6.7 KiB
6.7 KiB
Task
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 2024 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