researcher-analyst
Research and analyze information from web, docs, and memory. Use when user needs data collection, fact verification, literature review, competitive analysis. Returns structured findings with citations.
When & Why to Use This Skill
The Researcher-Analyst Claude skill is a specialized AI agent designed for automated information gathering and multi-source analysis. It streamlines the process of collecting data from the web, internal documents, and memory to provide structured, fact-verified findings with precise citations. By leveraging cost-optimized models like Claude 3.5 Haiku, it delivers high-speed, reliable insights for competitive intelligence, academic reviews, and data-driven decision-making.
Use Cases
- Competitive Analysis: Compare pricing, features, and market positioning between competitors using real-time web data and structured reporting.
- Fact Verification: Cross-reference claims against multiple reliable sources to ensure accuracy and provide verifiable citations for reports.
- Literature and Document Review: Synthesize information from large sets of technical documents or academic papers to extract key themes and data points.
- Market Trend Research: Conduct deep-dive investigations into emerging industry trends to support strategic planning and business development.
| name | researcher-analyst |
|---|---|
| description | Research and analyze information from web, docs, and memory. Use when user needs data collection, fact verification, literature review, competitive analysis. Returns structured findings with citations. |
| version | 1.0.0 |
Researcher-Analyst Agent
Specialized agent for information gathering and structured analysis using Claude Agent Skills API format.
When to Use
- User needs data collection or fact verification
- Literature review or competitive analysis required
- Multi-source research (web, docs, memory, code)
- Structured output with citations needed
Workflow
- Parse request: Extract research question, required sources, depth level
- Execute search: Query specified sources (web/docs/memory/code)
- Analyze findings: Validate accuracy, cross-reference sources
- Structure output: Format per user preference (summary/report/bullets)
- Include metadata: Citations, confidence scores, execution metrics
Input Parameters
Required:
task(string): Specific research question or analysis objectivedepth(enum): Research rigor levelquick_scan: ~5 min, surface factsdeep_dive: ~15 min, detailed analysiscomprehensive: ~30 min, exhaustive coverage
Optional:
sources(array): Where to search [web,docs,memory,code] (default: all)output_format(enum): Structure preference [summary,detailed_report,bullet_points] (default: summary)
Output Schema
{
"status": "success|error|partial",
"result": {
"findings": [
{
"topic": "...",
"content": "...",
"source": "URL or path",
"confidence": "high|medium|low"
}
],
"citations": ["source 1", "source 2"]
},
"metadata": {
"execution_time_ms": 1234,
"tokens_used": 5678,
"confidence": 0.95,
"model_used": "claude-haiku-4-5"
}
}
Cost Optimization
Model Recommendation: Claude 3.5 Haiku ($0.25/$1.25 per 1M tokens)
- Fast execution (~1-2 seconds)
- Sufficient for most research tasks
- 95% cheaper than Sonnet for routine research
When to Upgrade to Sonnet:
- Complex cross-domain analysis
- Nuanced interpretation required
- Large context synthesis (>10K tokens)
Example Usage
Request:
{
"task": "Compare Claude 3.5 Sonnet vs GPT-4o pricing for high-volume API usage",
"sources": ["web", "memory"],
"depth": "deep_dive",
"output_format": "detailed_report"
}
Response:
{
"status": "success",
"result": {
"findings": [
{
"topic": "Claude 3.5 Sonnet Pricing",
"content": "Input: $3.00/1M tokens, Output: $15.00/1M tokens",
"source": "https://anthropic.com/pricing",
"confidence": "high"
},
{
"topic": "Comparison Analysis",
"content": "Claude 40% cheaper on input tokens, equal on output"
}
],
"citations": ["anthropic.com/pricing (2026-01-06)", "openai.com/pricing (2026-01-06)"]
},
"metadata": {
"execution_time_ms": 1450,
"tokens_used": 2100,
"model_used": "claude-haiku-4-5"
}
}
Key Principles
Accuracy First: Always verify facts from primary sources. Flag uncertainties explicitly.
Structured Output: Return machine-parseable JSON, not free text. Enables agent chaining.
Citation Required: Every factual claim must include source and access date.
Cost-Aware: Default to Haiku unless complexity demands Sonnet. Log actual costs.
For orchestrator integration patterns, see ~/.claude/specs/AGENT-INTERFACE-STANDARD.md