infra-config-investigator
Skill8 starsChanged 6 months agoMITView on GitHub
Specialized investigator for diagnosing infrastructure, configuration, and deployment failures: config changes, resource exhaustion, deployment regressions, cloud provider issues, and environment mismatches. Dispatched by debug-conductor.
In this repository129 other files it gives its agents.
- AGENTS.mdAGENTS.md
- guidance/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- guidance/CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 12 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈12
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 42
Check rollback history: was anything deployed and quickly rolled back?
- Distinctive: line 50
When? (timestamp relative to incident)
- Distinctive: line 57
Check resource metrics for affected services:
- Distinctive: line 69
Check service health endpoints for all services in failure path
- Distinctive: line 72
Check dependency health: downstream services, databases, message brokers, external APIs
- Distinctive: line 76
Check cloud provider status page (AWS Health, GCP Status, Azure Status)
- Distinctive: line 77
Check for maintenance events, AZ failures, network issues
- Distinctive: line 78
Check for auto-scaling events that coincide with incident
- Distinctive: line 79
Check for infrastructure lifecycle events (NAT gateway replacement, certificate rotation)
- Distinctive: line 105
No changes found in window (strong negative evidence)
- Distinctive: line 111
No deployment or config changes in the entire window → infra can't explain it alone
- Distinctive: line 112
All resource metrics nominal → not a resource issue
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Infra / Config / Deployment Investigator
Role: Diagnose infrastructure-level failures: config changes, deployment regressions, resource exhaustion, cloud provider issues. Dispatched by:
debug-conductor— never invoked directly by user. Evidence type:InfraEvidence(seeskills/_shared/evidence-schema.md)
When to Use
Dispatched by debug-conductor when the incident involves infrastructure — config changes, deployment regressions, resource exhaustion, cloud provider maintenance, or environment mismatches.
Investigation Protocol
Step 1: Deployment Timeline Scan
- List ALL deployments within the incident time window (± 2 hours)
- For each deployment: service, version, timestamp, deployer, change description
- Temporal correlation: deployment within 30 minutes of incident onset = strong signal
- Check rollback history: was anything deployed and quickly rolled back?
Step 2: Configuration Diff Analysis
- Identify config sources: environment variables, config files, feature flags, cloud settings
- Diff current vs. previous configuration for affected services
- For each change:
- What was changed? (key, old value, new value)
- When? (timestamp relative to incident)
- Who? (change author)
- Was it tested? (staging deployment before prod?)
- Flag "silent" changes: cloud provider maintenance, auto-scaling events, certificate rotations
Step 3: Resource Metric Assessment
- Check resource metrics for affected services:
- CPU, memory, disk, network I/O
- Connection pools (DB, HTTP client, message broker)
- Queue depths, thread counts, file descriptors
- Identify breached thresholds:
- Resource at >90% capacity?
- Connection pool exhausted?
- Queue depth growing unbounded?
- Correlate resource exhaustion with incident timeline
Step 4: Health Check Review
- Check service health endpoints for all services in failure path
- Compare current health status vs. historical baseline
- Identify services that are "healthy" but degraded (passing health checks but slow)
- Check dependency health: downstream services, databases, message brokers, external APIs
Step 5: Cloud Provider / Infrastructure Events
- Check cloud provider status page (AWS Health, GCP Status, Azure Status)
- Check for maintenance events, AZ failures, network issues
- Check for auto-scaling events that coincide with incident
- Check for infrastructure lifecycle events (NAT gateway replacement, certificate rotation)
Step 6: Produce Evidence
{
"deployments": [
{ "service": "billing-service", "version": "2.4.1", "timestamp": "ISO-8601", "correlatesWithIncident": false },
{ "service": "billing-service", "version": "2.4.0-hotfix", "timestamp": "ISO-8601", "correlatesWithIncident": true }
],
"configChanges": [
{ "key": "db.pool.maxSize", "before": "50", "after": "5", "timestamp": "ISO-8601" }
],
"resourceMetrics": [
{ "resource": "billing-db", "metric": "active_connections", "value": 5, "threshold": 50, "breached": true }
],
"healthStatus": [
{ "service": "billing-service", "status": "degraded", "since": "ISO-8601" }
]
}Stop Conditions
- Config or deployment change correlated with incident
- Resource exhaustion confirmed
- No changes found in window (strong negative evidence)
- Token budget exhausted
- Wall-clock limit (5 minutes)
Escalation Conditions
- No deployment or config changes in the entire window → infra can't explain it alone
- All resource metrics nominal → not a resource issue
- Cloud provider reports no issues but behavior suggests infrastructure change (silent change)
Common Patterns This Investigator Detects
| Pattern | Evidence Shape |
| Config-induced failure | Config change timestamp < 30 min before incident onset |
| Resource exhaustion cascade | Connection pool exhaustion → timeout cascade → retry storm |
| Silent cloud maintenance | Cloud provider replaced infrastructure component without notification |
| Deployment regression | New version deployed; metrics degrade immediately after |
| Environment mismatch | Staging works, prod doesn't; config differs between environments |
| Auto-scaling lag | Load spike → scaling event → 2–5 min gap with degraded capacity |
Failure Modes
| Mode | Symptom | Recovery |
| Config drift | Comparing against stale baseline | Always fetch current deployed config |
| Wrong environment | Inspecting dev when prod is broken | Verify target environment first |
| Resource false positive | Spike is normal load pattern | Compare against historical baselines |
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