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Engineering operating model for teams where AI agents generate a large share of implementation output.
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Basé sur la classification professionnelle SOC
| name | ai-first-engineering |
| description | Engineering operating model for teams where AI agents generate a large share of implementation output. |
| origin | harness |
| workloads | ["ai"] |
Use this skill when designing process, reviews, and architecture for teams shipping with AI-assisted code generation.
Prefer architectures that are agent-friendly:
Avoid implicit behavior spread across hidden conventions.
Review for:
Minimize time spent on style issues already covered by automation.
Strong AI-first engineers:
Raise testing bar for generated code:
Create professional architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as standalone HTML files with SVG graphics, a built-in dark/light theme toggle, and one-click export to PNG / JPEG / WebP / SVG. Accepts plain-language descriptions or pasted Mermaid code (flowchart, sequenceDiagram, stateDiagram) and lays the diagram out from scratch in archify style. Use when the user asks for system architecture diagrams, infrastructure diagrams, cloud architecture visualizations, security diagrams, network topology, technical workflows, approval flows, runbooks, CI/CD flows, process diagrams, API call sequences, request lifecycles, data pipelines, ETL/ELT maps, PII boundaries, data lineage, state machines, lifecycle diagrams, status transitions, or asks to convert/beautify a Mermaid diagram.
Data analysis methodology — how to frame a question, pick the right technique, avoid statistical traps, and connect results to decisions. NOT a tool tutorial (pandas/polars/duckdb live in python-data-analysis) — this is the judgment layer: problem framing, analysis-type decision tree, experiment/causal design, and domain playbooks (churn, cohort, funnel, anomaly). Trigger: "왜 늘었/줄었지", 이탈 분석, 코호트, 퍼널, A/B 테스트, 인과추론, 상관 vs 인과, 유의성, 세그먼트, 이상 탐지, "이 데이터로 뭘 봐야", 지표 설계, exploratory analysis.
AWS cost management and FinOps practice — Cost Explorer, Budgets, Cost Anomaly Detection, CUR/data exports, cost allocation tags, Cost Categories, Savings Plans vs Reserved Instances, Compute Optimizer / Cost Optimization Hub, rightsizing, unit economics, showback/chargeback. Grounded in the FinOps Foundation Framework (Inform / Optimize / Operate). Trigger: cost anomaly, Savings Plan, Reserved Instance, rightsizing, cost allocation tag, chargeback, showback, unit cost, budget alert, CUR, Cost Explorer, unblended/amortized cost, RI coverage, commitment, "왜 청구서가 늘었지".
Track and report AI model token usage, spending, and budgets from a local cost-tracking database.
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
