Block 1 - Sticky Note
- Type / Role
- n8n-nodes-base.stickyNote - stickyNote
- Config choices
- Version 1
Quick overview This workflow runs weekly or on demand via Telegram commands (/report, /issues, /prs, /status) to fetch all your GitHub repositories and events, summarizes the last 7 days using an O...
n8n-nodes-base.stickynote, n8n-nodes-base.scheduletrigger, n8n-nodes-base.code, @n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.telegram, n8n-nodes-base.switch, n8n-nodes-base.httprequest
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Do Thanh Vinh.
Original n8n.io sourceThis workflow runs weekly or on demand via Telegram commands (/report, /issues, /prs, /status) to fetch all your GitHub repositories and events, summarizes the last 7 days using an OpenRouter-hosted Qwen model, and sends a multi-part engineering digest to your Telegram chat. No dashboards, no manual reporting.
/report, /issues, /prs, or /status is received./report generates a full 4-part weekly digest. /issues lists open issues. /prs shows PR status. /status gives a quick health check with emoji indicators.https://openrouter.ai/api/v1 for the Qwen model call. (or use any OpenAI-compatible endpoint)./report to your Telegram bot to test.var myRepos = ['owner/repo1', 'owner/repo2'];items = items.filter(function(item) {return myRepos.indexOf(item.json.full_name) !== -1;});/releases, /contributors). Add the new command logic in the LLM prompt under a new MODE section.||| splitting logic in "Format Telegram Messages" and concatenate all report parts into one message.Perfect for indie hackers, solo developers, open-source maintainers, and small engineering teams who want a lightweight weekly engineering report without expensive analytics tools or Jira setups.
WHY THIS APPROACH
Why not GitHub Insights? GitHub Insights shows raw metrics on a dashboard you have to visit. This workflow creates a human-readable engineering digest delivered directly to your Telegram — you don't need to leave your chat app to know what happened.
Token-efficient by design Repository activity is aggregated and compressed into a stats summary before being sent to the LLM. Only counts, not raw event payloads, reach the model. This reduces token usage and API cost significantly.
No vector DB, no embeddings, no extra subscription The AI model does summarization, not retrieval. Stats are computed in code, structured into a prompt, and turned into readable text. Simple means cheap, reliable, and easy to debug.
Activity, not incidents The health check uses traffic-light emojis to indicate development activity levels — green means actively developing, red means stale. This is an engineering velocity indicator, not an incident monitor.
Human stays in the loop The AI reports and summarizes. It does not close issues, assign people, or make decisions. A human reviews the digest and decides what to act on.
WHAT'S NEXT
Social media auto-post — Repurpose the weekly digest into a LinkedIn or Twitter post with a Telegram approval button before publishing. Obsidian sync — Push each report as a Markdown file to your Obsidian vault via Git. Build a searchable second brain of your project history. Unified weekly report — Aggregate GitHub + Gmail + Google Calendar + WakaTime (coding time) + Notion tasks into a single report. All sources have n8n integrations available. Error knowledge base — Extract bug patterns from issues, append to a JSON knowledge base. Feed into a RAG chatbot for future Q&A. Multi-channel delivery — Send the same report to Telegram + Email + Discord + Slack simultaneously.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
| Workflow | Send weekly GitHub digests to Telegram with Qwen via OpenRouter |
|---|---|
| Complexity | advanced |
| Nodes | 22 |
| Categories | Engineering, AI Summarization |
| Author | Do Thanh Vinh |
| Published | 21 May 2026 |
Use the JSON export at /data/workflows/15898/15898.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
Quick overview This workflow runs weekly or on demand via Telegram commands (/report, /issues, /prs, /status) to fetch all your GitHub repositories and events, summarizes the last 7 days using an O...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.