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bordenetbordenet/superpowers-plus/skills/engineering/infra-config-investigator/skill.md

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.

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
  1. Distinctive:

    Check rollback history: was anything deployed and quickly rolled back?

    line 42
  2. Distinctive:

    When? (timestamp relative to incident)

    line 50
  3. Distinctive:

    Check resource metrics for affected services:

    line 57
  4. Distinctive:

    Check service health endpoints for all services in failure path

    line 69
  5. Distinctive:

    Check dependency health: downstream services, databases, message brokers, external APIs

    line 72
  6. Distinctive:

    Check cloud provider status page (AWS Health, GCP Status, Azure Status)

    line 76
  7. Distinctive:

    Check for maintenance events, AZ failures, network issues

    line 77
  8. Distinctive:

    Check for auto-scaling events that coincide with incident

    line 78
  9. Distinctive:

    Check for infrastructure lifecycle events (NAT gateway replacement, certificate rotation)

    line 79
  10. Distinctive:

    No changes found in window (strong negative evidence)

    line 105
  11. Distinctive:

    No deployment or config changes in the entire window → infra can't explain it alone

    line 111
  12. Distinctive:

    All resource metrics nominal → not a resource issue

    line 112

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
skill.md

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 (see skills/_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

  1. List ALL deployments within the incident time window (± 2 hours)
  2. For each deployment: service, version, timestamp, deployer, change description
  3. Temporal correlation: deployment within 30 minutes of incident onset = strong signal
  4. Check rollback history: was anything deployed and quickly rolled back?

Step 2: Configuration Diff Analysis

  1. Identify config sources: environment variables, config files, feature flags, cloud settings
  2. Diff current vs. previous configuration for affected services
  3. 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?)
  4. Flag "silent" changes: cloud provider maintenance, auto-scaling events, certificate rotations

Step 3: Resource Metric Assessment

  1. 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
  2. Identify breached thresholds:
    • Resource at >90% capacity?
    • Connection pool exhausted?
    • Queue depth growing unbounded?
  3. Correlate resource exhaustion with incident timeline

Step 4: Health Check Review

  1. Check service health endpoints for all services in failure path
  2. Compare current health status vs. historical baseline
  3. Identify services that are "healthy" but degraded (passing health checks but slow)
  4. Check dependency health: downstream services, databases, message brokers, external APIs

Step 5: Cloud Provider / Infrastructure Events

  1. Check cloud provider status page (AWS Health, GCP Status, Azure Status)
  2. Check for maintenance events, AZ failures, network issues
  3. Check for auto-scaling events that coincide with incident
  4. 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

PatternEvidence Shape
Config-induced failureConfig change timestamp < 30 min before incident onset
Resource exhaustion cascadeConnection pool exhaustion → timeout cascade → retry storm
Silent cloud maintenanceCloud provider replaced infrastructure component without notification
Deployment regressionNew version deployed; metrics degrade immediately after
Environment mismatchStaging works, prod doesn't; config differs between environments
Auto-scaling lagLoad spike → scaling event → 2–5 min gap with degraded capacity

Failure Modes

ModeSymptomRecovery
Config driftComparing against stale baselineAlways fetch current deployed config
Wrong environmentInspecting dev when prod is brokenVerify target environment first
Resource false positiveSpike is normal load patternCompare against historical baselines

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