datadops
DatadOps is an Intelligent advanced Datadog workflows for Claude Code . It transforms raw monitoring data into actionable insights through i
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About
DatadOps is an Intelligent advanced Datadog workflows for Claude Code . It transforms raw monitoring data into actionable insights through intelligent DatadOps automation. This is a lightweight wrapper plugin that adds intelligent workflows on top of the official Datadog MCP server. While the official server provides access to raw Datadog data, this plugin orchestrates that data into complete operational workflows. - Service Health Dashboards - Incident Response Automation - Performance Investigation - Deployment Validation
Details
- Kind
- Plugins
- Topic
- Cloud & DevOps
- Publisher
- ahmidbbc
- Origin
- marketplace
- Category
- ferramentas
- Last push
- 2026-04-16T10:04:10Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
ahmidbbc/datadops/datadops
README
# 📊 DatadOps - Intelligent Monitoring Workflows
**Advanced Datadog workflows for Claude Code** - Transforms raw monitoring data into actionable insights through intelligent DatadOps automation.
## 🎬 Demo
https://github.com/user-attachments/assets/d7403866-67db-448b-8fee-f58beeed79fc
## ✨ What is DatadOps?
A **lightweight Claude Code plugin** that bundles the [official Datadog MCP server](https://docs.datadoghq.com/bits_ai/mcp_server/) configuration and adds intelligent workflows on top of it. The official server provides access to raw Datadog data; DatadOps orchestrates that data into complete operational workflows.
### 🆚 Official MCP Server vs DatadOps
| Aspect | Official Datadog MCP | DatadOps |
|--------|---------------------|-------------------|
| **Purpose** | Raw data access | Intelligent workflows |
| **Usage** | `search_datadog_logs`, `get_datadog_metric` | *"Investigate payment service issues"* |
| **Output** | JSON data | Actionable insights + recommendations |
| **Expertise** | Requires Datadog knowledge | Guides non-experts |
| **Scope** | Individual queries | End-to-end scenarios |
## 🚀 Key Features
### 🚨 **Incident Response Automation**
- **Multi-signal correlation**: Automatically correlates logs, metrics, APM traces
- **Timeline reconstruction**: Maps events, deployments, and failures
- **Impact assessment**: Quantifies customer and business impact
- **Actionable remediation**: Specific next steps with confidence levels
### 🔍 **Performance Investigation**
- **Bottleneck identification**: Pinpoints root causes across the stack
- **Resource correlation**: Links performance to infrastructure constraints
- **Optimization recommendations**: Specific improvements with impact estimates
- **Trend analysis**: Historical context and pattern recognition
### 📋 **Deployment Validation**
- **Before/after comparison**: Statistical analysis of deployment impact
- **Regression detection**: Automated identification of performance degradations
- **Rollback recommendations**: Data-driven go/no-go decisions
- **Health scoring**: Quantified deployment success metrics
### 🩺 **Service Health Dashboards**
- **Overall health scoring**: 0-100 health scores with component breakdown
- **Proactive alerting**: Early warning before customer impact
- **Dependency mapping**: Service relationship health analysis
- **Executive reporting**: Business-friendly health summaries
## 🎯 Perfect For
### **SRE/DevOps Teams**
- Faster incident response (30-minute investigations → 5 minutes)
- Standardized troubleshooting procedures
- Automated deployment validation
- Proactive performance optimization
### **Development Teams**
- Performance bottleneck identification without deep Datadog expertise
- Deployment confidence through automated health checks
- Quick service health assessment
- Integration with CI/CD pipelines
### **Management & Product**
- Service health visibility in business terms
- Impact assessment for incidents
- Performance trend reporting
- Resource optimization ROI
## 📊 Usage Examples
### Incident Response
```
User: "Payment service is returning 500 errors. Help me investigate."
DatadOps Response:
🚨 INCIDENT ANALYSIS - Payment Service
SEVERITY: HIGH (Customer Impact Detected)
DURATION: 23 minutes (since 14:37 UTC)
ROOT CAUSE ANALYSIS:
• Database connection pool exhaustion (95% utilization)
• Spike in payment processing requests (+300% vs baseline)
• Payment gateway API latency increased to 2.4s (baseline: 200ms)
IMMEDIATE ACTIONS:
1. Scale payment service to 6 instances (currently 3)
2. Increase DB connection pool from 20→40
3. Enable circuit breaker for payment gateway
IMPACT:
• ~1,200 affected transactions
• Payment success rate: 45% (baseline: 99.2%)
• Estimated revenue impact: $15K/hour
MONITORING:
• payment.success_rate < 95%: Page oncall
• db.connections > 90%: Auto-scale
```
### Performance Investigation
```
User: "Checkout service is slow. What's the bottleneck?"
DatadOps Response:
🔍 PERFORMANCE ANALYSIS - Checkout Service
PERFORMANCE DEGRADATION: 340ms → 1.2s (P95 latency)
BOTTLENECK BREAKDOWN:
1. 🥇 Database queries: 420ms (35% of total)
- Order validation: 180ms (missing index on user_id)
- Inventory check: 240ms (N+1 query pattern)
2. 🥈 External API calls: 380ms (32% of total)
- Payment gateway: 280ms (network latency)
- Inventory service: 100ms (within SLA)
3. 🥉 Application logic: 260ms (22% of total)
- Price calculation: 180ms (inefficient algorithm)
OPTIMIZATION RECOMMENDATIONS:
• HIGH IMPACT: Add index on orders(user_id) → -180ms
• MEDIUM IMPACT: Batch inventory queries → -150ms
• LOW IMPACT: Cache price calculations → -100ms
ESTIMATED IMPROVEMENT: 1.2s → 590ms (51% faster)
```
### Deployment Health Check
```
User: "Validate the checkout-service v2.1.4 deployment."
DatadOps Response:
📋 DEPLOYMENT HEALTH - Checkout Service v2.1.4
OVERALL HEALTH SCORE: 92/100 ✅ HEALTHY
METRICS COMPARISON (30min post vs baseline):
✅ Success Rate: 99.1% → 99.3% (+0.2%)
✅ P95 Latency: 280ms → 260ms (-7%)
✅ Error Rate: 0.8% → 0.5% (-37%)
⚠️ CPU Usage: 45% → 65% (+44%)
PERFORMANCE IMPACT:
• Response times improved across all percentiles
• Memory usage stable (+2%)
• No new error patterns detected
• Database performance unchanged
RECOMMENDATIONS:
✅ Continue deployment - performance improved
⚠️ Monitor CPU usage - approaching 70% threshold
📈 Consider auto-scaling trigger adjustment
NEXT CHECKPOINT: Monitor for 2 hours, validate at high traffic
```
## 🛠 Installation
### 1. Install DatadOps Plugin
```bash
claude plugin install datadops
```
This installs the plugin and its bundled Datadog MCP configuration.
If you install or update the plugin during an active Claude Code session, run `/reload-plugins`.
### 2. Authenticate with Datadog
1. Open Claude Code
2. Type `/mcp`
3. Select `datadog` → Authenticate
4. Complete the OAuth flow in your browser
### 3. Test Installation
```
Ask Claude: "Give me a health overview of production services"
```
Or invoke the skill directly:
```
/datadops:service-health-overview production services
```
[📖 **Detailed Installation Guide**](setup/installation-guide.md)
## 🎮 Skills Overview
| Skill | Trigger Phrases | Time to Resolution |
|-------|----------------|-------------------|
| **incident-response** | "investigate incident", "service down", "production issues" | 2-5 minutes |
| **performance-investigation** | "performance issue", "slow response", "bottleneck" | 3-7 minutes |
| **deployment-health-check** | "validate deployment", "check release" | 1-3 minutes |
| **service-health-overview** | "health check", "service status", "overview" | 30-60 seconds |
## 🏗 Architecture
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Claude Code │◄──►│ DatadOps │◄──►│ Datadog MCP │
│ │ │ Plugin │ │ Server │
│ (User Query) │ │ (Workflows) │ │ (Raw Data) │
└─────────────────┘ └──────────────────┘ └─────────────────┘
▲ ▲
│ │
Orchestrates 15-20 Fetches from
MCP tools into Official Datadog
complete workflows API
```
## 📈 Impact Metrics
**Before DatadOps:**
- 30+ minutes per incident investigation
- Manual correlation across multiple Datadog pages
- Inconsistent troubleshooting approaches
- High expertise barrier for non-SRE teams
**After DatadOps:**
- < 5 minutes for comprehensive incident analysis
- Automated multi-signal correlation
- Standardized investigation workflows
- Accessible to all team members
## 🤝 Contributing
### Adding New Skills
1. **Identify workflow gap**: What manual process takes > 10 minutes?
2. **Map MCP tools needed**: Which official Datadog MCP tools are required?
3. **Define success criteria**: What constitutes a successful workflow outcome?
4. **Create skill template**: Follow existing skill structure
5. **Add test cases**: Include realistic scenarios and expected outcomes
### Skill Template
```markdown
---
name: your-skill-name
description: Brief description with trigger conditions
compatibility:
tools: [datadog]
dependencies: [list_of_required_mcp_tools]
---
# Your Skill Name
## Capabilities
- What this skill accomplishes
- Key value propositions
## Usage Examples
- Realistic user prompts
- Expected workflow outputs
## Success Metrics
- Time to resolution targets
- Accuracy/completeness measures
```
## 📄 License
MIT License - Use freely, contribute back improvements.
## 🔗 Related Projects
- [Official Datadog MCP Server](https://docs.datadoghq.com/bits_ai/mcp_server/)
- [Claude Code Plugins](https://claude.com/plugins)
- [Model Context Protocol](https://modelcontextprotocol.io/)
## 🔎 Validation
Run the local smoke test before publishing plugin changes:
```bash
python3 scripts/smoke_test.py
```
---
**🚀 Ready to transform your Datadog monitoring into intelligent workflows?**
[📖 Get Started](setup/installation-guide.md) • [🎯 View Test Cases](test-cases.json) • [💡 Request Features](https://github.com/your-repo/issues)