01046b01e4
Umfangreiche Erweiterung der Skill-Bibliothek: Neue Skills für Humanisierung (Englisch/PT-BR), Design-Validierung, AI-SEO und Coolify-Deployment inkl. Regelwerke, Presets, Pattern-Referenzen, Testfälle und Automatisierungsskripte. Zusätzliche Skills für Revenue-Centric Design, Pier Cloud, OKF, Lebenslauf- und LinkedIn-Optimierung sowie zahlreiche Referenzdateien, Checklisten und YAML/JSON/Markdown-Templates. Einführung einer vollständigen OpenWiki-Dokumentation mit Architektur-, Domain- und Workflow-Beschreibungen, zentralem Index und automatisierten Updates. Modularer Aufbau, restriktive Lizenzen und umfassende Qualitäts- und Evaluationsmechanismen für alle neuen Inhalte.
5.7 KiB
5.7 KiB
Presets de Cargo & Formatos Canônicos
Referência para auditoria LinkedIn e currículo ATS. Cobre profissionais especializados em geral — não apenas dev.
Presets de Cargo
Cada preset contém: label, role, keywords, headline_areas, headline_tech (ferramentas/competências).
Tecnologia
| Label | Headline Canônica |
|---|---|
backend-engineer |
Backend Engineer | APIs, Microservices & Distributed Systems | Python · Java · Go · PostgreSQL · AWS · Kubernetes |
frontend-engineer |
Frontend Engineer | Web Apps, UX Performance & Design Systems | React · TypeScript · Next.js · CSS · Performance |
fullstack-engineer |
Full Stack Engineer | Product Engineering, APIs & Full Stack Delivery | React · Node.js · TypeScript · PostgreSQL · AWS |
data-engineer |
Data Engineer | Data Platform, CDP & Reliability | GCP · Airflow · BigQuery · Spark · Terraform · dbt |
devops-engineer |
DevOps / SRE | Cloud Infrastructure, Reliability & Platform Ops | Kubernetes · Terraform · AWS · Docker · CI/CD |
mobile-engineer |
Mobile Engineer | Mobile Apps, Performance & Cross-Platform | Kotlin · Swift · Flutter · React Native |
staff-engineer |
Staff Software Engineer | Platform Architecture, Scale & Technical Leadership | System Design · Distributed Systems · Cloud |
Dados & Analytics
| Label | Headline Canônica |
|---|---|
data-scientist |
Data Scientist | Machine Learning, Statistical Modeling & Business Intelligence | Python · R · TensorFlow · SQL · Tableau |
data-analyst |
Data Analyst | Business Intelligence, Reporting & Data Visualization | SQL · Power BI · Tableau · Excel · Python |
analytics-engineer |
Analytics Engineer | Data Modeling, Metrics & Self-Serve Analytics | dbt · SQL · Looker · BigQuery · Snowflake |
Marketing & Growth
| Label | Headline Canônica |
|---|---|
growth-marketer |
Growth Marketing Manager | Acquisition, Retention & Experimentation | Google Ads · Meta Ads · GA4 · HubSpot · A/B Testing |
product-marketer |
Product Marketing Manager | Positioning, Launch Strategy & Sales Enablement | Messaging · Competitive Intel · Content · GTM |
seo-specialist |
SEO Specialist | Technical SEO, Content Strategy & Link Building | Ahrefs · Screaming Frog · GSC · GA4 · Schema |
Finanças & Negócios
| Label | Headline Canônica |
|---|---|
financial-analyst |
Financial Analyst | FP&A, Modeling & Strategic Planning | Excel · Power BI · SAP · Bloomberg · SQL |
product-manager |
Product Manager | Discovery, Roadmap & Delivery | Jira · Amplitude · Figma · SQL · OKRs |
management-consultant |
Management Consultant | Strategy, Operations & Digital Transformation | McKinsey 7S · Lean · Excel · PowerPoint |
Engenharia & Indústria
| Label | Headline Canônica |
|---|---|
mechanical-engineer |
Mechanical Engineer | Product Design, FEA & Manufacturing | SolidWorks · AutoCAD · ANSYS · GD&T · Lean Manufacturing |
civil-engineer |
Civil Engineer | Structural Design, Project Management & BIM | AutoCAD · Revit · SAP2000 · MS Project · BIM 360 |
NOTA: Estes são exemplos. O usuário pode definir qualquer cargo — o agente deve adaptar keywords e sugestões ao contexto fornecido.
Formatos Canônicos
Headline LinkedIn
Formato: {Posição} | {Áreas de trabalho mais fortes} | {Ferramentas/Competências com ·}
Regras:
- Exatamente 3 blocos separados por
| - Bloco 1: cargo/posição (com senioridade quando relevante)
- Bloco 2: áreas de domínio / especialidades
- Bloco 3: ferramentas, tecnologias ou competências-chave separadas por
· - Máximo 220 caracteres
Exemplo:
Senior Data Engineer | Data Platform, CDP & Reliability | GCP · Airflow · BigQuery · Spark · Terraform
Bullet de Experiência (LinkedIn)
Formato: Ação + métrica em destaque + ferramentas/tecnologias + impacto para a empresa
Regras:
- ~3 linhas máximo por bullet
- Máximo 5 bullets por experiência
- Iniciar com verbo de ação
- Incluir pelo menos 1 métrica por bullet quando possível
Exemplo:
Productionized a multi-agent AI remediation platform using Google ADK, FastAPI, LLMs, RAG,
Kubernetes, and GitHub-hosted runbooks, resolving ~70% of recurring low-risk KTLO incidents
across Airflow, Dataproc, BigQuery, and Keboola
About LinkedIn
Modelo narrativo:
- Abertura com anos de experiência + foco principal
- Empresa atual com escala/métricas
- Áreas de atuação
- Experiência anterior com provas
- Lista de domínios/competências/stack no final
Tamanho: 1000–2000 caracteres ideal
Exemplo:
I'm a Senior Data Engineer and Cloud Data Architect with 6+ years of experience focused on
helping create and support scalable, reliable, and governed data platforms across GCP and Azure.
At ShopNova, I work on petabyte-scale data platforms supporting 2,000+ pipelines, 500+
Airflow/Composer DAGs, and large-scale GCP workloads. [...]
My strongest areas are Cloud Data Architecture, Data Engineering, Airflow/Composer, GCP, Azure,
BigQuery, Dataproc, Databricks, PySpark, Terraform, Kubernetes, CDP, Observability, and AI
Automation.
Bullet de Currículo (ATS)
Formato: Verbo de ação + resultado quantificado + contexto/ferramenta
Regras:
- 1–2 linhas por bullet
- 4–6 bullets por experiência
- Priorizar impacto mensurável
- Usar verbos de ação fortes (Projetou, Implementou, Reduziu, Automatizou, Liderou, Otimizou)
Exemplo:
Reduziu tempo de processamento de pipelines em 40% ao migrar jobs Spark para Dataproc Serverless com Terraform
Automatizou 200+ DAGs no Airflow/Composer, eliminando 15h/semana de intervenção manual