agent
Build LLM agents using `tdx agent pull/push` with YAML/Markdown config. Covers agent.yml structure, tools (knowledge_base, agent, web_search, image_gen), @ref syntax, and knowledge bases. Use for TD AI agent development workflow.
When & Why to Use This Skill
This Claude skill provides a comprehensive CLI-based framework for building, managing, and deploying LLM agents. It simplifies the AI development workflow by using structured YAML and Markdown files for configuration, enabling developers to integrate knowledge bases, web search, and advanced reasoning capabilities with professional-grade version control and deployment commands.
Use Cases
- Local-to-Cloud Workflow: Edit agent configurations, system prompts, and starter messages in a local IDE and synchronize them with the TD platform using pull/push commands.
- RAG and Knowledge Integration: Connect agents to structured table-based databases or unstructured Markdown files to provide context-aware responses using Retrieval-Augmented Generation.
- Multi-Agent Orchestration: Define complex workflows where agents can call other specialized agents as tools, facilitating modular and scalable AI architectures.
- Advanced Reasoning Configuration: Fine-tune agent performance by enabling 'Extended Thinking' (reasoning_effort) and managing model parameters like temperature for complex problem-solving tasks.
- CLI-Based Testing and Debugging: Rapidly iterate on agent behavior by testing prompts and tool outputs directly from the terminal using the integrated chat interface.
| name | agent |
|---|---|
| description | Build LLM agents using `tdx agent pull/push` with YAML/Markdown config. Covers agent.yml structure, tools (knowledge_base, agent, web_search, image_gen), @ref syntax, and knowledge bases. Use for TD AI agent development workflow. |
tdx Agent - LLM Agent Development
Build and manage LLM agents using tdx agent pull/push with YAML/Markdown configuration files.
Key Commands
# Pull project to local files (creates agents/{project}/)
tdx agent pull "My LLM Project"
tdx agent pull "My LLM Project" "Agent Name" # Single agent
# Push local changes to TD
tdx agent push # Push all from current dir
tdx agent push ./agents/my-project/my-agent/ # Push single agent
tdx agent push --dry-run # Preview changes
# Clone project (for staging/production deployment)
tdx agent clone "Source Project" --name "New Project"
tdx agent clone ./agents/my-project/ --name "Prod" --profile production
# List/show agents
tdx agents # List in current project
tdx agent show "Agent Name"
# Test agents with chat
tdx chat --agent "project/Agent Name" "Your message"
tdx chat --new --agent "project/Agent Name" "Start new conversation"
Folder Structure
agents/{project-name}/
├── tdx.json # {"llm_project": "Project Name"}
├── {agent-name}/
│ ├── agent.yml # Agent configuration
│ ├── prompt.md # System prompt (markdown)
│ └── starter_message.md # Optional multiline starter
├── knowledge_bases/
│ ├── {name}.yml # Table-based KB (TD database)
│ └── {name}.md # Text-based KB (plain text)
└── prompts/
└── {name}.yml
agent.yml
name: Support Agent
model: claude-4-sonnet # claude-4-sonnet, claude-4-haiku
temperature: 1 # REQUIRED: must be 1 when reasoning_effort is set
max_tool_iterations: 5
reasoning_effort: medium # none, minimal, low, medium, high (requires temperature: 1)
starter_message: Hello! How can I help?
tools:
- type: knowledge_base
target: '@ref(type: "knowledge_base", name: "support-kb")'
target_function: SEARCH # SEARCH, LOOKUP, READ_TEXT, LIST_COLUMNS
function_name: search_kb
function_description: Search support knowledge base
- type: agent
target: '@ref(type: "agent", name: "sql-expert")'
target_function: CHAT
function_name: ask_sql_expert
function_description: Ask SQL expert for help
output_mode: RETURN # RETURN (default) or SHOW
- type: web_search
target: '@ref(type: "web_search_tool", name: "web-search")'
target_function: SEARCH
function_name: search_web
function_description: Search the web
- type: image_gen
target: '@ref(type: "image_generator", name: "image-gen")'
target_function: TEXT_TO_IMAGE
function_name: generate_image
function_description: Generate an image
variables:
- name: customer_context
target_knowledge_base: '@ref(type: "knowledge_base", name: "customers")'
target_function: LOOKUP
function_arguments: '{"query": "{{customer_id}}"}'
outputs:
- name: resolution_status
function_name: get_status
function_description: Get resolution status
json_schema: '{"type": "object", "properties": {"status": {"type": "string"}}}'
Reference Syntax
All cross-resource references use @ref(...):
'@ref(type: "knowledge_base", name: "my-kb")'
'@ref(type: "agent", name: "my-agent")'
'@ref(type: "prompt", name: "my-prompt")'
'@ref(type: "web_search_tool", name: "web-search")'
'@ref(type: "image_generator", name: "image-gen")'
Knowledge Bases
Table-based (.yml) - Queries TD database
name: Product Catalog
database: ecommerce_db
tables:
- name: products
td_query: select * from products
enable_data: true
enable_data_index: true
Text-based (.md) - Plain text content
---
name: Company FAQ
---
# Frequently Asked Questions
## Return Policy
We offer 30-day returns...
Prompts
name: greeting-prompt
agent: '@ref(type: "agent", name: "support-agent")'
system_prompt: |
Generate a personalized greeting...
template: |
Customer: {{customer_name}}
Typical Workflow
# 1. Pull project
tdx agent pull "My Project"
# 2. Edit files locally (agent.yml, prompt.md, knowledge bases)
# 3. Preview changes
tdx agent push --dry-run
# 4. Push to TD
tdx agent push
# 5. Test with tdx chat
tdx chat --agent "My Project/My Agent" "Hello, test message"
Testing Agents
Use tdx chat to test agents from the command line:
# Basic chat
tdx chat --agent "project-name/Agent Name" "Your question here"
# Start new conversation (clears history)
tdx chat --new --agent "project-name/Agent Name" "Fresh start"
# Continue existing conversation
tdx chat --agent "project-name/Agent Name" "Follow-up question"
Extended Thinking (Reasoning)
To enable extended thinking/reasoning, you must set temperature: 1:
# With reasoning enabled
model: claude-4-sonnet
temperature: 1 # REQUIRED when using reasoning_effort
reasoning_effort: medium # none, minimal, low, medium, high
# Without reasoning (flexible temperature)
model: claude-4-sonnet
temperature: 0.7 # Can be any value 0-1
# reasoning_effort: omit or set to none
Note: If you get the error temperature may only be set to 1 when thinking is enabled, either:
- Set
temperature: 1, or - Remove the
reasoning_effortfield
Related Skills
- tdx-basic - Core CLI operations and context management