Practical Examples

Real-world examples showing how to use TachiBot tools and workflows effectively. From simple tool calls to complex multi-step orchestrations.

Using Tools Directly (No Workflows)

You can call any TachiBot tool directly from Claude Code without creating a workflow. Just use natural language:

Example 1: Simple Research

Ask Claude to use perplexity_ask tool:

Example 2: Code Analysis

Direct code review with qwen_coder:

Multi-Tool Chains

Chain multiple tools together for more powerful analysis. Claude can orchestrate this automatically:

Research → Validate → Analyze → Save

Complete analysis pipeline in natural language:

Multi-Model Consensus

Get opinions from 3 different models:

Running Workflows

Workflows are pre-built multi-step processes. TachiBot includes 11 ready-to-use workflows:

How to Run a Workflow

Available Workflows

ultra-creative-brainstorm

18 steps, 14 techniques

Comprehensive creative ideation pipeline

code-review

7 steps, parallel execution

Multi-perspective code analysis

iterative-problem-solver

15 steps, conditional

Research → Analyze → Solve with refinement

code-architecture-review

7 steps

Systematic architecture analysis

accessibility-code-audit

Multiple steps

WCAG compliance checking

pingpong

8 steps, debate

Multi-model debate and refinement

System Workflows (Advanced)

High-capacity workflows for large-scale analysis:

verifier

~58k tokens

Multi-model consensus verification (7 steps)

scout

~90k tokens

Multi-source information gathering (6 steps)

challenger

~64k tokens

Devil's advocate critical analysis (6 steps)

Finding & Reading Workflow Outputs

Output Directory Structure

All workflow outputs are saved to workflow-output/ directory:

📁 File Naming Convention

{stepNumber}-{stepName}-{modelName}-{YYYY-MM-DD-HH-MM-SS-DayName}.md

Example: 3-brainstorm-ideas-gpt-5-2025-11-14-16-32-05-Thursday.md

Configuration

Override default output directory in your .env file:

Reading Results

1. Check Manifest.json

Contains execution summary and metadata:

2. Read Individual Step Outputs

3. Debugging Failed Workflows

Use jq to find errors in manifest.json:

Real-World Use Cases

Use Case 1: Analyzing a GitHub Issue

Multi-tool analysis pipeline:

Use Case 2: Technical Decision Making

Compare database options:

Use Case 3: Code Refactoring Plan

Comprehensive refactoring strategy:

Advanced Patterns

Parallel Execution

Run multiple tools simultaneously for faster results:

Conditional Execution

Execute steps based on previous results:

File-Based Chaining (Large Outputs)

Handle outputs larger than 1MB by saving to disk:

Prompt Engineering Integration

Use 14 research-backed techniques for better reasoning:

Related Resources

All Tools ReferenceComplete documentation for all 31 tools Workflows GuideLearn to create custom workflows Tool ProfilesPre-configured tool sets Tools ConfigurationCustomize which tools are enabled

💡 Pro Tip

Start with direct tool calls to understand what each tool does. Once you find a pattern you use often, convert it to a workflow for repeatability. Use the create_workflow tool to generate YAML from your natural language description!

Practical Examples

Real-world examples showing how to use TachiBot tools and workflows effectively. From simple tool calls to complex multi-step orchestrations.

Using Tools Directly (No Workflows)

You can call any TachiBot tool directly from Claude Code without creating a workflow. Just use natural language:

Example 1: Simple Research

Ask Claude to use perplexity_ask tool:

Example 2: Code Analysis

Direct code review with qwen_coder:

Multi-Tool Chains

Chain multiple tools together for more powerful analysis. Claude can orchestrate this automatically:

Research → Validate → Analyze → Save

Complete analysis pipeline in natural language:

Multi-Model Consensus

Get opinions from 3 different models:

Running Workflows

Workflows are pre-built multi-step processes. TachiBot includes 11 ready-to-use workflows:

How to Run a Workflow

Available Workflows

ultra-creative-brainstorm

18 steps, 14 techniques

Comprehensive creative ideation pipeline

code-review

7 steps, parallel execution

Multi-perspective code analysis

iterative-problem-solver

15 steps, conditional

Research → Analyze → Solve with refinement

code-architecture-review

7 steps

Systematic architecture analysis

accessibility-code-audit

Multiple steps

WCAG compliance checking

pingpong

8 steps, debate

Multi-model debate and refinement

System Workflows (Advanced)

High-capacity workflows for large-scale analysis:

verifier

~58k tokens

Multi-model consensus verification (7 steps)

scout

~90k tokens

Multi-source information gathering (6 steps)

challenger

~64k tokens

Devil's advocate critical analysis (6 steps)

Finding & Reading Workflow Outputs

Output Directory Structure

All workflow outputs are saved to workflow-output/ directory:

📁 File Naming Convention

{stepNumber}-{stepName}-{modelName}-{YYYY-MM-DD-HH-MM-SS-DayName}.md

Example: 3-brainstorm-ideas-gpt-5-2025-11-14-16-32-05-Thursday.md

Configuration

Override default output directory in your .env file:

Reading Results

1. Check Manifest.json

Contains execution summary and metadata:

2. Read Individual Step Outputs

3. Debugging Failed Workflows

Use jq to find errors in manifest.json:

Real-World Use Cases

Use Case 1: Analyzing a GitHub Issue

Multi-tool analysis pipeline:

Use Case 2: Technical Decision Making

Compare database options:

Use Case 3: Code Refactoring Plan

Comprehensive refactoring strategy:

Advanced Patterns

Parallel Execution

Run multiple tools simultaneously for faster results:

Conditional Execution

Execute steps based on previous results:

File-Based Chaining (Large Outputs)

Handle outputs larger than 1MB by saving to disk:

Prompt Engineering Integration

Use 14 research-backed techniques for better reasoning:

Related Resources

All Tools ReferenceComplete documentation for all 31 tools Workflows GuideLearn to create custom workflows Tool ProfilesPre-configured tool sets Tools ConfigurationCustomize which tools are enabled

Practical Examples

On This Page

Using Tools Directly (No Workflows)

Example 1: Simple Research

Example 2: Code Analysis

Multi-Tool Chains

Research → Validate → Analyze → Save

Multi-Model Consensus

Running Workflows

How to Run a Workflow

Available Workflows

System Workflows (Advanced)

Finding & Reading Workflow Outputs

Output Directory Structure

📁 File Naming Convention

Configuration

Reading Results

1. Check Manifest.json

2. Read Individual Step Outputs

3. Debugging Failed Workflows

Real-World Use Cases

Use Case 1: Analyzing a GitHub Issue

Use Case 2: Technical Decision Making

Use Case 3: Code Refactoring Plan

Advanced Patterns

Parallel Execution

Conditional Execution

File-Based Chaining (Large Outputs)

Prompt Engineering Integration

Related Resources

💡 Pro Tip

Practical Examples

On This Page

Using Tools Directly (No Workflows)

Example 1: Simple Research

Example 2: Code Analysis

Multi-Tool Chains

Research → Validate → Analyze → Save

Multi-Model Consensus

Running Workflows

How to Run a Workflow

Available Workflows

System Workflows (Advanced)

Finding & Reading Workflow Outputs

Output Directory Structure

📁 File Naming Convention

Configuration

Reading Results

1. Check Manifest.json

2. Read Individual Step Outputs

3. Debugging Failed Workflows

Real-World Use Cases

Use Case 1: Analyzing a GitHub Issue

Use Case 2: Technical Decision Making

Use Case 3: Code Refactoring Plan

Advanced Patterns

Parallel Execution

Conditional Execution

File-Based Chaining (Large Outputs)

Prompt Engineering Integration

Related Resources

💡 Pro Tip