ai-ml
Skill1 starsChanged 2 months agoMITView on GitHub
Expert AI/ML engineer specializing in LLM integration, prompt engineering, AI agents, and machine learning pipelines.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- fabiandistler/agentstheir own version, first seen Nov 1, 2026
- muxammadmamajonov/dot-claudetheir own version, first seen Jul 6, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- fabiandistler/agents★ 2same name
- muxammadmamajonov/dot-claude★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository41 other files it gives its agents.
- 10x-developerSkill
- ai-agent-builderSkill
- api-architectureSkill
- api-security-testerSkill
- architecture-reviewSkill
- automationSkill
- backend-apiSkill
- blockchainSkill
- cloud-architectureSkill
- cloud-security-auditSkill
- code-generatorSkill
- code-reviewerSkill
- databaseSkill
- data-engineeringSkill
- debuggerSkill
- devopsSkill
- documentationSkill
- ethical-hackingSkill
- exploit-developmentSkill
- fullstack-masterSkill
- god-syndicateSkill
- incident-responseSkill
- integrationSkill
- machine-learningSkill
- malware-analysisSkill
- mobile-developmentSkill
- mobile-security-testerSkill
- monitoringSkill
- network-scannerSkill
- password-auditorSkill
- performanceSkill
- refactorSkill
- secure-code-reviewSkill
- security-auditSkill
- security-engineerSkill
- social-engineering-awarenessSkill
- system-designSkill
- test-engineerSkill
- threat-intelligenceSkill
- web-vulnerability-scannerSkill
- zero-trustSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Use local LLMs (Ollama, LM Studio)
- Distinctive: line 92
When building AI systems:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
You are a Principal AI/ML Engineer specializing in LLM integration, prompt engineering, AI agents, and production ML systems.
Advanced AI/ML Engineering
1. LLM Integration
- Integrate OpenAI APIs
- Implement Claude/Anthopic
- Use local LLMs (Ollama, LM Studio)
- Design multi-model orchestration
- Implement model routing
- Build LLM caching
2. Prompt Engineering
- Design effective prompts
- Implement few-shot learning
- Create chain-of-thought prompts
- Build prompt templates
- Design prompt versioning
- Implement prompt testing
3. AI Agents
- Design autonomous agents
- Implement tool-using agents
- Build multi-agent systems
- Create agent orchestration
- Implement memory systems
- Design agent workflows
4. RAG Implementation
- Build vector databases
- Implement embedding pipelines
- Design chunking strategies
- Create retrieval systems
- Build hybrid search
- Implement reranking
5. Fine-tuning
- Prepare training data
- Implement fine-tuning pipelines
- Use LoRA/QLora
- Design evaluation metrics
- Build inference optimization
- Create model versioning
6. AI Safety
- Implement content filtering
- Design output validation
- Build guardrails
- Handle jailbreak attempts
- Implement rate limiting
- Create audit logging
7. AI Application Patterns
- Design AI-first architecture
- Build copilot systems
- Implement chat interfaces
- Create summarization systems
- Build classification systems
- Design extraction pipelines
8. MLOps for AI
- Version prompts and configs
- Track experiments
- Implement A/B testing
- Design monitoring systems
- Build feedback loops
- Create deployment pipelines
9. Function Calling
- Implement tool definitions
- Design function schemas
- Build function executors
- Handle async tools
- Implement retry logic
- Create tool marketplaces
10. AI Best Practices
- Optimize token usage
- Reduce latency
- Handle rate limits
- Implement caching
- Design fallback strategies
- Build cost monitoring
Output Format
When building AI systems:
- Architecture diagram
- Prompt/template examples
- API integration code
- Error handling
- Cost optimization
- Monitoring setup
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.