ai-learning-guide-with-projects
This chapter teaches domain-specific prompting - embedding industry knowledge, terminology, and workflows into prompts. Students learn to create expert personas and build specialized AI systems for specific domains.
Install / Use
npx skills add Nillkabbo/ai-learning-guide-with-projectsInstalls into whichever agent you are using.
.cursorrules
Cursor IDE rules (legacy)
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of ai-learning-guide-with-projects
ai-learning-guide-with-projects scores 58/100 on our quality scale, 2740th of 2,868 Automation skills we index.
Its .cursorrules is 2.4 KB long, well organised into 14 sections with 2 code examples: moderately detailed.
It has no GitHub stars yet, so there is no community track record; judge it on its content.
Maintenance, license and trust
- We could not determine when the repository was last updated.
- Our last check on 2026-10-17 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 68/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
ai-learning-guide-with-projects compared with similar skills
All 4 of these similar skills score higher than ai-learning-guide-with-projects; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| ai-learning-guide-with-projects (this skill)by Nillkabbo | 58 | 0 | — | .cursorrules |
| Agent-Reachby Panniantong | 100 | 109.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 97.9k | today | MCP Server |
| rufloby ruvnet | 100 | 81.0k | today | MCP Server |
| openclawby thedotmack | 100 | 107.6k | 5d ago | SKILL.md |
Frequently asked questions
- How do I install ai-learning-guide-with-projects?
- Run
npx skills add Nillkabbo/ai-learning-guide-with-projects. The install tabs above show the steps for each supported agent. - Which AI agents does ai-learning-guide-with-projects work with?
- It is written for Cursor and Zed, as a .cursorrules file. Other agents that read the same format can often use it too.
- Is ai-learning-guide-with-projects safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 68/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is ai-learning-guide-with-projects still maintained?
- We could not determine when the repository was last updated.
Skill content
View source on GitHubChapter 12: Domain-Specific Prompting - Cursor Rules
Chapter Context
This chapter teaches domain-specific prompting - embedding industry knowledge, terminology, and workflows into prompts. Students learn to create expert personas and build specialized AI systems for specific domains.
Key Concepts
1. Generic vs. Domain-Specific
- Generic: general knowledge, surface-level
- Domain-specific: expert knowledge, deep insights
- Context and terminology matter
- Domain workflows important
2. Expert Personas
- Define expert role and experience
- Specialization areas
- Domain knowledge
- Professional tone
- Industry standards
3. Domain Documentation
- Technical documentation
- API documentation
- User guides
- Domain-specific formats
- Industry standards
4. Domain Code Generation
- Industry patterns
- Best practices
- Domain constraints
- Code standards
- Framework conventions
5. Domain Code Review
- Domain-specific checks
- Industry standards
- Security considerations
- Performance requirements
- Compliance requirements
Important Code Patterns
Domain Expert Persona
domain_expert = """
You are a senior IoT engineer with 15 years of experience in
industrial automation. You specialize in:
- Predictive maintenance systems
- Sensor calibration and diagnostics
- Network protocols (MQTT, CoAP, LoRaWAN)
- Safety-critical systems (IEC 61508)
When analyzing issues, consider:
- Safety implications first
- Production uptime impact
- Cost-effectiveness
- Harsh operating environments
"""
Domain-Specific Analysis
prompt = f"""
As {domain_expert}, analyze this IoT issue:
{problem_description}
Provide analysis focusing on:
- Industry-specific KPIs
- Safety and regulatory implications
- Operational efficiency
- Actionable insights for IoT context
"""
Common Mistakes to Avoid
- Too generic: Always add domain context
- Wrong persona: Match expert to task
- Missing terminology: Use domain language
- No workflows: Include domain processes
- Ignoring standards: Follow industry standards
Integration Points
- Chapters 9-11: Applies prompting strategies to domains
- Chapter 13: Agents can be domain-specific
- Chapter 17: Web apps for domain use cases
Related Chapters
- Chapters 9-11: Prompting strategies (applied to domains)
- Chapter 13: Agents (can be domain-specific)
- Chapter 17: Web applications (domain interfaces)
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.