cicd-audit-optimizer

name	cicd-audit-optimizer
description	Comprehensive CI/CD and automation audit skill. Catalogs all pipelines, assesses operational health, identifies gaps, and provides prioritized recommendations for optimization. Includes error cataloging and root cause analysis framework.

Skill: CI/CD Audit & Optimization

Your role is to act as a DevOps Audit Specialist who systematically analyzes CI/CD pipelines, automation workflows, and deployment processes to identify inefficiencies, gaps, and opportunities for improvement.

Objective

Conduct a full audit of the current CI/CD and automation landscape to:

1. Establish a complete inventory
2. Verify operational health
3. Identify critical gaps and optimization opportunities
4. Provide strategic recommendations to increase deployment velocity and reliability

Phase 1: Full Inventory & Scope

Document all CI/CD pipelines and adjacent automations across all services and repositories.

For each item, capture:

Pipeline Metadata

• Service/Repo: Which application or repository does it belong to?
• Description: What is its purpose? (e.g., "Builds and deploys frontend-web to staging.")
• Tools Used: (e.g., Jenkins, GitHub Actions, GitLab CI, Terraform, Ansible, custom scripts)
• Triggers: How is it activated? (e.g., on-commit to main, nightly schedule, manual trigger)
• Artifacts: What does it produce? (e.g., Docker image, .jar file, Terraform plan)

Discovery Commands

# Find all CI/CD configuration files
find . -type f \( \
  -name ".gitlab-ci.yml" -o \
  -name ".github/workflows/*.yml" -o \
  -name "Jenkinsfile" -o \
  -name "azure-pipelines.yml" -o \
  -name ".circleci/config.yml" -o \
  -name "bitbucket-pipelines.yml" \
\)

# Find all automation scripts
find . -type f \( \
  -name "deploy*.sh" -o \
  -name "build*.sh" -o \
  -name "*pipeline*" -o \
  -name "Makefile" \
\)

# Find all IaC files
find . -type f \( \
  -name "*.tf" -o \
  -name "*.tfvars" -o \
  -name "*.yaml" -path "*/ansible/*" -o \
  -name "*.yml" -path "*/ansible/*" \
\)

Phase 2: Operational Health Check

For each item identified in Phase 1, assess its current operational status.

Health Metrics

1. Current Status: Is it fully operational, partially failing, or disabled?

2. Key Metrics (last 30 days):

   • Success/Failure Rate (%)
   • Average Runtime
   • Failure patterns (time of day, specific steps)

3. Monitoring & Alerting: How are we alerted if this automation fails?

   • Slack alert
   • PagerDuty
   • Email
   • No monitoring (❌ HIGH RISK)

GitHub Actions Health Check

# Get workflow runs for last 30 days
gh run list --limit 100 --json conclusion,name,startedAt,durationMs

# Get specific workflow details
gh workflow view <workflow-name>

# Get failed runs
gh run list --status failure --limit 20

Health Status Report Template

## Pipeline Health Report

### ✅ Healthy Pipelines (>95% success rate)
- [pipeline-name]: 99.2% success, avg 3m runtime
- ...

### ⚠️ At-Risk Pipelines (80-95% success rate)
- [pipeline-name]: 87% success, avg 12m runtime
  - Common failures: Flaky integration tests
  - Alert method: Slack #ci-failures

### ❌ Failing Pipelines (<80% success rate)
- [pipeline-name]: 45% success, avg 8m runtime
  - Common failures: Timeout on npm install
  - Alert method: None (CRITICAL)
  - Action required: Investigate dependency issues

### 🚫 Disabled/Abandoned Pipelines
- [pipeline-name]: Last run 3 months ago
  - Reason: Unknown
  - Action required: Archive or re-enable

Phase 3: Gap Analysis & Future Automation

Identify all gaps and opportunities to improve the automation posture.

1. Manual Gaps

What processes in the software delivery lifecycle (from commit to production) are still partially or fully manual?

Common Manual Processes to Check:

• Manual QA testing
• Manual approval gates (where automation could pre-screen)
• Running database migrations by hand
• Creating hotfixes
• Rolling back changes
• Environment provisioning
• SSL certificate renewal
• DNS updates
• Secret rotation
• Dependency updates

2. Pipeline Bottlenecks

Performance Analysis:

# Analyze GitHub Actions timing
gh run list --json durationMs,name,steps \
  | jq -r '.[] | "\(.name),\(.durationMs)"' \
  | sort -t, -k2 -rn \
  | head -20

Common Bottlenecks:

• Slow test suites (can they be parallelized?)
• Large Docker image builds (can layers be cached?)
• Sequential jobs that could run in parallel
• External API calls without timeout/retry logic

3. Missing Automations

Security & Compliance:

• Automated dependency vulnerability scanning (Dependabot, Snyk)
• SAST (Static Application Security Testing)
• DAST (Dynamic Application Security Testing)
• Secret scanning (GitGuardian, TruffleHog)
• License compliance checking

Quality Gates:

• Code coverage enforcement
• Performance regression testing
• Load testing
• Accessibility testing (WCAG compliance)
• Browser compatibility testing

Operations:

• Auto-generated release notes
• Automated rollback on failed health checks
• Automatic scaling based on load
• Cost optimization (shutdown dev/staging at night)
• Infrastructure drift detection

Observability:

• Automatic SLO monitoring
• Error rate alerts
• Performance monitoring (APM integration)
• Log aggregation and alerting

Phase 4: Error Cataloging & Root Cause Analysis

The Failure Catalog

Create a structured log of all CI/CD failures with these fields:

{
  "incident_id": "INC-2025-001",
  "timestamp": "2025-11-09T14:32:00Z",
  "system_agent": "deploy-production-workflow",
  "symptom": "Deployment to production failed with 500 error",
  "full_logs": "https://github.com/org/repo/actions/runs/12345",
  "initial_triage": "Infrastructure",
  "root_cause": "ECS task definition had incorrect environment variables",
  "category": "Configuration Error",
  "fix_applied": "Updated terraform to use correct secret ARN",
  "prevention": "Added validation step to check env vars before deploy"
}

Root Cause Analysis Framework

Step 1: Can you reproduce the failure exactly?

NO → Intermittent failure (Infrastructure/Tools issue)

• Look for: Resource spikes (CPU/memory), network timeouts, rate limiting, race conditions
• Tools: Check CloudWatch metrics, application logs, network traces

YES → Consistent failure (Skill/Capability or Tools issue)

• Proceed to Step 2

Step 2: Can you reproduce the failure manually?

Bypass automation. Run the exact command manually with the same permissions.

YES, still fails → Tools/Technology problem

• The code/config is invalid
• External API is down
• Tool version has a bug
• Fix: Address the underlying tool/code issue

NO, works manually → Automation Skill or Infrastructure problem

• The automation is doing something (or not doing something) differently
• Proceed to Step 3

Step 3: Do logs show a clear infrastructure error?

YES → Infrastructure problem

• Error 503: Service Unavailable
• OOMKilled (Out of Memory)
• Permission Denied / 403 Forbidden
• Connection Timeout
• Fix: Address infrastructure capacity, permissions, networking

NO → Skill/Capability problem

• Tool misuse (wrong parameters)
• Hallucination (invented parameters)
• Specification issue (ambiguous instructions)
• Error handling failure
• Fix: Improve automation logic, error handling, or documentation

The 5 Whys Method

Use this for drilling down to root cause:

Symptom: Deploy-to-production workflow failed

Why? The terraform apply command failed
Why? Command returned 403 Forbidden from AWS
Why? IAM role credentials lacked permissions
Why? ECS task execution role was missing ec2:CreateInstance policy
Why? New "Create Instance" policy was never attached to service account

ROOT CAUSE: Missing IAM policy attachment
FIX: Attach policy to service account
PREVENTION: Add IAM policy validation step to terraform CI/CD

Categorization Framework

Classify each failure into one of these categories:

1. Skills & Capabilities (The "Brain")

• Vague automation instructions
• Poor error handling logic
• Automation passes wrong parameters
• Missing context or dependencies

2. Infrastructure (The "Body")

• OOM (Out of Memory) errors
• Network timeouts
• IAM/permission issues
• Disk space exhausted
• Resource limits reached

3. Tools & Technology (The "Tools")

• Bug in tool version
• Flaky tests
• Inconsistent build environment
• External API downtime
• Dependency conflicts

4. Process & People (The "Human")

• Manual intervention caused drift
• Bad code merged to main
• Outdated documentation
• Missing code review

Deliverable Format

Executive Summary

• Total pipelines/automations inventoried
• Overall health score (% healthy, at-risk, failing)
• Top 3 critical gaps
• Top 3 quick wins

Detailed Inventory

• Complete list of all CI/CD pipelines with metadata
• Health metrics for each

Gap Analysis

Prioritized list of recommendations with:

• Impact: High/Medium/Low
• Complexity: High/Medium/Low
• Estimated effort: Hours/Days/Weeks
• Priority score: Impact × Urgency / Complexity

Example Recommendation

### Recommendation #1: Implement Automated Dependency Updates

**Impact**: HIGH
- Reduces security vulnerabilities
- Eliminates weekly manual dependency review meeting
- Prevents production incidents from outdated packages

**Complexity**: LOW
- Enable Dependabot in GitHub (1 config file)
- Configure auto-merge for patch versions

**Estimated Effort**: 2 hours

**Priority Score**: 9/10

**Implementation Plan**:
1. Create `.github/dependabot.yml` config
2. Enable auto-merge for patch/minor versions with passing tests
3. Configure Slack notifications for major version updates
4. Set up weekly dependency dashboard review

DORA Metrics Baseline

As part of the audit, establish baseline DORA metrics:

# Deployment Frequency
gh api graphql -f query='
  query($owner: String!, $repo: String!) {
    repository(owner: $owner, name: $repo) {
      deployments(last: 100, environments: ["production"]) {
        nodes {
          createdAt
        }
      }
    }
  }
' -f owner=ORG -f repo=REPO

# Lead Time for Changes
# (Calculate time from commit to production deployment)

# Change Failure Rate
# (% of deployments that required hotfix/rollback)

# Mean Time to Recovery
# (Average time from incident detection to resolution)

Tools and Commands Reference

GitHub CLI (gh)

# List all workflows
gh workflow list

# View workflow runs
gh run list --workflow=deploy.yml --limit 50

# View failed runs
gh run list --status=failure --json conclusion,name,startedAt

# Download workflow logs
gh run view <run-id> --log

GitLab CI

# Get pipeline status
curl --header "PRIVATE-TOKEN: <token>" \
  "https://gitlab.com/api/v4/projects/<id>/pipelines"

# Get failed jobs
curl --header "PRIVATE-TOKEN: <token>" \
  "https://gitlab.com/api/v4/projects/<id>/pipelines/<id>/jobs?scope=failed"

Jenkins

# Get build status via API
curl -s "http://jenkins/job/<job-name>/lastBuild/api/json" \
  | jq '.result'

# Get failed builds
curl -s "http://jenkins/job/<job-name>/api/json" \
  | jq '.builds[] | select(.result == "FAILURE")'

Success Criteria

This audit is complete when:

• 100% of CI/CD pipelines are documented
• Health metrics available for all active pipelines
• All failures from last 30 days are cataloged
• Root cause identified for top 10 most frequent failures
• Prioritized recommendations list created
• DORA baseline metrics established
• Executive summary delivered