# AI Agents and Conversational AI - Skills and Agents Backlog

This backlog identifies specialized skills and agents (subagents) that could enhance the processes in this specialization beyond general-purpose capabilities.

## Overview

- **Total Skills Identified**: 47
- **Total Agents Identified**: 38
- **Shared/Cross-Process Candidates**: 24

---

## Skills Backlog

### LangChain Integration Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-LC-001 | langchain-react-agent | LangChain ReAct agent implementation with tool binding | react-agent-implementation, function-calling-agent |
| SK-LC-002 | langchain-memory | LangChain memory integration (ConversationBufferMemory, ConversationSummaryMemory) | conversational-memory-system, chatbot-design-implementation |
| SK-LC-003 | langchain-retriever | LangChain retriever implementation with various strategies | rag-pipeline-implementation, advanced-rag-patterns |
| SK-LC-004 | langchain-tools | LangChain tool creation and integration utilities | custom-tool-development, function-calling-agent |
| SK-LC-005 | langchain-chains | LangChain chain composition (SequentialChain, RouterChain) | dialogue-flow-design, chatbot-design-implementation |

### LangGraph Workflow Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-LG-001 | langgraph-state-graph | LangGraph StateGraph builder with state schema design | langgraph-workflow-design, multi-agent-system |
| SK-LG-002 | langgraph-checkpoint | LangGraph checkpoint and persistence configuration | langgraph-workflow-design, conversational-memory-system |
| SK-LG-003 | langgraph-hitl | Human-in-the-loop integration for LangGraph workflows | langgraph-workflow-design, tool-safety-validation |
| SK-LG-004 | langgraph-routing | Conditional edge routing and state-based transitions | langgraph-workflow-design, plan-and-execute-agent |
| SK-LG-005 | langgraph-subgraph | Subgraph composition and modular workflow design | multi-agent-system, langgraph-workflow-design |

### RAG Pipeline Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-RAG-001 | rag-chunking-strategy | Document chunking with multiple strategies (semantic, recursive, fixed) | rag-pipeline-implementation, chunking-strategy-design |
| SK-RAG-002 | rag-embedding-generation | Batch embedding generation with caching and rate limiting | rag-pipeline-implementation, vector-database-setup |
| SK-RAG-003 | rag-hybrid-search | Hybrid search combining semantic and keyword retrieval | advanced-rag-patterns, rag-pipeline-implementation |
| SK-RAG-004 | rag-reranking | Cross-encoder reranking and MMR diversity filtering | advanced-rag-patterns, rag-pipeline-implementation |
| SK-RAG-005 | rag-query-transformation | Query expansion, HyDE, and multi-query generation | advanced-rag-patterns, knowledge-base-qa |

### Vector Database Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-VDB-001 | pinecone-integration | Pinecone vector database setup and operations | vector-database-setup, rag-pipeline-implementation |
| SK-VDB-002 | weaviate-integration | Weaviate vector database with GraphQL queries | vector-database-setup, rag-pipeline-implementation |
| SK-VDB-003 | chroma-integration | Chroma local vector database setup and operations | vector-database-setup, rag-pipeline-implementation |
| SK-VDB-004 | qdrant-integration | Qdrant vector database with filtering and payloads | vector-database-setup, rag-pipeline-implementation |
| SK-VDB-005 | milvus-integration | Milvus distributed vector database configuration | vector-database-setup, rag-pipeline-implementation |

### Prompt Engineering Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-PE-001 | prompt-template-design | Structured prompt template creation with variables | prompt-engineering-workflow, system-prompt-guardrails |
| SK-PE-002 | few-shot-example-gen | Few-shot example generation and optimization | prompt-engineering-workflow, intent-classification-system |
| SK-PE-003 | chain-of-thought-prompts | Chain-of-thought and step-by-step reasoning prompts | prompt-engineering-workflow, self-reflection-agent |
| SK-PE-004 | constitutional-ai-prompts | Constitutional AI and safety guardrail prompts | system-prompt-guardrails, content-moderation-safety |
| SK-PE-005 | prompt-compression | Token-efficient prompt compression techniques | cost-optimization-llm, agent-performance-optimization |

### Agent Framework Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-AF-001 | crewai-setup | CrewAI multi-agent orchestration setup | multi-agent-system, plan-and-execute-agent |
| SK-AF-002 | autogen-setup | Microsoft AutoGen multi-agent configuration | multi-agent-system, autonomous-task-planning |
| SK-AF-003 | llamaindex-agent | LlamaIndex agent and query engine setup | rag-pipeline-implementation, knowledge-base-qa |
| SK-AF-004 | semantic-kernel-setup | Microsoft Semantic Kernel planner and skills | function-calling-agent, plan-and-execute-agent |
| SK-AF-005 | haystack-pipeline | Haystack NLP pipeline configuration | rag-pipeline-implementation, intent-classification-system |

### Memory System Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-MEM-001 | zep-memory-integration | Zep memory server integration for long-term memory | conversational-memory-system, long-term-memory-management |
| SK-MEM-002 | mem0-integration | Mem0 (formerly MemGPT) memory layer integration | conversational-memory-system, long-term-memory-management |
| SK-MEM-003 | redis-memory-backend | Redis backend for conversation state persistence | conversational-memory-system, chatbot-design-implementation |
| SK-MEM-004 | memory-summarization | Conversation summarization for memory compression | conversational-memory-system, long-term-memory-management |
| SK-MEM-005 | entity-memory-extraction | Entity and fact extraction for user profiling | long-term-memory-management, conversational-persona-design |

### NLU/Intent Classification Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-NLU-001 | huggingface-classifier | Hugging Face transformer model fine-tuning for intent classification | intent-classification-system, entity-extraction-slot-filling |
| SK-NLU-002 | setfit-few-shot | SetFit few-shot learning for intent classification | intent-classification-system |
| SK-NLU-003 | rasa-nlu-integration | Rasa NLU pipeline configuration and training | intent-classification-system, chatbot-design-implementation |
| SK-NLU-004 | spacy-ner | spaCy NER model training and entity extraction | entity-extraction-slot-filling, chatbot-design-implementation |
| SK-NLU-005 | llm-classifier | LLM-based zero-shot/few-shot classification | intent-classification-system, dialogue-flow-design |

### Safety and Moderation Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-SAF-001 | content-moderation-api | Content moderation API integration (OpenAI, Perspective) | content-moderation-safety, system-prompt-guardrails |
| SK-SAF-002 | guardrails-ai-setup | Guardrails AI validation framework setup | system-prompt-guardrails, prompt-injection-defense |
| SK-SAF-003 | nemo-guardrails | NVIDIA NeMo Guardrails configuration | system-prompt-guardrails, content-moderation-safety |
| SK-SAF-004 | prompt-injection-detector | Prompt injection detection and prevention | prompt-injection-defense, tool-safety-validation |
| SK-SAF-005 | pii-redaction | PII detection and redaction utilities | content-moderation-safety, system-prompt-guardrails |

### Observability Skills

| Skill ID | Skill Name | Description | Applicable Processes |
|----------|------------|-------------|---------------------|
| SK-OBS-001 | langsmith-tracing | LangSmith tracing and debugging setup | llm-observability-monitoring, agent-evaluation-framework |
| SK-OBS-002 | langfuse-integration | LangFuse LLM observability integration | llm-observability-monitoring, cost-optimization-llm |
| SK-OBS-003 | phoenix-arize-setup | Arize Phoenix observability platform setup | llm-observability-monitoring, agent-evaluation-framework |
| SK-OBS-004 | opentelemetry-llm | OpenTelemetry instrumentation for LLM apps | llm-observability-monitoring, agent-deployment-pipeline |

---

## Agents (Subagents) Backlog

### Agent Architecture Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-AA-001 | react-agent-architect | Designs ReAct agent loops with thought-action-observation patterns | react-agent-implementation |
| AG-AA-002 | plan-execute-planner | Creates hierarchical task decomposition and replanning logic | plan-and-execute-agent, autonomous-task-planning |
| AG-AA-003 | multi-agent-coordinator | Orchestrates multi-agent communication and coordination | multi-agent-system |
| AG-AA-004 | langgraph-workflow-designer | Designs stateful LangGraph workflows with cycles and conditionals | langgraph-workflow-design |
| AG-AA-005 | tool-use-specialist | Designs tool schemas and function calling patterns | function-calling-agent, custom-tool-development |

### RAG and Retrieval Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-RAG-001 | rag-pipeline-architect | Designs end-to-end RAG pipeline architecture | rag-pipeline-implementation, advanced-rag-patterns |
| AG-RAG-002 | chunking-strategy-expert | Optimizes document chunking for retrieval quality | chunking-strategy-design, rag-pipeline-implementation |
| AG-RAG-003 | vector-db-specialist | Configures and optimizes vector database deployments | vector-database-setup |
| AG-RAG-004 | retrieval-optimizer | Tunes retrieval parameters and implements reranking | advanced-rag-patterns, rag-pipeline-implementation |
| AG-RAG-005 | knowledge-base-curator | Structures and maintains knowledge base content | knowledge-base-qa, rag-pipeline-implementation |

### Conversational AI Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-CAI-001 | dialogue-designer | Creates conversation flows and dialogue trees | dialogue-flow-design, chatbot-design-implementation |
| AG-CAI-002 | intent-taxonomy-expert | Designs intent hierarchies and classification schemas | intent-classification-system |
| AG-CAI-003 | entity-extraction-specialist | Builds entity extraction and slot filling systems | entity-extraction-slot-filling |
| AG-CAI-004 | conversation-repair-expert | Designs error handling and clarification strategies | dialogue-flow-design, chatbot-design-implementation |
| AG-CAI-005 | persona-designer | Creates chatbot personas and brand voice guidelines | conversational-persona-design, empathetic-response-generation |

### Prompt Engineering Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-PE-001 | system-prompt-engineer | Crafts system prompts with guardrails and safety constraints | system-prompt-guardrails, prompt-engineering-workflow |
| AG-PE-002 | few-shot-curator | Creates and optimizes few-shot examples | prompt-engineering-workflow, intent-classification-system |
| AG-PE-003 | prompt-optimizer | A/B tests and optimizes prompts for performance | prompt-engineering-workflow, ab-testing-conversational |
| AG-PE-004 | cot-reasoning-expert | Designs chain-of-thought and reasoning prompts | prompt-engineering-workflow, self-reflection-agent |

### Memory and State Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-MEM-001 | memory-architect | Designs memory hierarchies and consolidation strategies | conversational-memory-system, long-term-memory-management |
| AG-MEM-002 | user-profile-builder | Extracts and manages user preferences and profiles | long-term-memory-management |
| AG-MEM-003 | semantic-memory-curator | Manages vector-based semantic memory retrieval | conversational-memory-system |
| AG-MEM-004 | state-machine-designer | Creates dialogue state tracking and management | dialogue-flow-design, langgraph-workflow-design |

### Safety and Evaluation Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-SAF-001 | safety-auditor | Reviews agents for safety and alignment issues | content-moderation-safety, bias-detection-fairness |
| AG-SAF-002 | prompt-injection-defender | Implements prompt injection defenses | prompt-injection-defense |
| AG-SAF-003 | bias-fairness-analyst | Audits for bias and implements fairness testing | bias-detection-fairness |
| AG-SAF-004 | agent-evaluator | Designs evaluation frameworks and benchmarks | agent-evaluation-framework, conversation-quality-testing |
| AG-SAF-005 | llm-judge | Implements LLM-as-judge evaluation patterns | agent-evaluation-framework, conversation-quality-testing |

### Deployment and Operations Specialists

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-OPS-001 | agent-deployment-engineer | Configures containerization and deployment pipelines | agent-deployment-pipeline |
| AG-OPS-002 | cost-optimizer | Analyzes and reduces LLM token costs | cost-optimization-llm |
| AG-OPS-003 | latency-optimizer | Optimizes agent response latency | agent-performance-optimization |
| AG-OPS-004 | observability-engineer | Sets up tracing, logging, and monitoring | llm-observability-monitoring |

### Specialized Domain Agents

| Agent ID | Agent Name | Role | Applicable Processes |
|----------|------------|------|---------------------|
| AG-DOM-001 | voice-ai-specialist | Integrates ASR/TTS for voice-enabled agents | voice-enabled-conversational |
| AG-DOM-002 | multimodal-agent-expert | Builds vision-language agent capabilities | multi-modal-agent |
| AG-DOM-003 | fine-tuning-specialist | Manages LLM fine-tuning pipelines | llm-fine-tuning-conversational |
| AG-DOM-004 | synthetic-data-generator | Creates synthetic conversation training data | synthetic-conversation-data |

---

## Process-to-Skills/Agents Mapping

### Agent Architecture Implementation

#### react-agent-implementation
- **Skills**: SK-LC-001, SK-LC-004, SK-PE-003, SK-OBS-001
- **Agents**: AG-AA-001, AG-AA-005, AG-SAF-004

#### plan-and-execute-agent
- **Skills**: SK-LG-004, SK-AF-001, SK-AF-004
- **Agents**: AG-AA-002, AG-MEM-004

#### multi-agent-system
- **Skills**: SK-LG-001, SK-LG-005, SK-AF-001, SK-AF-002
- **Agents**: AG-AA-003, AG-AA-004

#### langgraph-workflow-design
- **Skills**: SK-LG-001, SK-LG-002, SK-LG-003, SK-LG-004, SK-LG-005
- **Agents**: AG-AA-004, AG-MEM-004

### Conversational AI Development

#### chatbot-design-implementation
- **Skills**: SK-LC-002, SK-LC-005, SK-NLU-003, SK-MEM-003
- **Agents**: AG-CAI-001, AG-CAI-004, AG-CAI-005

#### dialogue-flow-design
- **Skills**: SK-LC-005, SK-NLU-005
- **Agents**: AG-CAI-001, AG-CAI-004, AG-MEM-004

#### intent-classification-system
- **Skills**: SK-NLU-001, SK-NLU-002, SK-NLU-003, SK-NLU-005, SK-PE-002
- **Agents**: AG-CAI-002, AG-PE-002

#### entity-extraction-slot-filling
- **Skills**: SK-NLU-001, SK-NLU-004
- **Agents**: AG-CAI-003

### Tool Use and Function Calling

#### function-calling-agent
- **Skills**: SK-LC-001, SK-LC-004, SK-AF-004
- **Agents**: AG-AA-005

#### custom-tool-development
- **Skills**: SK-LC-004
- **Agents**: AG-AA-005

#### tool-safety-validation
- **Skills**: SK-LG-003, SK-SAF-004
- **Agents**: AG-SAF-001, AG-SAF-002

### RAG (Retrieval-Augmented Generation)

#### rag-pipeline-implementation
- **Skills**: SK-RAG-001, SK-RAG-002, SK-RAG-003, SK-RAG-004, SK-VDB-001, SK-VDB-002, SK-VDB-003, SK-LC-003, SK-AF-003
- **Agents**: AG-RAG-001, AG-RAG-002, AG-RAG-004

#### advanced-rag-patterns
- **Skills**: SK-RAG-003, SK-RAG-004, SK-RAG-005, SK-LC-003
- **Agents**: AG-RAG-001, AG-RAG-004

#### vector-database-setup
- **Skills**: SK-VDB-001, SK-VDB-002, SK-VDB-003, SK-VDB-004, SK-VDB-005, SK-RAG-002
- **Agents**: AG-RAG-003

#### chunking-strategy-design
- **Skills**: SK-RAG-001
- **Agents**: AG-RAG-002

### Memory Systems

#### conversational-memory-system
- **Skills**: SK-LC-002, SK-MEM-001, SK-MEM-002, SK-MEM-003, SK-MEM-004, SK-LG-002
- **Agents**: AG-MEM-001, AG-MEM-003

#### long-term-memory-management
- **Skills**: SK-MEM-001, SK-MEM-002, SK-MEM-004, SK-MEM-005
- **Agents**: AG-MEM-001, AG-MEM-002

### Prompt Engineering and Optimization

#### prompt-engineering-workflow
- **Skills**: SK-PE-001, SK-PE-002, SK-PE-003, SK-PE-005
- **Agents**: AG-PE-001, AG-PE-002, AG-PE-003, AG-PE-004

#### system-prompt-guardrails
- **Skills**: SK-PE-001, SK-PE-004, SK-SAF-001, SK-SAF-002, SK-SAF-003
- **Agents**: AG-PE-001, AG-SAF-001

### Safety and Alignment

#### content-moderation-safety
- **Skills**: SK-SAF-001, SK-SAF-003, SK-SAF-005, SK-PE-004
- **Agents**: AG-SAF-001, AG-SAF-003

#### prompt-injection-defense
- **Skills**: SK-SAF-002, SK-SAF-004
- **Agents**: AG-SAF-002

#### bias-detection-fairness
- **Skills**: None specific (general ML fairness tools)
- **Agents**: AG-SAF-001, AG-SAF-003

### Evaluation and Testing

#### agent-evaluation-framework
- **Skills**: SK-OBS-001, SK-OBS-003
- **Agents**: AG-SAF-004, AG-SAF-005

#### conversation-quality-testing
- **Skills**: SK-OBS-001, SK-OBS-002
- **Agents**: AG-SAF-004, AG-SAF-005

#### ab-testing-conversational
- **Skills**: SK-OBS-002
- **Agents**: AG-PE-003

#### regression-testing-agent
- **Skills**: SK-OBS-001
- **Agents**: AG-SAF-004

### Deployment and Operations

#### agent-deployment-pipeline
- **Skills**: SK-OBS-004
- **Agents**: AG-OPS-001

#### llm-observability-monitoring
- **Skills**: SK-OBS-001, SK-OBS-002, SK-OBS-003, SK-OBS-004
- **Agents**: AG-OPS-004

#### cost-optimization-llm
- **Skills**: SK-PE-005, SK-OBS-002
- **Agents**: AG-OPS-002

#### agent-performance-optimization
- **Skills**: SK-PE-005
- **Agents**: AG-OPS-003

### Voice and Multi-Modal

#### voice-enabled-conversational
- **Skills**: None specific (voice API integrations)
- **Agents**: AG-DOM-001

#### multi-modal-agent
- **Skills**: None specific (vision-language model APIs)
- **Agents**: AG-DOM-002

### Fine-Tuning and Customization

#### llm-fine-tuning-conversational
- **Skills**: SK-NLU-001
- **Agents**: AG-DOM-003

#### synthetic-conversation-data
- **Skills**: SK-PE-002
- **Agents**: AG-DOM-004, AG-PE-002

### Persona and Personality

#### conversational-persona-design
- **Skills**: SK-MEM-005
- **Agents**: AG-CAI-005

#### empathetic-response-generation
- **Skills**: None specific (sentiment/emotion APIs)
- **Agents**: AG-CAI-005

### Agent-Specific Patterns

#### self-reflection-agent
- **Skills**: SK-PE-003
- **Agents**: AG-PE-004

#### autonomous-task-planning
- **Skills**: SK-AF-002
- **Agents**: AG-AA-002

#### knowledge-base-qa
- **Skills**: SK-RAG-005, SK-AF-003
- **Agents**: AG-RAG-005

---

## Shared/Cross-Process Candidates

The following skills and agents are applicable across multiple processes and should be prioritized for implementation:

### High-Priority Shared Skills

| Skill ID | Skill Name | Process Count | Priority |
|----------|------------|---------------|----------|
| SK-LC-001 | langchain-react-agent | 2 | High |
| SK-LC-002 | langchain-memory | 3 | High |
| SK-LC-004 | langchain-tools | 3 | High |
| SK-LG-001 | langgraph-state-graph | 2 | High |
| SK-RAG-001 | rag-chunking-strategy | 2 | High |
| SK-RAG-003 | rag-hybrid-search | 2 | High |
| SK-VDB-001 | pinecone-integration | 2 | High |
| SK-PE-001 | prompt-template-design | 2 | High |
| SK-OBS-001 | langsmith-tracing | 5 | Critical |
| SK-SAF-001 | content-moderation-api | 2 | High |
| SK-MEM-001 | zep-memory-integration | 2 | High |
| SK-NLU-001 | huggingface-classifier | 3 | High |

### High-Priority Shared Agents

| Agent ID | Agent Name | Process Count | Priority |
|----------|------------|---------------|----------|
| AG-AA-005 | tool-use-specialist | 3 | High |
| AG-RAG-001 | rag-pipeline-architect | 3 | High |
| AG-CAI-001 | dialogue-designer | 2 | High |
| AG-PE-001 | system-prompt-engineer | 2 | High |
| AG-PE-002 | few-shot-curator | 3 | High |
| AG-MEM-001 | memory-architect | 2 | High |
| AG-MEM-004 | state-machine-designer | 3 | High |
| AG-SAF-001 | safety-auditor | 4 | Critical |
| AG-SAF-004 | agent-evaluator | 4 | Critical |
| AG-SAF-005 | llm-judge | 2 | High |

---

## Implementation Recommendations

### Phase 1: Core Infrastructure Skills (Immediate)
1. **SK-OBS-001** (langsmith-tracing) - Critical for debugging all agent processes
2. **SK-LC-001** (langchain-react-agent) - Foundation for agent implementations
3. **SK-LG-001** (langgraph-state-graph) - Foundation for workflow processes
4. **SK-RAG-001** (rag-chunking-strategy) - Foundation for RAG processes

### Phase 2: Safety and Quality Agents (Short-term)
1. **AG-SAF-001** (safety-auditor) - Critical for all production agents
2. **AG-SAF-004** (agent-evaluator) - Essential for quality assurance
3. **AG-PE-001** (system-prompt-engineer) - Core for prompt-based processes

### Phase 3: Specialized Integration Skills (Medium-term)
1. **SK-VDB-001/002/003** (vector database integrations) - As RAG processes mature
2. **SK-MEM-001/002** (memory integrations) - As memory systems mature
3. **SK-AF-001/002** (multi-agent frameworks) - As multi-agent processes mature

### Phase 4: Domain-Specific Agents (Long-term)
1. **AG-DOM-001** (voice-ai-specialist) - When voice features needed
2. **AG-DOM-002** (multimodal-agent-expert) - When vision features needed
3. **AG-DOM-003** (fine-tuning-specialist) - When custom models needed

---

## External Tool/Service Dependencies

### LLM Providers
- OpenAI API (GPT-4, GPT-3.5)
- Anthropic API (Claude 3)
- Azure OpenAI Service
- Google Gemini API

### Vector Databases
- Pinecone
- Weaviate
- Chroma (local)
- Qdrant
- Milvus

### Memory Services
- Zep
- Mem0 (MemGPT)
- Redis

### Observability Platforms
- LangSmith
- LangFuse
- Arize Phoenix

### Safety/Moderation APIs
- OpenAI Moderation API
- Perspective API
- LlamaGuard

### NLU/NLP Services
- Hugging Face Inference API
- spaCy
- Rasa

---

## Notes

1. **Skill vs Agent Decision**: Skills are deterministic, tool-like capabilities that can be composed. Agents are autonomous actors with reasoning capabilities that can make decisions.

2. **Reusability Priority**: Skills and agents marked as "shared" should be implemented with reusability in mind, including clear interfaces and configuration options.

3. **External Dependencies**: Many skills depend on external services (LLM APIs, vector databases). Implementations should handle authentication, rate limiting, and error recovery.

4. **Version Compatibility**: Skills integrating with frameworks like LangChain or LangGraph should track version compatibility, as these libraries evolve rapidly.

5. **Testing Requirements**: Each skill/agent should include unit tests and integration tests before being considered production-ready.
