Deep Research Team Claude Code Agents
16 agents in the Deep Research Team category. Each installs in one command and drops straight into your .claude directory.
Technical Researcher
Use this agent when you need to analyze code repositories, technical documentation, implementation details, or evaluate technical solutions. This includes researching GitHub projects, reviewing API documentation, finding code examples, assessing code quality, tracking version histories, or comparing technical implementations. <example>Context: The user wants to understand different implementations of a rate limiting algorithm. user: "I need to implement rate limiting in my API. What are the best approaches?" assistant: "I'll use the technical-researcher agent to analyze different rate limiting implementations and libraries." <commentary>Since the user is asking about technical implementations, use the technical-researcher agent to analyze code repositories and documentation.</commentary></example> <example>Context: The user needs to evaluate a specific open source project. user: "Can you analyze the architecture and code quality of the FastAPI framework?" assistant: "Let me use the technical-researcher agent to examine the FastAPI repository and its technical details." <commentary>The user wants a technical analysis of a code repository, which is exactly what the technical-researcher agent specializes in.</commentary></example>
Data Analyst
Use this agent when you need quantitative analysis, statistical insights, or data-driven research. This includes analyzing numerical data, identifying trends, creating comparisons, evaluating metrics, and suggesting data visualizations. The agent excels at finding and interpreting data from statistical databases, research datasets, government sources, and market research.\n\nExamples:\n- <example>\n Context: The user wants to understand market trends in electric vehicle adoption.\n user: "What are the trends in electric vehicle sales over the past 5 years?"\n assistant: "I'll use the data-analyst agent to analyze EV sales data and identify trends."\n <commentary>\n Since the user is asking for trend analysis of numerical data over time, the data-analyst agent is perfect for finding sales statistics, calculating growth rates, and identifying patterns.\n </commentary>\n</example>\n- <example>\n Context: The user needs comparative analysis of different technologies.\n user: "Compare the performance metrics of different cloud providers"\n assistant: "Let me launch the data-analyst agent to gather and analyze performance benchmarks across cloud providers."\n <commentary>\n The user needs quantitative comparison of metrics, which requires the data-analyst agent to find benchmark data, create comparisons, and identify statistical differences.\n </commentary>\n</example>\n- <example>\n Context: After implementing a new feature, the user wants to analyze its impact.\n user: "We just launched the new recommendation system. Can you analyze its performance?"\n assistant: "I'll use the data-analyst agent to examine the performance metrics and identify any significant changes."\n <commentary>\n Performance analysis requires statistical evaluation of metrics, trend detection, and data quality assessment - all core capabilities of the data-analyst agent.\n </commentary>\n</example>
Research Orchestrator
Use this agent when you need to coordinate a comprehensive research project that requires multiple specialized agents working in sequence. This agent manages the entire research workflow from initial query clarification through final report generation. <example>Context: User wants to conduct thorough research on a complex topic. user: "I need to research the impact of quantum computing on cryptography" assistant: "I'll use the research-orchestrator agent to coordinate a comprehensive research project on this topic" <commentary>Since this is a complex research request requiring multiple phases and specialized agents, the research-orchestrator will manage the entire workflow.</commentary></example> <example>Context: User has a vague research request that needs clarification and systematic investigation. user: "Tell me about AI safety" assistant: "Let me use the research-orchestrator to coordinate a structured research process on AI safety" <commentary>The broad nature of this query requires orchestration of multiple research phases, making the research-orchestrator the appropriate choice.</commentary></example>
Report Generator
Use this agent when you need to transform synthesized research findings into a comprehensive, well-structured final report. This agent excels at creating readable narratives from complex research data, organizing content logically, and ensuring proper citation formatting. It should be used after research has been completed and findings have been synthesized, as the final step in the research process. Examples: <example>Context: The user has completed research on climate change impacts and needs a final report. user: 'I've gathered all this research on climate change effects on coastal cities. Can you create a comprehensive report?' assistant: 'I'll use the report-generator agent to create a well-structured report from your research findings.' <commentary>Since the user has completed research and needs it transformed into a final report, use the report-generator agent to create a comprehensive, properly formatted document.</commentary></example> <example>Context: Multiple research threads have been synthesized and need to be presented cohesively. user: 'We have findings from 5 different researchers on AI safety. Need a unified report.' assistant: 'Let me use the report-generator agent to create a cohesive report that integrates all the research findings.' <commentary>The user needs multiple research streams combined into a single comprehensive report, which is exactly what the report-generator agent is designed for.</commentary></example>
Academic Researcher
Academic research specialist for scholarly sources, peer-reviewed papers, and academic literature. Use PROACTIVELY for research paper analysis, literature reviews, citation tracking, and academic methodology evaluation.
Fact Checker
Fact verification and source validation specialist. Use PROACTIVELY for claim verification, source credibility assessment, misinformation detection, citation validation, and information accuracy analysis.
Competitive Intelligence Analyst
Competitive intelligence and market research specialist. Use PROACTIVELY for competitor analysis, market positioning research, industry trend analysis, business intelligence gathering, and strategic market insights.
Research Coordinator
Use this agent when you need to strategically plan and coordinate complex research tasks across multiple specialist researchers. This agent analyzes research requirements, allocates tasks to appropriate specialists, and defines iteration strategies for comprehensive coverage. <example>Context: The user has asked for a comprehensive analysis of quantum computing applications in healthcare. user: "I need a thorough research report on how quantum computing is being applied in healthcare, including current implementations, future potential, and technical challenges" assistant: "I'll use the research-coordinator agent to plan this complex research task across our specialist researchers" <commentary>Since this requires coordinating multiple aspects (technical, medical, current applications), use the research-coordinator to strategically allocate tasks to different specialist researchers.</commentary></example> <example>Context: The user wants to understand the economic impact of AI on job markets. user: "Research the economic impact of AI on job markets, including statistical data, expert opinions, and case studies" assistant: "Let me engage the research-coordinator agent to organize this multi-faceted research project" <commentary>This requires coordination between data analysis, academic research, and current news, making the research-coordinator ideal for planning the research strategy.</commentary></example>
Research Synthesizer
Use this agent when you need to consolidate and synthesize findings from multiple research sources or specialist researchers into a unified, comprehensive analysis. This agent excels at merging diverse perspectives, identifying patterns across sources, highlighting contradictions, and creating structured insights that preserve the complexity and nuance of the original research while making it more accessible and actionable. <example>Context: The research-orchestrator has completed Phase 4 parallel research on 'LLM fine-tuning costs' using academic-researcher, web-researcher, and data-analyst. user: "Synthesize the research outputs." assistant: "I'll invoke the research-synthesizer agent to merge all specialist findings into a unified analysis." <commentary>The orchestrator has confirmed all three researcher outputs exist as files, making this the correct trigger point for synthesis. The agent will locate each output file, extract claims, and produce both synthesis-summary.md and synthesis.json.</commentary></example> <example>Context: The research-orchestrator has completed parallel research on 'WASM adoption in server-side runtimes' using academic-researcher, web-researcher, and technical-researcher. All three output files are confirmed present. user: "All researchers are done. Synthesize everything into a report." assistant: "Let me use the research-synthesizer agent to consolidate the three specialist outputs into a structured synthesis." <commentary>Three distinct researcher outputs referencing the same topic are present; the synthesis agent is the correct next step to unify them and surface contradictions and shared themes.</commentary></example>
Agent Overview
A ready-to-install Claude Code component.
Research Brief Generator
Use this agent when you need to transform a user's research query into a structured, actionable research brief that will guide subsequent research activities. This agent takes clarified queries and converts them into comprehensive research plans with specific questions, keywords, source preferences, and success criteria. <example>Context: The user has asked a research question that needs to be structured into a formal research brief.\nuser: "I want to understand the impact of AI on healthcare diagnostics"\nassistant: "I'll use the research-brief-generator agent to transform this query into a structured research brief that will guide our research."\n<commentary>Since we need to create a structured research plan from the user's query, use the research-brief-generator agent to break down the question into specific sub-questions, identify keywords, and define research parameters.</commentary></example><example>Context: After query clarification, we need to create a research framework.\nuser: "How are quantum computers being used in drug discovery?"\nassistant: "Let me use the research-brief-generator agent to create a comprehensive research brief for investigating quantum computing applications in drug discovery."\n<commentary>The query needs to be transformed into a structured brief with specific research questions and parameters, so use the research-brief-generator agent.</commentary></example>
Query Clarifier
Use this agent when you need to analyze research queries for clarity and determine if user clarification is needed before proceeding with research. This agent should be invoked at the beginning of research workflows to ensure queries are specific and actionable. Examples: <example>Context: The user has created a query-clarifier agent to analyze research queries for clarity. user: "Tell me about AI" assistant: "I'll use the query-clarifier agent to analyze this query for clarity and determine if we need more specific information." <commentary>Since the query is quite broad and could refer to many aspects of AI, the query-clarifier agent should analyze it and likely request clarification about which specific aspect of AI the user is interested in.</commentary></example> <example>Context: The user has created a query-clarifier agent to ensure research queries are actionable. user: "Compare the performance of different sorting algorithms in terms of time complexity" assistant: "Let me analyze this query with the query-clarifier agent to ensure it's clear and specific enough for research." <commentary>This query is relatively clear but the agent should still analyze it to confirm no clarification is needed before proceeding with the research.</commentary></example>
Research Analyst
"Use this agent when you need comprehensive research across multiple sources with synthesis of findings into actionable insights, trend identification, and detailed reporting. Specifically:\\n\\n<example>\\nContext: A company is planning to enter a new technology market and needs deep research on market size, competitors, technologies, and growth trends.\\nuser: \"Research the current state of the AI-assisted code generation market. We need to understand market size, key competitors, technology trends, growth projections, and potential market entry strategies for the next 2-3 years.\"\\nassistant: \"I'll conduct comprehensive market research across industry reports, competitor analyses, technology trends, and market forecasts. I'll gather data on market size and growth rates, identify and profile major competitors, analyze technology trends and innovations, assess market segments, and synthesize findings into a detailed report with strategic recommendations for market entry.\"\\n<commentary>\\nInvoke research-analyst when you need wide-ranging research that synthesizes information from multiple sources into a comprehensive report with trends, competitive landscape, and strategic insights. This agent excels at pulling together disparate information into coherent analysis.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: An organization needs to understand regulatory changes and their implications across multiple jurisdictions and time periods.\\nuser: \"Research GDPR implementation requirements and how they've evolved since 2018. We need to understand initial regulations, amendments, enforcement trends, fines imposed, and current compliance best practices globally.\"\\nassistant: \"I'll research GDPR regulations comprehensively: gathering initial framework documentation, tracking regulatory amendments and guidance updates, analyzing enforcement actions and penalties imposed, identifying global compliance variations, and synthesizing this into a timeline showing evolution and current best practices for implementation.\"\\n<commentary>\\nUse research-analyst for research requiring deep temporal analysis, tracking regulatory or industry evolution, and synthesizing complex information into structured understanding. The agent excels at creating comprehensive reports that show patterns and changes over time.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A research team needs analysis of industry trends to inform strategic planning and identify emerging opportunities.\\nuser: \"Analyze current trends in remote work technology adoption. We need to understand adoption rates by industry, key drivers and barriers, emerging tools and platforms, skills gap evolution, and predictions for the next 3-5 years.\"\\nassistant: \"I'll research remote work trends systematically: gathering adoption statistics by sector, identifying key drivers and obstacles, analyzing emerging technologies and platforms, researching skills requirements and gaps, synthesizing workforce trend data, and synthesizing into a report with opportunity identification and strategic implications for our product development.\"\\n<commentary>\\nInvoke research-analyst when you need to understand broad trends, identify patterns across industries or demographics, and extract strategic opportunities from research findings. This agent synthesizes disparate data points into actionable trend analysis.\\n</commentary>\\n</example>"
Search Specialist
"Use when you need to find specific information across multiple sources using advanced search strategies, query optimization, and targeted information retrieval. Invoke this agent when the priority is locating precise, relevant results efficiently rather than analyzing or synthesizing content. Specifically:\\n\\n<example>\\nContext: User is looking for academic papers on a narrow topic with specific technical criteria and needs comprehensive coverage across multiple databases.\\nuser: \"I need to find all papers published in the last 3 years about neural network pruning techniques for mobile devices. I need papers from at least 5 different sources.\"\\nassistant: \"I'll use the search-specialist agent to systematically search academic databases, conference proceedings, and preprint servers with optimized queries to ensure comprehensive coverage of this specific topic.\"\\n<commentary>\\nUse search-specialist when the user needs exhaustive information retrieval with multiple sources, specific quality criteria, and emphasis on finding hard-to-locate content. This agent excels at designing search strategies, optimizing queries, and ensuring no relevant information is missed.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User is building a competitive intelligence report and needs specific market data, financial information, and company announcements from varied sources.\\nuser: \"Find all recent announcements, patents, and financial reports from our three main competitors. I need documents from the past 18 months across public databases, patent offices, and news sources.\"\\nassistant: \"I'll deploy the search-specialist agent to systematically search patent databases, SEC filings, news archives, and company websites with targeted queries to gather comprehensive competitive intelligence.\"\\n<commentary>\\nUse search-specialist when you need to aggregate information from diverse, specialized sources with specific temporal or categorical constraints. The agent's expertise in source selection and query optimization ensures thorough coverage.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User needs to locate a specific technical specification or documentation that exists but is difficult to find across various repositories and archives.\\nuser: \"I'm looking for the technical specification document for the legacy messaging protocol we deprecated in 2015. It's somewhere in our archives or public documentation sites.\"\\nassistant: \"I'll use the search-specialist agent to systematically search archived documentation, public repositories, and historical snapshots using keyword variations and source-specific search techniques to locate this document.\"\\n<commentary>\\nUse search-specialist when searching for specific, difficult-to-locate information in archives, legacy systems, or scattered across multiple repositories. The agent applies advanced search techniques like reverse searching, citation tracking, and deep web access to find needle-in-haystack information.\\n</commentary>\\n</example>"
Data Researcher
"Use this agent when you need to discover, collect, and validate data from multiple sources to fuel analysis and decision-making. Invoke this agent for identifying data sources, gathering raw datasets, performing quality checks, and preparing data for downstream analysis or modeling. Specifically:\\n\\n<example>\\nContext: A data scientist needs access to customer behavior data across multiple sources. The data scientist asked you to find and compile raw customer interaction logs, transaction history, and engagement metrics from APIs, databases, and web sources.\\nuser: \"We need a comprehensive customer dataset combining transaction logs, user engagement, and demographic data from all our sources. Can you find, collect, and validate this data?\"\\nassistant: \"I'll identify all available customer data sources including your transaction database, engagement tracking system, and third-party demographic APIs. I'll collect raw data from each source, validate completeness and accuracy, check for duplicates and inconsistencies, document data lineage, and deliver clean datasets ready for analysis along with a data quality report.\"\\n<commentary>\\nUse data-researcher when you need raw data discovery and collection. This agent excels at finding disparate sources, extracting raw datasets, performing quality validation, and preparing data pipelines for downstream analysts or scientists.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A market research team needs historical social media data, competitor pricing data, and industry reports to inform competitive analysis, but the data is scattered across multiple platforms and sources.\\nuser: \"We need to gather competitive intelligence data: pricing information from our competitors' websites over the past year, social media sentiment about their products, and relevant industry reports. How can we collect all this?\"\\nassistant: \"I'll systematically discover and collect data from competitor websites (web scraping), social media platforms (API access and monitoring), industry report repositories, and news sources. I'll validate data consistency, handle missing periods, document collection methodology, identify and fix data quality issues, and organize datasets for competitive analysis.\"\\n<commentary>\\nInvoke data-researcher when you need to assemble raw data from diverse, sometimes unstructured sources. The agent handles the data discovery, collection, validation, and preparation work that precedes analytical work.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A researcher has identified several scientific datasets relevant to climate analysis but needs to access them, merge them, check for quality issues, and prepare them for statistical analysis.\\nuser: \"I've identified 6 public climate datasets from government sources, academic institutions, and satellite databases. Can you access, download, validate, and consolidate them into a single research dataset?\"\\nassistant: \"I'll locate and download each dataset from its source, verify completeness against metadata specifications, check for temporal and geographic coverage, identify and handle missing or outlier values, reconcile different measurement units and formats, remove duplicates across datasets, and deliver a consolidated, quality-checked dataset with full documentation of sources and processing steps.\"\\n<commentary>\\nUse data-researcher for the critical work of assembling and validating raw research datasets. This agent handles discovery, extraction, validation, and preparation—enabling researchers and analysts to focus on analysis rather than data wrangling.\\n</commentary>\\n</example>"
Nia Oracle
Expert research agent specialized in leveraging Nia's knowledge tools. Use PROACTIVELY for discovering repos/docs, deep technical research, remote codebases exploration, documentation queries, and cross-agent knowledge handoffs. Automatically indexes and searches discovered resources.
Other Agents Categories
Catalog data and component content are sourced from the open-source davila7/claude-code-templates project (MIT license). ToolZip curates the listing and writes original descriptions; every component links back to its original source. Claude Code is a product of Anthropic. ToolZip is an independent catalog and is not affiliated with or endorsed by Anthropic.