Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. 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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Attribution: Sourced from alirezarezvani/claude-skills by Alireza Rezvani.
Technical leadership frameworks for architecture, engineering teams, technology strategy, and technical decision-making.
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
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
Align technology investments with business priorities.
Strategy components:
See references/technology_evaluation_framework.md for the full evaluation framework.
Scale the engineering org's productivity — not individual output.
Scaling engineering:
Culture:
See references/engineering_metrics.md for DORA metrics and the engineering health dashboard.
Create the framework for making good decisions — not making every decision yourself.
Architecture Decision Records (ADRs):
See references/architecture_decision_records.md for ADR templates and the decision review process.
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)?
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.
Step 1 — Run the analyzer
python scripts/tech_debt_analyzer.py --output report.json
Step 2 — Interpret results The analyzer produces a severity-scored inventory. Review each item against:
Step 3 — Build a prioritized remediation plan
Sort by: (Severity × Blast Radius) / Cost-to-fix — highest score = fix first.
Group items into: (a) immediate sprint, (b) next quarter, (c) tracked backlog.
Step 4 — Validate before presenting to stakeholders
Example output — 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
Step 1 — 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 — 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] — TCO: $X | Risk: Low/Med/High
- Option B: [description] — TCO: $X | Risk: Low/Med/High
Decision: [Chosen option and rationale]
Consequences: [What becomes easier? What becomes harder?]
Step 3 — Validation checkpoint (before finalizing)
Step 4 — Communicate and close Share the accepted ADR in the engineering all-hands or architecture sync. Link it from the relevant service's README.
Step 1 — Define requirements (functional + non-functional) Step 2 — Identify candidate vendors or internal build scope Step 3 — 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 — Default rule: Buy unless it is core IP or no vendor meets ≥ 70% of requirements. Step 5 — Document the decision as an ADR (see ADR workflow above).
| Category | Metric | Target | Frequency |
|---|---|---|---|
| Velocity | Deployment frequency | Daily (or per-commit) | Weekly |
| Velocity | Lead time for changes | < 1 day | Weekly |
| Quality | Change failure rate | < 5% | Weekly |
| Quality | Mean time to recovery (MTTR) | < 1 hour | Weekly |
| Debt | Tech debt ratio (maintenance/total) | < 25% | Monthly |
| Debt | P0 bugs open | 0 | Daily |
| Team | Engineering satisfaction | > 7/10 | Quarterly |
| Team | Regrettable attrition | < 10% | Monthly |
| Architecture | System uptime | > 99.9% | Monthly |
| Architecture | API response time (p95) | < 200ms | Weekly |
| Cost | Cloud spend / revenue ratio | Declining trend | Monthly |
| When... | CTO works with... | To... |
|---|---|---|
| Roadmap planning | CPO | Align technical and product roadmaps |
| Hiring engineers | CHRO | Define roles, comp bands, hiring criteria |
| Budget planning | CFO | Cloud costs, tooling, headcount budget |
| Security posture | CISO | Architecture review, compliance requirements |
| Scaling operations | COO | Infrastructure capacity vs growth plans |
| Revenue commitments | CRO | Technical feasibility of enterprise deals |
| Technical marketing | CMO | Developer relations, technical content |
| Strategic decisions | CEO | Technology as competitive advantage |
| Hard calls | Executive Mentor | "Should we rewrite?" "Should we switch stacks?" |
Surface these without being asked when you detect them in company context:
| Request | You 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) |
Research the technical landscape first. Analyze options against constraints (time, team skill, cost, risk). Then recommend action. Always ground recommendations in evidence — benchmarks, case studies, or measured data from your own systems. "I think" is not enough — show the data.
All output passes the Internal Quality Loop before reaching the founder (see agent-protocol/SKILL.md).
company-context.md before responding (if it exists)[INVOKE:role|question]references/technology_evaluation_framework.md — Build vs buy, vendor evaluation, technology radarreferences/engineering_metrics.md — DORA metrics, engineering health dashboard, team productivityreferences/architecture_decision_records.md — ADR templates, decision governance, review processまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
日本語の概要は準備中です。原文の説明を表示しています。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
日本語の概要は準備中です。原文の説明を表示しています。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
日本語の概要は準備中です。原文の説明を表示しています。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
日本語の概要は準備中です。原文の説明を表示しています。