AI Studio · AI Skills Map
Every AI skill, one map
What to learn first, the full catalogue of AI skills, and exactly where to learn each one on SkillNavora, plus the skills employers ask for in AI-era job descriptions.
How the pieces fit: a skill is a reusable method; a tool gives access or performs an action; evals, memory and context management are parts of the system around them. They work together. There's no fixed list of "all AI skills": you can create a new skill for any repeatable task.
Learn these first, in this order
Seven skills that make you effective with AI at work, each linked to its SkillNavora course.
- 1Prompt and context engineering
Make requests precise and evidence-based.
Prompt & Context Engineering · Prompt engineering in depth - 2Skill and template creation
Standardise your recurring PM work.
AI Skills & Templates · PRD library - 3Second brain and retrieval
Reuse your learning material and project knowledge.
AI Second Brain · RAG lesson - 4Research and data analysis
Turn information into supported decisions.
Data Science & AI handbook · AI for analysts - 5Evals
Check whether outputs meet your standards.
AI Evals · Evaluating LLM apps - 6API and browser automation
Connect the assistant to real workflows.
Browser Automation & AI Agents · APIs without code - 7Orchestration and monitoring
Run longer workflows reliably.
Agent Orchestration & Monitoring · n8n tutorial
The full AI skills catalogue (77 capabilities)
Every capability, and where to learn it on SkillNavora.
1. Instructions, context and knowledge
| Capability | Learn it on SkillNavora |
|---|---|
| Prompt engineering | Prompt & Context Engineering · Prompt engineering lesson · PM handbook: Claude for PMs |
| Context engineering | Prompt & Context Engineering · Second Brain: class 5 |
| Skill creation | AI Skills & Templates |
| Template creation | AI Skills & Templates · PRD library · BRD & FRD guide |
| Structured output | Prompt & Context Engineering · LLM APIs: structured output |
| Task decomposition | Prompt & Context Engineering · Agent Orchestration & Monitoring |
| Context compression | Prompt & Context Engineering |
| Document ingestion | Prompt & Context Engineering |
| OCR | Prompt & Context Engineering |
| Knowledge organisation | AI Second Brain |
| Semantic search | Embeddings & vector search |
| RAG | RAG lesson · PM handbook: LLMs & RAG |
| Source attribution | Prompt & Context Engineering · AI Evals |
| Persistent memory | AI Second Brain |
| Second-brain management | AI Second Brain |
| Knowledge graphs | Prompt & Context Engineering |
2. Research and analysis
| Capability | Learn it on SkillNavora |
|---|---|
| Web search & multi-source research | Browser Automation & AI Agents |
| Source credibility & fact checking | Browser Automation & AI Agents · Prompt & Context Engineering |
| Competitive analysis | AI PM Toolkit |
| Market research & sizing | AI PM Toolkit · PM handbook: discovery & research |
| Literature review | AI Second Brain |
| Customer-feedback & interview synthesis | AI PM Toolkit · AI Second Brain |
| Survey analysis | Statistics lessons |
| Quantitative data analysis | Python EDA · Statistics |
| SQL analysis | SQL track · Practice Lab |
| Spreadsheet analysis | Excel track · AI in Excel |
| Experiment analysis | Hypothesis testing · AI PM Toolkit |
| Scenario analysis | AI PM Toolkit |
3. Web browsing and action automation
| Capability | Learn it on SkillNavora |
|---|---|
| Browser navigation, extraction, forms, downloads, uploads | Browser Automation & AI Agents |
| Multi-step website workflows | Browser Automation & AI Agents |
| Browser testing & screenshot comparison | Browser Automation & AI Agents |
| Desktop & file automation | Browser Automation & AI Agents |
| API integration | APIs without code · LLM APIs from Python |
| Tool calling | Tool use & function calling |
| MCP integration | MCP lesson · MCP without code |
| Scheduled & event-driven automation | Agent Orchestration & Monitoring · n8n tutorial · Connect apps |
| Monitoring & alerts | Agent Orchestration & Monitoring |
| Approval workflows | Agent Orchestration & Monitoring · Browser Automation & AI Agents |
4. Evals, testing and reliability
| Capability | Learn it on SkillNavora |
|---|---|
| Eval datasets, rubrics, grading | AI Evals |
| Human & AI-assisted grading | AI Evals |
| Factuality & retrieval evaluation | AI Evals · RAG lesson |
| Instruction-following & tool-use evaluation | AI Evals |
| Task-completion & browser-agent evaluation | AI Evals · Browser Automation & AI Agents |
| Visual evaluation | AI Skills & Templates |
| Regression & robustness testing | AI Evals |
| Prompt-injection & permission-boundary testing | AI Evals · Browser Automation & AI Agents |
| Cost & latency evaluation | AI Evals · Agent Orchestration & Monitoring |
| Trace analysis & production monitoring | AI Evals · Agent Orchestration & Monitoring |
5. Agent coordination and workflow design
| Capability | Learn it on SkillNavora |
|---|---|
| Workflow orchestration, routing | Agent Orchestration & Monitoring |
| Planning, replanning, state, checkpointing | Agent Orchestration & Monitoring |
| Parallel execution & multi-agent coordination | Agent Orchestration & Monitoring · AI agents lesson |
| Human-in-the-loop & error recovery | Agent Orchestration & Monitoring |
| Caching, model selection, cost management | Agent Orchestration & Monitoring |
6. Writing, design and media
| Capability | Learn it on SkillNavora |
|---|---|
| Summarisation, business writing, editing | Prompt & Context Engineering · PM handbook: communication |
| Translation & localisation | Prompt & Context Engineering |
| Document & presentation generation | AI Skills & Templates |
| Data visualisation | Visualisation lessons · Power BI |
| UI/UX analysis & interface design | Figma tutorial · PM handbook: UX |
| Image, audio, video and speech | Content creation guide · Voice agents · Learning sites: AI media tools |
7. Software and data engineering
| Capability | Learn it on SkillNavora |
|---|---|
| Code generation, debugging, review, refactoring, tests | Claude Code tutorial · PM handbook: Claude Code · AI coding assistants |
| Documentation & API development | Shipping a GenAI app |
| Database design & data pipelines | ER diagrams · Python & SQL |
| Deployment, security review, dependencies | PM handbook: Claude Code · Shipping a GenAI app |
8. Product-management skills you can package
| Capability | Learn it on SkillNavora |
|---|---|
| Problem framing, personas, JTBD, journeys | AI PM Toolkit · PM handbook: problem framing |
| PRD creation | AI PRD Writer · PRD library · AI Skills & Templates |
| Prioritisation & roadmaps | AI PM Toolkit · PM handbook: strategy |
| Metrics design & experiment planning | AI PM Toolkit · PM handbook: analytics |
| Product teardown & launch planning | AI PM Toolkit · PM handbook: launch & growth |
| Stakeholder communication, decision logs, weekly reports | AI PM Toolkit · AI Second Brain · AI Skills & Templates |
9. Advanced AI product management
| Capability | Learn it on SkillNavora |
|---|---|
| AI product strategy, moats, data flywheels, build vs buy | AI Product Strategy |
| AI UX: trust, explainability, feedback, failure design | Designing AI Experiences |
| Pricing, packaging and unit economics of AI | Pricing & Monetising AI |
| Recommendation, classification and ML lifecycle | ML Products for PMs |
| AI governance, regulation (EU AI Act, DPDP), risk assessment | AI Governance & Responsible AI |
| Platforms, APIs, developer experience, ecosystems | Platform & API PM |
| Product operations: rituals, tools, insight pipelines | Product Operations |
| Multimodal AI: vision, image, voice, video, deepfake safety | Multimodal AI & AI Media |
What AI-era job descriptions ask for
Skills that commonly appear in job postings for these roles, and where to learn each.
AI Product Manager
- Define AI product strategy, use cases and success metrics
- Write PRDs for LLM/RAG/agent features with evals and guardrails
- Work with data science on model choice, cost and latency trade-offs
- Prompt and context design; prototyping with AI tools
- Experimentation (A/B tests) and product analytics (SQL, Mixpanel/Amplitude)
- Responsible AI: privacy, safety, bias
- Stakeholder communication and roadmaps
Data / AI Analyst
- SQL, Excel and Python for analysis
- Dashboards (Power BI / Tableau)
- Statistics and experiment analysis
- Using GenAI for analysis, text-to-SQL and automation
- Communicating insights to stakeholders
GenAI / AI Automation Specialist
- Build workflows with n8n, Make, Zapier and LLM APIs
- RAG, tool calling and MCP integrations
- Agents with approvals and monitoring
- Evaluating and improving AI outputs
- Browser and API automation
Business Analyst (AI-era)
- Requirements: BRD, FRD, user stories, acceptance criteria
- Process mapping and gap analysis
- Data analysis with SQL/Excel/Power BI
- Using AI for documentation, research and synthesis
