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cto-engineering-metrics

@rinaldofesta/cto-os-skills
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Expert methodology for defining, tracking, and interpreting engineering performance metrics including DORA, team health, productivity, and executive reporting.

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SKILL.md

name cto-engineering-metrics
description Expert methodology for defining, tracking, and interpreting engineering performance metrics including DORA, team health, productivity, and executive reporting.

CTO Engineering Metrics Skill

Purpose

This skill provides a comprehensive framework for measuring engineering organization performance. Use it to define metrics, create dashboards, interpret data, and communicate engineering impact to different stakeholders (executives, board, engineering team).

When to Use

Trigger this skill when you need to:

  • Set up engineering metrics and KPIs for your organization
  • Create executive dashboards or board presentations
  • Diagnose engineering productivity or quality issues
  • Demonstrate engineering value to business stakeholders
  • Benchmark your team against industry standards
  • Define OKRs and success criteria for engineering
  • Track team health and developer satisfaction
  • Measure DevOps/platform improvements

Core Methodology

Follow this systematic approach to engineering metrics:

Phase 1: Define Purpose and Audience

  1. Identify the Question

    • What are we trying to understand or improve?
    • What decision will these metrics inform?
    • What behavior do we want to encourage?

  2. Know Your Audience

    • Board/Investors: Business impact, risk, strategic progress
    • CEO/Executives: Productivity, velocity, quality, cost efficiency
    • Engineering Team: Flow, blockers, technical health, growth
    • Product Team: Delivery predictability, feature velocity
    • Yourself (CTO): Holistic health, trends, early warnings

  3. Avoid Metric Traps

    • Don't measure what's easy, measure what matters
    • Avoid vanity metrics (lines of code, hours worked)
    • Beware Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure"
    • Combine metrics to prevent gaming

Phase 2: Select Metrics Framework

Choose the appropriate framework for your context:

DORA Metrics (DevOps Performance)

The gold standard for measuring DevOps effectiveness:

  1. Deployment Frequency: How often do you deploy to production?
  2. Lead Time for Changes: Time from commit to production
  3. Mean Time to Recovery (MTTR): Time to restore service after incident
  4. Change Failure Rate: % of deployments causing failures

Use references/frameworks/dora-metrics.md for detailed implementation.

When to use: Assessing DevOps maturity, improving deployment pipeline

SPACE Framework (Developer Productivity)

Holistic view of developer productivity across 5 dimensions:

  1. Satisfaction: Developer happiness and wellbeing
  2. Performance: Code quality and impact
  3. Activity: Volume of work (commits, PRs, reviews)
  4. Communication: Collaboration effectiveness
  5. Efficiency: Ability to complete work without interruption

Use references/frameworks/space-framework.md for implementation.

When to use: Understanding developer experience, diagnosing productivity issues

Business Impact Metrics

Connect engineering to business outcomes:

  1. Feature Velocity: Business value delivered per sprint
  2. Engineering Cost per Customer: Cost efficiency
  3. Time to Market: Speed of delivering customer value
  4. System Reliability: Uptime, SLA compliance
  5. Engineering ROI: Return on engineering investment

Use references/frameworks/business-impact-metrics.md for details.

When to use: Demonstrating value to executives/board, justifying budget

Team Health Metrics

Measure team sustainability and morale:

  1. Developer Satisfaction Score: Regular surveys (eNPS)
  2. Retention Rate: Voluntary turnover
  3. Time to Productivity: How fast new hires contribute
  4. Burnout Indicators: On-call load, overtime, vacation usage
  5. Psychological Safety: Team confidence in taking risks

Use references/frameworks/team-health-metrics.md for surveys and benchmarks.

When to use: Preventing burnout, improving retention, building culture

Phase 3: Implement Measurement

  1. Identify Data Sources

    • Version control: GitHub, GitLab, Bitbucket
    • CI/CD: Jenkins, CircleCI, GitHub Actions
    • Issue tracking: Jira, Linear, GitHub Issues
    • Monitoring: Datadog, New Relic, PagerDuty
    • Surveys: Officevibe, Culture Amp, Google Forms

  2. Automate Collection

    • Use APIs to pull data automatically
    • Build dashboards with real-time updates
    • Set up regular surveys (weekly/monthly)
    • Avoid manual reporting (too time-consuming, error-prone)

  3. Establish Baselines

    • Measure current state before setting goals
    • Understand natural variation
    • Compare to industry benchmarks
    • Track trends over time, not point-in-time

Use references/templates/metrics-implementation-plan.md for step-by-step setup.

Phase 4: Interpret and Act

  1. Look for Patterns

    • Are metrics improving or declining?
    • Are there correlations? (e.g., deploy frequency ↑, change failure ↓)
    • Are there anomalies? (sudden spikes or drops)
    • Are different teams showing different patterns?

  2. Ask "Why?"

    • Metrics show what is happening, not why
    • Combine quantitative data with qualitative insights
    • Talk to engineers about what they see
    • Look for systemic issues, not individual performance

  3. Define Actions

    • What specifically will we change?
    • Who owns the improvement?
    • What's the expected impact on metrics?
    • When will we review progress?

  4. Avoid Anti-Patterns

    • Don't measure individuals, measure systems
    • Don't use metrics for performance reviews
    • Don't set arbitrary improvement targets without context
    • Don't track too many metrics (focus on 5-7 key ones)

Use references/templates/metrics-interpretation-guide.md for analysis playbook.

Phase 5: Communicate Effectively

Tailor your message to the audience:

For Board/Investors

Focus: Risk, strategic progress, competitive positioning

Key Metrics:

  • System reliability (uptime %)
  • Security incidents and response
  • Engineering budget vs. plan
  • Technical debt trajectory
  • Team growth and retention

Format: High-level dashboard, traffic-light status, narrative

Use references/templates/board-dashboard.md

For CEO/Executives

Focus: Business impact, velocity, quality, efficiency

Key Metrics:

  • Feature delivery rate
  • Engineering cost per revenue
  • Time to market
  • Quality (bugs, incidents)
  • Team productivity trends

Format: Executive dashboard with trends and insights

Use references/templates/executive-dashboard.md

For Engineering Team

Focus: Flow, improvements, celebrating wins

Key Metrics:

  • Deployment frequency
  • Lead time
  • Code review time
  • Incident trends
  • Team satisfaction

Format: Live dashboard, retrospectives, all-hands updates

Use references/templates/team-dashboard.md

Key Principles

  • Start Simple: Begin with 3-5 core metrics, expand later
  • Measure Systems, Not People: Focus on process improvement
  • Balance is Key: Combine speed, quality, and sustainability metrics
  • Context Matters: Benchmarks vary by company stage, industry, team size
  • Act on Insights: Metrics without action are vanity numbers
  • Iterate: Refine metrics as your organization evolves

Bundled Resources

Frameworks (references/frameworks/):

  • dora-metrics.md - Complete DORA implementation guide with benchmarks
  • space-framework.md - Developer productivity measurement
  • business-impact-metrics.md - Connecting engineering to revenue
  • team-health-metrics.md - Satisfaction, retention, burnout indicators

Templates (references/templates/):

  • metrics-implementation-plan.md - Step-by-step setup guide
  • board-dashboard.md - Board presentation template
  • executive-dashboard.md - CEO/executive reporting
  • team-dashboard.md - Engineering team metrics
  • metrics-interpretation-guide.md - How to analyze and act on data
  • survey-templates.md - Developer satisfaction surveys

Examples (references/examples/):

  • Real dashboard examples from startups to enterprises
  • Sample board presentations
  • Before/after improvement stories
  • Benchmark data by company stage

Usage Patterns

Example 1: User says "Set up DORA metrics for my 30-person engineering team"

→ Load references/frameworks/dora-metrics.md → Identify data sources (e.g., GitHub + CircleCI + PagerDuty) → Create implementation plan using template → Define baseline benchmarks for team stage → Set up dashboard with targets → Provide interpretation guide

Example 2: User says "Create board presentation showing engineering performance"

→ Load references/templates/board-dashboard.md → Focus on: reliability, security, strategic initiatives, team health → Use traffic-light status for clarity → Include 1-2 key risks and mitigation plans → Keep to 3-5 slides maximum → Provide talking points

Example 3: User says "Our deployment frequency is low, help diagnose why"

→ Look at full DORA metrics: lead time, MTTR, change failure rate → Check deployment process: manual steps, review bottlenecks, test duration → Survey team for qualitative insights → Identify top 3 blockers → Create improvement plan with expected metrics impact

Example 4: User says "Prove that engineering is being productive to the CEO"

→ Load references/frameworks/business-impact-metrics.md → Calculate: features shipped, customer impact, time to market → Show efficiency: cost per feature, productivity trends → Demonstrate quality: bug rates, incident trends → Connect to business goals: revenue features vs. platform investment → Use executive dashboard template

Metrics by Company Stage

Early Stage Startup (5-15 engineers)

Focus: Speed to learn, customer feedback

Key Metrics (3-5):

  • Deployment frequency (weekly)
  • Customer-reported bugs
  • Feature delivery vs. plan
  • Team satisfaction

Anti-Pattern: Over-measuring at this stage. Stay lean.

Growth Stage (15-50 engineers)

Focus: Scaling processes, maintaining quality

Key Metrics (5-7):

  • Full DORA metrics
  • Code review time
  • Incident rate and MTTR
  • Team health scores
  • Feature velocity

Challenge: Maintaining speed while adding process

Scale Stage (50-200 engineers)

Focus: Efficiency, coordination, technical excellence

Key Metrics (7-10):

  • DORA metrics by team
  • Developer productivity (SPACE)
  • Cost efficiency metrics
  • Cross-team delivery time
  • Technical debt ratio
  • Retention and satisfaction

Challenge: Comparing teams fairly, avoiding metric gaming

Enterprise (200+ engineers)

Focus: Strategic alignment, innovation balance, efficiency

Key Metrics (10-15):

  • All previous metrics, segmented
  • Engineering ROI
  • Innovation vs. maintenance ratio
  • Platform adoption rates
  • Organizational health
  • Strategic initiative progress

Challenge: Not drowning in metrics, maintaining agility

Warning Signs

These metric patterns indicate issues:

Pattern Possible Issue Action
Deploy frequency ↓ Fear of breaking things, process overhead Simplify process, improve testing
Lead time ↑ Bottlenecks, growing complexity Find bottlenecks (code review? testing?)
MTTR ↑ Poor monitoring, unclear ownership Improve observability, on-call process
Change failure rate ↑ Insufficient testing, rushing Strengthen testing, review release process
Satisfaction ↓ Burnout, unclear direction, blockers Talk to team, address top frustrations
Retention rate ↓ Culture issues, compensation, growth Exit interviews, competitive analysis

Writing Style

All outputs should be:

  • Data-driven: Use specific numbers and benchmarks
  • Actionable: Connect metrics to concrete improvements
  • Balanced: Show both strengths and areas for improvement
  • Contextual: Adjust for company stage and industry
  • Honest: Don't hide problems, show how you're addressing them

Version: 1.0.0 Philosophy: Measure what matters, act on insights, improve continuously