Transforming GitHub issues into contributor intelligence using AI + Workflow Orchestration
Open source projects are growing faster than ever.
Every day repositories receive:
- Bug reports
- Feature requests
- Contributor discussions
- Engineering questions
- Documentation improvements
- Infrastructure problems
But there’s a major challenge:
Maintainers are overwhelmed.
Important issues get buried.
Contributors struggle to find meaningful tasks.
Stale issues pile up.
Project health slowly declines.
So I built:
OSSI — Open Source Signal Intelligence System
An AI-powered orchestration platform built using Kestra that transforms GitHub repositories into actionable contributor intelligence.
🔗 GitHub Repository:
https://github.com/Mohit5Upadhyay/ossi-intel-orchestrator
🧠 What is OSSI?
OSSI stands for:
Open Source Signal Intelligence System
It’s an autonomous workflow orchestration platform that:
✅ Monitors GitHub repositories
✅ Fetches live GitHub issues
✅ Detects stale issues
✅ Uses AI to analyze engineering complexity
✅ Prioritizes issues automatically
✅ Recommends contributor actions
✅ Generates intelligence reports
✅ Sends automated engineering summaries via email
✅ Runs continuously on schedules using Kestra
Instead of manually reading hundreds of issues, OSSI creates an intelligent engineering layer over open-source repositories.
🤔 Why Does OSSI Matter?
Open source maintainers deal with a serious scaling problem.
As repositories grow:
- Issue backlogs explode
- Contributors become confused
- Duplicate issues increase
- Stale tickets accumulate
- Prioritization becomes difficult
And contributors face problems too:
Contributors struggle with:
- Finding beginner-friendly issues
- Understanding issue complexity
- Knowing project priorities
- Discovering impactful tasks
- Understanding technical context
OSSI solves this by turning repositories into structured engineering intelligence systems.
⚡ What OSSI Actually Does
The workflow continuously scans repositories and transforms raw GitHub data into actionable insights.
Example Intelligence Generated
OSSI automatically identifies:
- High-priority issues
- Beginner-friendly tasks
- Advanced engineering problems
- Potential stale issues
- Contributor recommendations
- Root cause analysis
- Suggested implementation steps
Example AI-generated output:
Priority: High
Difficulty: Intermediate
Good First Issue: Yes
Root Cause:
Missing validation layer causing inconsistent API responses.
Quick Fix Approach:
1. Add schema validation
2. Implement middleware checks
3. Add automated tests
This turns raw GitHub issues into contributor-ready engineering tasks.
🚀 Why Kestra Was the Perfect Choice
This entire system is powered by Kestra.
And honestly — Kestra completely changed how I think about automation.
Most automation tools feel like:
- Task runners
- Cron jobs
- Simple scripting systems
But Kestra feels like:
- Infrastructure orchestration
- Workflow operating systems
- AI pipeline orchestration
- Distributed automation architecture
🔥 What Makes Kestra Powerful
1️⃣ Everything is Declarative
The entire orchestration pipeline is written in YAML.
2️⃣ Built-in Scheduling
OSSI runs every 6 hours automatically.
triggers:
- id: scheduled_ossi_scan
type: io.kestra.plugin.core.trigger.Schedule
cron: "0 */6 * * *"
timezone: "Asia/Kolkata"
No external schedulers needed.
3️⃣ Parallel Processing
OSSI processes repositories dynamically using ForEach.
- id: process_repositories
type: io.kestra.plugin.core.flow.ForEach
values: "{{ inputs.repositories }}"
This allows multi-repository intelligence generation.
4️⃣ Native API Integrations
Kestra makes API orchestration incredibly easy.
Example GitHub issue fetching:
- id: fetch_open_issues
type: io.kestra.plugin.core.http.Request
method: GET
uri: "{{ vars.github_api }}?q=repo:{{ taskrun.value }}+is:issue+is:open"
headers:
Authorization: "Bearer {{ inputs.github_pat }}"
This is extremely clean compared to building everything manually.
5️⃣ AI Workflow Orchestration
One of the most powerful parts:
Kestra orchestrates AI systems beautifully.
OSSI sends repository issue data into AI models for:
- Engineering analysis
- Contributor recommendations
- Priority ranking
- Root cause reasoning
- Issue classification
This is where orchestration becomes much more than automation.
🏗️ OSSI Workflow Architecture
Here’s the full orchestration pipeline:
graph TD
A[Schedule Trigger] --> B[Process Repositories]
B --> C[Fetch GitHub Issues]
C --> D[Process Issue Data]
D --> E[AI Contributor Analysis]
E --> F[Generate Intelligence Report]
F --> G[Send Email Report]
G --> H[Workflow Completed]
🔍 Deep Dive Into the Workflow
1️⃣ Workflow Trigger
The workflow starts automatically every 6 hours.
triggers:
- id: scheduled_ossi_scan
type: io.kestra.plugin.core.trigger.Schedule
cron: "0 */6 * * *"
timezone: "Asia/Kolkata"
This transforms OSSI into a continuously running intelligence system.
2️⃣ Processing Multiple Repositories
OSSI supports multiple repositories dynamically.
inputs:
- id: repositories
type: ARRAY
itemType: STRING
Example repositories:
defaults:
- "kestra-io/kestra"
- "open-metadata/OpenMetadata"
3️⃣ GitHub Issue Intelligence
The workflow fetches live issues directly from GitHub APIs.
- id: fetch_open_issues
type: io.kestra.plugin.core.http.Request
method: GET
uri: "{{ vars.github_api }}?q=repo:{{ taskrun.value }}+is:issue+is:open"
This creates a real-time engineering data stream.
4️⃣ Data Processing Using Shell + jq
After fetching issues, OSSI processes repository data.
commands:
- |
cat repo_issues.json | jq -r '
.items[]
| "
Issue:
#\(.number)
Title:
\(.title)
"
'
This stage transforms raw API responses into structured engineering summaries.
5️⃣ Stale Issue Detection
OSSI automatically identifies neglected issues.
select(.comments < 2)
This helps maintainers:
- Reduce backlog clutter
- Improve issue hygiene
- Re-engage contributors
Small automation.
Massive operational value.
6️⃣ AI Contributor Analysis
This is the brain of OSSI.
The workflow sends issue summaries into an AI model.
- id: ai_contributor_analysis
type: io.kestra.plugin.core.http.Request
The AI then generates:
- Contributor recommendations
- Priority analysis
- Root cause insights
- Engineering reasoning
- Suggested implementation steps
This turns GitHub into an intelligent engineering platform.
7️⃣ 📧 SMTP Email Configuration
After generating intelligence reports, OSSI automatically delivers them using SMTP email orchestration.
Kestra makes email automation extremely clean.
Workflow email task:
- id: contributor_intelligence_email
type: io.kestra.plugin.email.MailSend
host: smtp.gmail.com
port: 465
username: "YOUR_USER_EMAIL_HERE"
password: "YOUR_APP_PASSWORD_HERE"
from: "YOUR_USER_EMAIL_HERE"
to: "RECIPIENT_EMAIL_HERE"
subject: "🚀 OSSI Intelligence Report"
transportStrategy: SMTPS
The workflow automatically sends:
AI contributor insights
Engineering summaries
Issue prioritization
Repository intelligence
Stale issue detection reports
directly into your inbox.
8️⃣ 🤖 GitHub Models Configuration
OSSI uses GitHub Models to generate contributor intelligence automatically.
The workflow sends processed GitHub issue summaries into gpt-4o using Kestra's HTTP orchestration capabilities.
Actual workflow configuration:
- id: ai_contributor_analysis
type: io.kestra.plugin.core.http.Request
method: POST
uri: "https://models.inference.ai.azure.com/chat/completions"
headers:
Content-Type: application/json
Authorization: "Bearer {{ inputs.github_pat }}"
Model configuration:
{
"model": "gpt-4o",
"temperature": 0.2,
"top_p": 1.0
}
The AI model performs:
Issue prioritization
Contributor recommendations
Root cause analysis
Engineering impact analysis
Difficulty classification
Beginner issue detection
This transforms OSSI into an autonomous engineering intelligence system instead of just a monitoring workflow.
9️⃣ Automated Email Intelligence Reports
🔐 Gmail SMTP Setup
To enable email delivery:
Enable 2-Factor Authentication on your Google account
Generate a Google App Password:
https://myaccount.google.com/apppasswords
Then replace:
username: "YOUR_USER_EMAIL_HERE"
password: "YOUR_APP_PASSWORD_HERE"
with your actual credentials.
This allows OSSI to autonomously deliver engineering intelligence reports after every workflow execution.
Finally, OSSI sends beautifully structured engineering reports directly to maintainers.
The report contains:
- Repository intelligence
- Contributor insights
- Engineering priorities
- Issue recommendations
- AI-generated analysis
All automatically orchestrated through Kestra.
🖥️ Setting Up OSSI Locally
Now let’s actually run it.
🐳 Step 1 — Run Kestra with Docker
Download the official Kestra Docker Compose file.
Linux/macOS
curl -o docker-compose.yml \
https://raw.githubusercontent.com/kestra-io/kestra/develop/docker-compose.yml
Windows
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/kestra-io/kestra/develop/docker-compose.yml" -OutFile "docker-compose.yml"
Once the file is downloaded, start Kestra using:
docker compose up -d
🧩 Step 2 — Import the OSSI Workflow
Clone the repository:
git clone https://github.com/Mohit5Upadhyay/ossi-intel-orchestrator
Open Kestra UI.
Go to:
- Flows
- Create Flow
- Paste YAML workflow
Save the workflow.
Done.
🔐 Step 3 — Configure GitHub Token & Email Config
Generate GitHub PAT:
https://github.com/settings/tokens
Required permissions:
- repo
Then configure:
github_patrecipient_email
inside workflow inputs.
▶️ Step 4 — Execute Workflow
Run the workflow manually OR wait for the schedule trigger.
Kestra will now:
- Fetch issues
- Analyze repositories
- Generate intelligence
- Send reports
autonomously.
📊 Kestra’s Visualization is Incredible
One thing I absolutely loved:
Workflow Topology View
Kestra visualizes:
- Task relationships
- Dependencies
- Execution structure
- Processing stages
This becomes extremely useful for complex orchestration systems.
⏱️ Live Execution Tracking
Kestra also provides:
- Gantt execution charts
- Runtime visibility
- Retry monitoring
- Failure tracking
- Execution logs
This makes debugging workflows much easier.
🧠 What I Learned Building OSSI
This project taught me something important:
AI becomes MUCH more powerful when combined with orchestration.
Without orchestration:
- AI is isolated
With orchestration:
- AI becomes infrastructure
That realization completely changed my engineering mindset.
🚀 Why Developers Should Learn Workflow Orchestration
If you're interested in:
- AI Engineering
- DevOps
- Automation
- ETL Systems
- AI Agents
- Distributed Systems
- Event-driven architectures
- Data pipelines
then orchestration is a critical skill.
And Kestra is one of the best tools I’ve used for learning it.
💡 Projects You Can Build Using Kestra
After building OSSI, I realized Kestra can orchestrate almost anything.
Some ideas:
- AI code review systems
- Autonomous CI/CD intelligence
- DevOps monitoring pipelines
- AI documentation generators
- Security analysis workflows
- Multi-agent AI systems
Once you start thinking in orchestration pipelines —
you begin engineering systems differently.
🔥 Why OSSI Matters for Open Source
OSSI is not just automation.
It’s:
- Contributor enablement
- Engineering intelligence
- Repository analytics
- AI-powered prioritization
- Open source acceleration
It helps:
- Maintainers scale better
- Contributors onboard faster
- Communities stay healthier
- Engineering efforts become focused
And I think systems like this will become increasingly important for the future of open source.
🛠️ Technologies Used
Core Stack
- Kestra
- GitHub API
- GitHub Models
- YAML
- Shell Scripting
- jq
- SMTP Automation
Concepts
- Workflow orchestration
- AI pipelines
- ETL processing
- Contributor intelligence
- Scheduled automation
- Engineering analytics
🔗 Project Links
GitHub Repository
https://github.com/Mohit5Upadhyay/ossi-intel-orchestrator
Kestra
Kestra GitHub
https://github.com/kestra-io/kestra
🎯 Final Thoughts
Before this project, I thought automation meant:
“running scripts automatically”
Now I think of orchestration as:
“building autonomous engineering systems”
OSSI started as a workflow experiment.
But it evolved into:
- AI infrastructure
- contributor intelligence
- engineering automation
- orchestration architecture
And honestly...
This feels like just the beginning of AI-powered workflow systems.
If you’re learning:
- AI Engineering
- Automation
- DevOps
- Workflow Systems
- Open Source Infrastructure
Build orchestration projects.
They teach you how real engineering systems operate.
And Kestra is an incredible place to start.
🚀
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