:35 - Daily Fact Extraction + RSS Generation (.github/workflows/extract_daily_facts.yml)
• 01:50 - Council Briefing Generation + RSS Update (.github/workflows/generate-council-briefing.yml)
• 02:30 - HackMD Note Updates (.github/workflows/update_hackmd_notes.yml)
• 04:00 - Poster Generation (.github/workflows/generate-posters.yml)
• 04:30 - Discord Briefing (.github/workflows/daily_discord_briefing.yml)

• Monthly (1st of each month @ 03:00 UTC) - Council retrospective episode analyzing the previous month
• Quarterly (1st of Jan/Apr/Jul/Oct @ 04:00 UTC) - Strategic summary across 3 months
• Annual (Manual trigger) - Comprehensive year-in-review summary

Retrospectives output to the-council/retros/ and the-council/summaries/.

1. External Data Ingestion (.github/workflows/sync.yml): (Runs at 01:00 UTC)

   • This workflow runs daily to synchronize data from external repositories and sources. This includes documentation from elizaOS/eliza and madjin/daily-silk, GitHub activity logs from elizaos/elizaos.github.io, AI news from M3-org/ai-news, and episode data from m3-org/clanktank and m3-org/the-council.
   • Raw synced data is stored in directories like docs/, daily-silk/, github/, ai-news/, clanktank/episodes/, and the-council/episodes/.

2. Daily Fact Extraction (.github/workflows/extract_daily_facts.yml): (Runs at 01:15 UTC)

   • This workflow runs scripts/extract-facts.py daily after data synchronization.
   • scripts/extract-facts.py takes the daily aggregated data (from the previous day, or requires aggregate-daily-sources.yml to have run if processing current day's live data, though its schedule suggests it processes already aggregated data from a prior step if available, or just focuses on what aggregate-sources.py can provide it if it were to be run by this workflow directly) and uses an LLM with a specialized prompt to distill significant information.
   • Outputs are structured JSON facts to the-council/facts/YYYY-MM-DD.json and a Markdown version to hackmd/facts/YYYY-MM-DD.md.
   • A permalink the-council/facts/daily.json is also created.

3. Daily Context Aggregation (.github/workflows/aggregate-daily-sources.yml): (Runs at 01:30 UTC)

   • This workflow runs scripts/aggregate-sources.py daily.
   • scripts/aggregate-sources.py consolidates data from the synced external sources (e.g., ai-news/, github/summaries/) and internal structured data into a comprehensive daily JSON file: the-council/aggregated/YYYY-MM-DD.json.
   • A permalink the-council/aggregated/daily.json is created, pointing to the latest daily aggregated file.

4. Council Briefing Generation (.github/workflows/generate-council-briefing.yml): (Runs at 02:00 UTC)

   • Triggered daily after context aggregation, this workflow runs scripts/generate_council_context.py.
   • This script takes the-council/aggregated/daily.json as input and uses an LLM (via OpenRouter) with strategic prompts (e.g., scripts/prompts/strategy/north-star.txt) to produce a high-level strategic briefing.
   • The output is saved as the-council/council_briefing/YYYY-MM-DD.json, with a permalink the-council/council_briefing/daily.json.

5. HackMD Note Generation & Backup (.github/workflows/update_hackmd_notes.yml): (Runs at 02:30 UTC)

   • This workflow runs daily to manage topical insights on HackMD.
   • It first executes scripts/create-hackmd.py which ensures HackMD notes exist for prompts and updates book.json.
   • Then, it runs scripts/update-hackmd.py which uses the-council/aggregated/daily.json as context to generate content for each prompt, update HackMD notes, and save local backups.
   • Changes to book.json, hackmd/**/*.md, and hackmd/**/*.json are committed.

6. Enhanced Poster Generation (.github/workflows/generate-posters.yml): (Runs at 04:00 UTC)

   • This workflow generates visual poster content using the enhanced scripts/posters-enhanced.sh script.
   • Features multiple rendering engines (wkhtmltoimage, Chromium, ImageMagick) with robust fallback handling.
   • Creates date-stamped posters (YYYY-MM-DD_category.png) to avoid Discord caching issues.
   • Generates 16+ poster categories daily with ElizaOS branding and responsive layouts.
   • All posters are hosted on GitHub Pages for reliable distribution.

7. Daily Discord Briefing (.github/workflows/daily_discord_briefing.yml): (Runs at 04:30 UTC)

   • This workflow runs scripts/webhook.py daily after all data processing and poster generation are complete.
   • Uses yesterday's date-stamped poster to avoid GitHub Pages deployment lag and Discord caching issues.
   • Includes automatic poster cleanup and sends formatted briefings with LLM-generated summaries.
   • Integrates rich Discord embeds with poster images hosted on GitHub Pages.
   • Requires OPENROUTER_API_KEY and DISCORD_BOT_TOKEN secrets for LLM summarization and Discord posting.

Each major directory contains comprehensive documentation. Click the links below to explore detailed information about each component:

• the-council/: Central data processing hub containing daily aggregated data, strategic council briefings, and extracted facts
  • aggregated/: Daily raw aggregated data (YYYY-MM-DD.json) from all sources
  • council_briefing/: Strategic council briefings with high-level analysis
  • facts/: Daily extracted facts and insights with source tracing
  • episodes/: Episode data from strategic discussions (including monthly retros)
  • retros/: Monthly retrospective analyses
  • summaries/: Quarterly and annual strategic summaries

• ai-news/: Multi-source AI intelligence (1,640+ files) from elizaOS and Hyperfy ecosystems
• github/: GitHub activity analytics (900+ files) with daily/weekly/monthly summaries and statistics
• daily-silk/: Daily AI news from Discord community (167+ files, 25,000+ lines)
• docs/: Technical documentation synced from elizaOS/eliza repository

• hackmd/: LLM-generated content backups organized by category with HackMD synchronization
• posters/: Visual content generation for Discord and social sharing (rolling 7-day archive)

• clanktank/: Episode database for Clank Tank business pitch show (31 complete episode scripts)
• archive/: Historical data repository (1,813+ files) preserving 7+ months of elizaOS ecosystem evolution

• .github/workflows/: GitHub Actions workflow configurations for daily automation pipeline
• scripts/: All automation scripts (Python primary, shell secondary) with comprehensive tooling
  • prompts/: LLM interaction templates organized by category (comms, dev, strategy)
• book.json: HackMD state management file mapping prompts to note IDs and update strategies

• aggregate-sources.py: The main data aggregation engine, creating the-council/aggregated/YYYY-MM-DD.json.
• extract-facts.py: Performs deep analysis on aggregated data, outputs structured facts to the-council/facts/YYYY-MM-DD.json and markdown to hackmd/facts/YYYY-MM-DD.md.
• generate-council-context.py: Processes aggregated data to create strategic council briefings in the-council/council_briefing/YYYY-MM-DD.json.
• generate-monthly-retro.py: Generates monthly "State of ElizaOS" council episodes.
• generate-quarterly-summary.py: Generates quarterly/annual strategic summaries.
• generate-rss.py: Generates RSS feeds for facts and council briefings.

• discord/webhook.py: Sends daily facts briefing to Discord with LLM summarization and poster images. Also handles poster cleanup (keeps last 7 days).
• discord/bot.py: Council briefing Discord bot.
• hackmd/create.py: Initializes HackMD notes for prompts, manages book.json.
• hackmd/update.py: Generates daily content for HackMD notes using prompts and aggregated data.

A complete system for generating character-driven visual content:

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  ANALYZE    │ ──▶ │  GENERATE   │ ──▶ │ ILLUSTRATE  │
│  analyze.py │     │ generate.py │     │illustrate.py│
│             │     │             │     │             │
│ Images →    │     │ Manifest →  │     │ Ref sheet + │
│ manifest    │     │ ref sheet   │     │ story →     │
│             │     │             │     │ illustration│
└─────────────┘     └─────────────┘     └─────────────┘

Available Characters: eliza, marc, peepo, spartan, shaw

See scripts/posters/README.md for detailed usage.

This system is designed to be modular and extensible, allowing for the integration of new data sources and processing steps as the project evolves.

The repository is organized into several main directories with comprehensive documentation. Each directory README provides detailed technical specifications and usage examples:

elizaOS Knowledge Aggregation System
├── 📁 Core Processing Hub
│   └── the-council/            # 🔗 Central data processing (see README.md)
│       ├── aggregated/         #   Daily raw data aggregation (YYYY-MM-DD.json)
│       ├── council_briefing/   #   Strategic analysis & briefings  
│       ├── facts/              #   LLM-extracted insights & intelligence
│       └── episodes/           #   Strategic discussion archives
│
├── 📁 Data Sources & Intelligence  
│   ├── ai-news/                # 🔗 Multi-source AI intelligence (see README.md)
│   │   ├── elizaos/            #   ElizaOS ecosystem news (270+ files)
│   │   │   ├── dev/            #   Developer-focused content
│   │   │   ├── discord/        #   Community discussions  
│   │   │   └── json/ + md/     #   Daily summaries & reports
│   │   └── hyperfy/            #   VR/Web3 platform developments
│   ├── github/                 # 🔗 GitHub activity analytics (see README.md)
│   │   ├── stats/              #   Quantitative metrics (day/week/month)
│   │   ├── summaries/          #   Human-readable reports (900+ files)
│   │   ├── api/                #   API endpoint data
│   │   └── contributors/       #   Contributor profiles & statistics
│   ├── daily-silk/             # 🔗 Discord AI news aggregation (see README.md)
│   └── docs/                   #   Technical docs from elizaOS/eliza
│
├── 📁 Generated Content & Distribution
│   ├── hackmd/                 # 🔗 LLM-generated content backups (see README.md)
│   │   ├── comms/              #   Communication & outreach content
│   │   ├── dev/                #   Development & technical content  
│   │   ├── strategy/           #   Strategic analysis & planning
│   │   └── facts/              #   Daily extracted facts (markdown)
│   └── posters/                # 🔗 Visual content generation (see README.md)
│       └── YYYY-MM-DD_*.png    #   Date-stamped poster images (last 7 days kept)
│
├── 📁 Specialized Content
│   ├── clanktank/              # 🔗 Business pitch show episodes (see README.md)
│   │   └── episodes/           #   31 complete episode scripts (JSON)
│   └── archive/                # 🔗 Historical data preservation (see README.md)
│       ├── daily-elizaos/      #   Legacy daily reports (Oct 2024-Apr 2025)
│       └── elizaos/            #   Community collaboration archives
│
├── 📁 Automation & Configuration
│   ├── .github/workflows/      #   Daily automation pipeline (8 workflows)
│   ├── scripts/                #   Python automation scripts & tooling
│   │   ├── etl/                #   Data pipeline (aggregate, extract, generate)
│   │   ├── integrations/       #   External services (Discord, HackMD)
│   │   ├── posters/            #   Character illustration system
│   │   │   ├── characters/     #   Character assets & manifests
│   │   │   └── config/         #   Style presets & configuration
│   │   └── prompts/            #   LLM interaction templates (comms/dev/strategy)
│   └── book.json               #   HackMD state management configuration
│
└── 📄 Documentation
    ├── README.md               #   This comprehensive system overview
    └── CLAUDE.md               #   Development guidelines & architecture notes
    
📊 System Scale: 5,000+ files • 50+ MB data • Daily automation • 7+ months history
🔗 All major directories contain detailed README.md documentation

Archive of Discord discussions from various channels related to AI development and communities. This section is being deprecated in favor of more structured data sources.

Technical documentation from the ElizaOS/eliza repository, specifically from its packages/docs folder. These files contain guides, tutorials, API references, and technical specifications for the ElizaOS system.

Activity logs from ElizaOS/elizaos.github.io (_data branch), organized by day, week, and month. This provides a chronological view of development activities and changes.

Synced directories:

• github/stats/ - Quantitative metrics (day/week/month JSON files)
• github/summaries/ - Human-readable activity reports
• github/api/ - API endpoint data
• github/contributors/ - Contributor profiles and statistics

Tip: here's a command to turn the JSON stats files into a single text file:

Stats

jq -r '                     
  "\n=== \(.interval.intervalStart) ===",
  .overview,
  "\nTop Issues:",
  (.topIssues[]? | "- #\(.number) [\(.state)] \(.title) by \(.author) (\(.commentCount) comments)"),
  "\nTop PRs:",
  (.topPRs[]? | "- #\(.number) \(.title) by \(.author) (\(.additions) +, \(.deletions) -)"),
  "\nCompleted Items:",
  (.completedItems[]? | "- \(.type): \(.title) (#\(.prNumber))"),
  "\nTop Contributors:",
  ([.topContributors[]? | "\(.username) (score: \(.totalScore | floor))"] | .[:3] | .[])
' github/stats/month/stats_2025-04*.json > monthly-github-stats.txt

User summaries

jq -r '                       
  map(select(.date | startswith("2025"))) |
  group_by(.date)[] |
  ("=== " + (.[0].date) + " ==="),
  (.[] | .summary, "---"),
  ""
' user_summaries.json > user_summaries.txt

Daily AI news summaries generated by M3-org/ai-news, an AI-powered news aggregation platform that collects, analyzes, and summarizes information from multiple sources in real-time. The news data is stored in the gh-pages branch of the original repository.

Daily AI news collected from a Discord channel using SILK and discord.py. The data is automatically fetched, processed, and stored in markdown format, with each file representing a day's worth of AI news and updates. The content is organized chronologically and includes timestamps for each entry. The data is collected daily and provides a comprehensive view of AI developments and announcements.

Daily summaries and discussions related to AI, specifically from the ElizaOS and Hyperfy communities, sourced from the M3-org/ai-news repository (gh-pages branch). This includes:

• ai-news/elizaos/: Summaries and logs from ElizaOS related channels.
• ai-news/hyperfy/: Summaries and logs from Hyperfy related channels.

Structured episode content from M3-org repositories containing rich narrative and analytical content:

Episodes from the m3-org/clanktank repository. Contains JSON files with episode data covering various topics in AI, crypto, and technology. Each episode includes structured content with topics, discussions, and insights from the clanktank community.

Episodes from the m3-org/the-council repository. Contains JSON files with strategic discussions and analysis from the council. This data is synced daily as part of the automated pipeline, providing ongoing strategic insights and community discussions.

The scripts/ directory contains Python scripts used for automating content generation and updates.

• scripts/prompts/: Contains prompt templates categorized into subdirectories (comms, dev, strategy). These templates are used by scripts/update-hackmd.py along with daily context data to generate content for specific HackMD notes.
• scripts/create-hackmd.py: Creates new HackMD notes for prompts found in scripts/prompts/ that are not already listed in book.json. It populates book.json with the note ID and an "overwrite" update strategy.
• scripts/update-hackmd.py: Reads the latest daily context data, generates content for each prompt (defined in book.json) using an LLM (via OpenRouter). It updates the corresponding HackMD note by overwriting its title (with the current date) and its entire content (placing the prompt details from the local file in <details> tags, followed by the LLM-generated text). It also updates the main Book Index note on HackMD and saves local backups.

The hackmd/ directory stores local backups of the content generated by scripts/update-hackmd.py. The structure mirrors the scripts/prompts/ categories, with each prompt having its own subdirectory containing dated markdown files (e.g., hackmd/comms/discord-announcement/2025-05-05.md) and optionally JSON files.

Documentation from the ElizaOS package ecosystem, which includes a collection of adapters, clients, and plugins that extend the functionality of the ElizaOS platform. This directory contains detailed information about each package's features, configuration, and integration methods.

Information about ElizaOS partners and integrations, including details about official partnerships, supported platforms, and integration capabilities. This documentation helps users understand the broader ecosystem of services and platforms that work with ElizaOS.

This repository is designed to be used as a knowledge source for RAG systems. The markdown files can be ingested into vector databases or other retrieval systems to provide context for AI agents.

1. Clone this repository to your local machine or server
2. Use the files as a corpus for training or fine-tuning AI models
3. Index the content for retrieval in RAG systems
4. Reference specific sections in your AI prompts for domain-specific knowledge

To add a new source to the knowledge repository:

1. Create a dedicated directory for the source
2. Ensure all files are in markdown (.md) format when possible
3. Update this README with information about the new source
4. Create a GitHub action to keep the source updated (see below)

This repository uses GitHub Actions to automatically update content from various sources. To contribute a new action:

1. Create a new workflow file in .github/workflows/
2. Configure the action to fetch and format data from the source
3. Set an appropriate schedule for updates
4. Test the action to ensure it correctly updates the repository

This workflow runs weekly on Fridays at 18:00 UTC and can be triggered manually. It executes the following steps:

1. Install Dependencies: Sets up Python, Node.js, and installs necessary packages (requests, @hackmd/hackmd-cli).
2. Create Notes: Runs scripts/create-hackmd.py to check for new prompt files in scripts/prompts/ and creates corresponding notes on HackMD if they don't exist in book.json. Requires HMD_API_ACCESS_TOKEN secret.
3. Update Notes: Runs scripts/update-hackmd.py to generate content using the latest daily data and prompts, then updates the HackMD notes and the main book index. Requires HMD_API_ACCESS_TOKEN and OPENROUTER_API_KEY secrets.
4. Commit Changes: Commits any modifications to book.json and the generated markdown files in the hackmd/ directory back to the repository.

To build the most reliable, developer-friendly open-source AI agent framework and cloud platform—enabling builders worldwide to deploy autonomous agents that work seamlessly across chains and platforms. We create infrastructure where agents and humans collaborate, forming the foundation for a decentralized AI economy that accelerates the path toward beneficial AGI.

1. Execution Excellence - Reliability and seamless UX over feature quantity
2. Developer First - Great DX attracts builders; builders create ecosystem value
3. Open & Composable - Multi-agent systems that interoperate across platforms
4. Trust Through Shipping - Build community confidence through consistent delivery

• ElizaOS Framework (v1.6.x) - Core TypeScript toolkit for building persistent, interoperable agents
• ElizaOS Cloud - Managed deployment platform with integrated storage and cross-chain capabilities
• Flagship Agents - Reference implementations demonstrating platform capabilities
• Cross-Chain Infrastructure - Native support for multi-chain agent operations

ElizaOS is an open-source "operating system for AI agents" aimed at decentralizing AI development. Built on three pillars:

1. The Eliza Framework - TypeScript toolkit for persistent agents
2. AI-Enhanced Governance - Building toward autonomous DAOs
3. Eliza Labs - R&D driving cloud, cross-chain, and multi-agent capabilities

The native token coordinates the ecosystem. The vision is an intelligent internet built on open protocols and collaboration.

This repository addresses the challenge of information scattered across platforms (Discord, GitHub, X). It uses AI agents as "bridges" to collect, wrangle (summarize/tag), and distribute information in various formats (JSON, MD, RSS, dashboards, council episodes). Documentation is treated as a first-class citizen to empower AI assistants and streamline community operations.