agent

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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.

⭐3stars🔀4forks📁View on GitHub🕐Updated Dec 31, 2025

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.
nameagent
descriptionBuild 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:

  1. Set temperature: 1, or
  2. Remove the reasoning_effort field
  • tdx-basic - Core CLI operations and context management