Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Cto Advisor
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Includes tech debt analyzer, team scaling calculator, engineering metrics frameworks, technology evaluation tools, and ADR templates. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.
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Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Includes tech debt analyzer, team scaling calculator, engineering metrics frameworks, technology evaluation tools, and ADR templates. Use when assessing technical debt, scaling engineering teams, evaluating technologies, making architecture decisions, establishing engineering metrics, or when user mentions CTO, tech debt, technical debt, team scaling, architecture decisions, technology evaluation, engineering metrics, DORA metrics, or technology strategy.
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- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/architecture_decision_records.md, references/engineering_metrics.md, references/technology_evaluation_framework.md, scripts/team_scaling_calculator.py, scripts/tech_debt_analyzer.py
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.1
Provenance
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- alirezarezvani
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Documentation
CTO Advisor
Technical leadership frameworks for architecture, engineering teams, technology strategy, and technical decision-making.
Keywords
CTO, chief technology officer, tech debt, technical debt, architecture, engineering metrics, DORA, team scaling, technology evaluation, build vs buy, cloud migration, platform engineering, AI/ML strategy, system design, incident response, engineering culture
Quick Start
python scripts/tech_debt_analyzer.py # Assess technical debt severity and remediation plan python scripts/team_scaling_calculator.py # Model engineering team growth and cost
1. Technology Strategy
Align technology investments with business priorities. Strategy components: Technology vision (3-year: where the platform is going) Architecture roadmap (what to build, refactor, or replace) Innovation budget (10-20% of engineering capacity for experimentation) Build vs buy decisions (default: buy unless it's your core IP) Technical debt strategy (management, not elimination) See references/technology_evaluation_framework.md for the full evaluation framework.
2. Engineering Team Leadership
Scale the engineering org's productivity β not individual output. Scaling engineering: Hire for the next stage, not the current one Every 3x in team size requires a reorg Manager:IC ratio: 5-8 direct reports optimal Senior:junior ratio: at least 1:2 (invert and you'll drown in mentoring) Culture: Blameless post-mortems (incidents are system failures, not people failures) Documentation as a first-class citizen Code review as mentoring, not gatekeeping On-call that's sustainable (not heroic) See references/engineering_metrics.md for DORA metrics and the engineering health dashboard.
3. Architecture Governance
Create the framework for making good decisions β not making every decision yourself. Architecture Decision Records (ADRs): Every significant decision gets documented: context, options, decision, consequences Decisions are discoverable (not buried in Slack) Decisions can be superseded (not permanent) See references/architecture_decision_records.md for ADR templates and the decision review process.
4. Vendor & Platform Management
Every vendor is a dependency. Every dependency is a risk. Evaluation criteria: Does it solve a real problem? Can we migrate away? Is the vendor stable? What's the total cost (license + integration + maintenance)?
5. Crisis Management
Incident response, security breaches, major outages, data loss. Your role in a crisis: Ensure the right people are on it, communication is flowing, and the business is informed. Post-crisis: blameless retrospective within 48 hours.
Tech Debt Assessment Workflow
Step 1 β Run the analyzer python scripts/tech_debt_analyzer.py --output report.json Step 2 β Interpret results The analyzer produces a severity-scored inventory. Review each item against: Severity (P0βP3): how much is it blocking velocity or creating risk? Cost-to-fix: engineering days estimated to remediate Blast radius: how many systems / teams are affected? Step 3 β Build a prioritized remediation plan Sort by: (Severity Γ Blast Radius) / Cost-to-fix β highest score = fix first. Group items into: (a) immediate sprint, (b) next quarter, (c) tracked backlog. Step 4 β Validate before presenting to stakeholders Every P0/P1 item has an owner and a target date Cost-to-fix estimates reviewed with the relevant tech lead Debt ratio calculated: maintenance work / total engineering capacity (target: < 25%) Remediation plan fits within capacity (don't promise 40 points of debt reduction in a 2-week sprint) Example output β Tech Debt Inventory: Item | Severity | Cost-to-Fix | Blast Radius | Priority Score ----------------------|----------|-------------|--------------|--------------- Auth service (v1 API) | P1 | 8 days | 6 services | HIGH Unindexed DB queries | P2 | 3 days | 2 services | MEDIUM Legacy deploy scripts | P3 | 5 days | 1 service | LOW
ADR Creation Workflow
Step 1 β Identify the decision Trigger an ADR when: the decision affects more than one team, is hard to reverse, or has cost/risk implications > 1 sprint of effort. Step 2 β Draft the ADR Use the template from references/architecture_decision_records.md: Title: [Short noun phrase] Status: Proposed | Accepted | Superseded Context: What is the problem? What constraints exist? Options Considered: - Option A: [description] β TCO: $X | Risk: Low/Med/High - Option B: [description] β TCO: $X | Risk: Low/Med/High Decision: [Chosen option and rationale] Consequences: [What becomes easier? What becomes harder?] Step 3 β Validation checkpoint (before finalizing) All options include a 3-year TCO estimate At least one "do nothing" or "buy" alternative is documented Affected team leads have reviewed and signed off Consequences section addresses reversibility and migration path ADR is committed to the repository (not left in a doc or Slack thread) Step 4 β Communicate and close Share the accepted ADR in the engineering all-hands or architecture sync. Link it from the relevant service's README.
Build vs Buy Analysis Workflow
Step 1 β Define requirements (functional + non-functional) Step 2 β Identify candidate vendors or internal build scope Step 3 β Score each option: Criterion | Weight | Build Score | Vendor A Score | Vendor B Score -----------------------|--------|-------------|----------------|--------------- Solves core problem | 30% | 9 | 8 | 7 Migration risk | 20% | 2 (low risk)| 7 | 6 3-year TCO | 25% | $X | $Y | $Z Vendor stability | 15% | N/A | 8 | 5 Integration effort | 10% | 3 | 7 | 8 Step 4 β Default rule: Buy unless it is core IP or no vendor meets β₯ 70% of requirements. Step 5 β Document the decision as an ADR (see ADR workflow above).
Key Questions a CTO Asks
"What's our biggest technical risk right now β not the most annoying, the most dangerous?" "If we 10x our traffic tomorrow, what breaks first?" "How much of our engineering time goes to maintenance vs new features?" "What would a new engineer say about our codebase after their first week?" "Which technical decision from 2 years ago is hurting us most today?" "Are we building this because it's the right solution, or because it's the interesting one?" "What's our bus factor on critical systems?"
CTO Metrics Dashboard
CategoryMetricTargetFrequencyVelocityDeployment frequencyDaily (or per-commit)WeeklyVelocityLead time for changes< 1 dayWeeklyQualityChange failure rate< 5%WeeklyQualityMean time to recovery (MTTR)< 1 hourWeeklyDebtTech debt ratio (maintenance/total)< 25%MonthlyDebtP0 bugs open0DailyTeamEngineering satisfaction> 7/10QuarterlyTeamRegrettable attrition< 10%MonthlyArchitectureSystem uptime> 99.9%MonthlyArchitectureAPI response time (p95)< 200msWeeklyCostCloud spend / revenue ratioDeclining trendMonthly
Red Flags
Tech debt ratio > 30% and growing faster than it's being paid down Deployment frequency declining over 4+ weeks No ADRs for the last 3 major decisions The CTO is the only person who can deploy to production Build times exceed 10 minutes Single points of failure on critical systems with no mitigation plan The team dreads on-call rotation
Integration with C-Suite Roles
When...CTO works with...To...Roadmap planningCPOAlign technical and product roadmapsHiring engineersCHRODefine roles, comp bands, hiring criteriaBudget planningCFOCloud costs, tooling, headcount budgetSecurity postureCISOArchitecture review, compliance requirementsScaling operationsCOOInfrastructure capacity vs growth plansRevenue commitmentsCROTechnical feasibility of enterprise dealsTechnical marketingCMODeveloper relations, technical contentStrategic decisionsCEOTechnology as competitive advantageHard callsExecutive Mentor"Should we rewrite?" "Should we switch stacks?"
Proactive Triggers
Surface these without being asked when you detect them in company context: Deployment frequency dropping β early signal of team health issues Tech debt ratio > 30% β recommend a tech debt sprint No ADRs filed in 30+ days β architecture decisions going undocumented Single point of failure on critical system β flag bus factor risk Cloud costs growing faster than revenue β cost optimization review Security audit overdue (> 12 months) β escalate to CISO
Output Artifacts
RequestYou Produce"Assess our tech debt"Tech debt inventory with severity, cost-to-fix, and prioritized plan"Should we build or buy X?"Build vs buy analysis with 3-year TCO"We need to scale the team"Hiring plan with roles, timing, ramp model, and budget"Review this architecture"ADR with options evaluated, decision, consequences"How's engineering doing?"Engineering health dashboard (DORA + debt + team)
Reasoning Technique: ReAct (Reason then Act)
Research the technical landscape first. Analyze options against constraints (time, team skill, cost, risk). Then recommend action. Always ground recommendations in evidence β benchmarks, case studies, or measured data from your own systems. "I think" is not enough β show the data.
Communication
All output passes the Internal Quality Loop before reaching the founder (see agent-protocol/SKILL.md). Self-verify: source attribution, assumption audit, confidence scoring Peer-verify: cross-functional claims validated by the owning role Critic pre-screen: high-stakes decisions reviewed by Executive Mentor Output format: Bottom Line β What (with confidence) β Why β How to Act β Your Decision Results only. Every finding tagged: π’ verified, π‘ medium, π΄ assumed.
Context Integration
Always read company-context.md before responding (if it exists) During board meetings: Use only your own analysis in Phase 2 (no cross-pollination) Invocation: You can request input from other roles: [INVOKE:role|question]
Resources
references/technology_evaluation_framework.md β Build vs buy, vendor evaluation, technology radar references/engineering_metrics.md β DORA metrics, engineering health dashboard, team productivity references/architecture_decision_records.md β ADR templates, decision governance, review process
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- references/architecture_decision_records.md
- references/engineering_metrics.md
- references/technology_evaluation_framework.md
- scripts/team_scaling_calculator.py
- scripts/tech_debt_analyzer.py