Autonomous agent for research and analysis tasks

Autonomous agent for research and analysis tasks

Launches a smart agent to handle complex searches and investigations. Great for large-scale work without eating up context.

Usage

# Request Task from Claude
"Investigate [task] using Task"

What Task Does

Works Independently

• Combines multiple tools automatically
• Gathers and analyzes step by step
• Puts results together in clear reports

Saves Context

• Uses less memory than manual searching
• Searches lots of files efficiently
• Pulls data from outside sources

Ensures Quality

• Checks if sources are reliable
• Verifies from different angles
• Fills in missing pieces

Basic Examples

# Complex codebase investigation
"Investigate which files implement this feature using Task"

# Large-scale file search
"Identify configuration file inconsistencies using Task"

# External information collection
"Investigate the latest AI technology trends using Task"

Collaboration with Claude

# Complex problem analysis
"Analyze the cause of memory leaks using Task, including profiling results and logs"

# Dependency investigation
"Investigate vulnerabilities of this npm package using Task"

# Competitor analysis
"Investigate API specifications of competing services using Task"

# Architecture analysis
"Analyze dependencies of this microservice using Task"

Task vs Other Commands

When to Use What

Command	Main Use Case	Execution Method	Information Collection
Task	Investigation, analysis, search	Autonomous execution	Multiple sources
ultrathink	Deep thinking, judgment	Structured thinking	Existing knowledge-focused
sequential-thinking	Problem-solving, design	Step-by-step thinking	As needed
plan	Implementation planning	Approval process	Requirement analysis

Quick Decision Guide

Need to gather info?
├─ Yes → From many places or lots of files?
│          ├─ Yes → **Use Task**
│          └─ No → Just ask normally
└─ No → Need deep thinking?
          ├─ Yes → Use ultrathink/sequential-thinking
          └─ No → Just ask normally

When Task Works Best

Great For

• Exploring complex codebases (dependencies, architecture)
• Searching many files (patterns, configs)
• Gathering external info (tech trends, libraries)
• Combining data from multiple places (logs, metrics)
• Repetitive investigations (audits, debt checks)
• Big searches that would eat too much context

Not Great For

• Simple questions I already know
• Quick one-time tasks
• Things needing back-and-forth discussion
• Design decisions (use plan or thinking commands instead)

Detailed Examples by Category

System Analysis and Investigation

# Complex system analysis
"Identify bottlenecks in the EC site using Task, investigating database, API, and frontend"

# Architecture analysis
"Analyze dependencies of this microservice using Task, including API communication and data flow"

# Technical debt investigation
"Analyze technical debt in legacy code using Task, including refactoring priorities"

Security and Compliance

# Security audit
"Investigate vulnerabilities in this application using Task, based on OWASP Top 10"

# License investigation
"Investigate license issues in project dependencies using Task"

# Configuration file audit
"Identify security configuration inconsistencies using Task, including environment differences"

Performance and Optimization

# Performance analysis
"Identify heavy queries in the application using Task, including execution plans and optimization proposals"

# Resource usage investigation
"Investigate causes of memory leaks using Task, including profiling results and code analysis"

# Bundle size analysis
"Investigate frontend bundle size issues using Task, including optimization suggestions"

External Information Collection

# Technology trend investigation
"Investigate 2026 JavaScript framework trends using Task"

# Competitor analysis
"Investigate API specifications of competing services using Task, including feature comparison table"

# Library evaluation
"Compare state management libraries using Task, including performance and learning costs"

Execution Flow and Quality Assurance

Task Execution Flow

1. Initial Analysis
   ├─ Decomposition of task and identification of investigation scope
   ├─ Selection of necessary tools and information sources
   └─ Development of execution plan

2. Information Collection
   ├─ File search and code analysis
   ├─ Collection of external information
   └─ Data structuring

3. Analysis and Integration
   ├─ Relevance analysis of collected information
   ├─ Identification of patterns and issues
   └─ Verification of hypotheses

4. Reporting and Proposal
   ├─ Structuring of results
   ├─ Creation of improvement proposals
   └─ Presentation of next actions

Quality Assurance

• Reliability check of information sources: Fact confirmation from multiple sources
• Completeness check: Verification of no gaps in investigation targets
• Consistency verification: Confirmation of consistency in conflicting information
• Practicality evaluation: Assessment of feasibility and effectiveness of proposals

Error Handling and Constraints

Common Constraints

• External API usage limits: Rate limits and authentication errors
• Large file processing limits: Memory and timeout constraints
• Access permission issues: Restrictions on file and directory access

Error Handling

• Partial result reporting: Analysis with only obtainable information
• Alternative proposals: Suggestion of alternative investigation methods under constraints
• Stepwise execution: Division of large-scale tasks for execution

Notes

• Task is optimal for complex, autonomous investigation and analysis tasks
• For simple questions or when immediate answers are needed, use normal question format
• Treat investigation results as reference information and always verify important decisions
• When collecting external information, pay attention to the freshness and accuracy of information

Execution Example

# Usage example
"Investigate issues in GraphQL schema using Task"

# Expected behavior
# 1. Dedicated agent starts
# 2. Search for GraphQL-related files
# 3. Analyze schema definitions
# 4. Compare with best practices
# 5. Identify issues and propose improvements
# 6. Create structured report