Field Research Assistant
Organize field data, synthesize findings, and maintain research audit trails
What You Can Do
This skill transforms raw field observations into structured research data with built-in reproducibility. You can create standardized data collection templates, synthesize observations into coherent findings, and maintain complete audit trails that document every decision and change. The result is field research that's verifiable, defensible, and ready for analysis or publication.
Features
Auto-generate customizable observation templates tailored to your research context (interviews, site surveys, usability tests, environmental studies)
Transform raw field notes into coherent findings by identifying patterns, themes, and contradictions across multiple observations
Automatically document when data was collected, who collected it, what was changed, and why—ensuring full reproducibility
Check completeness of collected data, flag missing fields, inconsistent formats, and potential quality issues before analysis
Automatically identify key insights, outliers, and patterns within your field data with cited evidence from original observations
Rate data completeness, consistency, and reliability on a transparent scale with feedback on how to improve weak areas
Create professional findings reports that include methodology, observations summary, analysis, and audit trail documentation
Example Output
Example 1: Structured Observation Template
Field Study: User Testing - Mobile Checkout Flow
Date: 2026-08-08 | Participant: P04 | Researcher: Sarah M.
[Template with fields for context, observed behavior, time taken, errors encountered, participant quote, assumptions made]
Example 2: Synthesized Findings
## Key Finding: Mobile Checkout Abandonment
- Observed in 3 of 5 participants (60%) at cart review step
- Primary cause: Hidden shipping cost (cited by 2 participants)
- Secondary issue: Unclear return policy link (cited by 1 participant)
- Severity: High | Evidence strength: 3 direct quotes
## Recommended Action
Display shipping cost before users enter payment info (tested in P01, P03, P05)
Example 3: Audit Trail
Observation O-047 (Interview w/ Sarah, 2026-08-06)
- Created by: jane@acme.com, 09:15 AM
- Last modified: jane@acme.com, 10:30 AM (added clarifying note about shipping confusion)
- Change log: Added participant quote, updated severity from Medium to High
- Status: Ready for synthesis
What's Included
- Research Protocol Template: Pre-built template for documenting your research methodology, objectives, and data collection approach
- Observation Schema Builder: Tool to generate customized data templates based on your research type (interviews, surveys, site observations, usability tests)
- Synthesis Workflow: Step-by-step process to transform field notes into findings with pattern identification, theme extraction, and evidence linking
- Audit Trail Manager: Automatic logging of all data entries, modifications, and decisions with timestamps and researcher attribution
- Quality Checklist: Guidance on data completeness checks, consistency validation, and reproducibility verification before analysis
Who It's For
- UX Researchers
- Ethnographers and Anthropologists
- Market Research Analysts
- Quality Assurance Engineers
- Environmental Scientists
Best For
- Usability Testing Studies
- Interview Data Organization
- Site Survey Documentation
- Qualitative Research Synthesis
- Audit-Ready Fieldwork
You might also like
Claude helps you design sound experiments from scratch, validate your methodology against established best practices, and identify potential flaws before you spend resources on implementation. You'll get a reproducible protocol, statistical power analysis, and a comprehensive checklist of control variables and potential confounds—ensuring your experiment produces reliable, actionable results.
Transform raw research data, notes, and preliminary findings into a complete, publication-ready manuscript. This skill handles the entire manuscript architecture—from organizing results into Methods and Results sections to crafting a compelling Abstract and Discussion that positions your findings in context. You provide the data and key insights; Claude accelerates the structuring, drafting, and refinement phases so you can focus on science rather than formatting.
Accelerate literature reviews by automatically synthesizing research papers, extracting key methodologies and findings, and identifying research gaps. This skill analyzes multiple papers simultaneously to spot unexplored areas, methodological blind spots, and emerging opportunities in your field. You'll get structured analysis, comparison matrices, and research landscape summaries that would normally take weeks to produce manually.
Turn your pile of research articles and notes into a structured, synthesized literature review. This skill helps you organize findings by theme, identify contradictions and research gaps, compare methodologies across studies, and generate synthesis templates ready for publication. You'll create clear visual maps of your research landscape and spot opportunities for original contribution.
This skill helps you transform scattered research papers into a coherent synthesis that identifies knowledge gaps, establishes research context, and builds compelling narratives for proposals and publications. You'll analyze methodology, trace citation influence, detect emerging trends, and extract actionable insights from complex literature collections without manually reading every paper.
You can rapidly diagnose complex avionics failures by mapping failure cascades across flight management, inertial navigation, air data, and communications subsystems. The framework guides you through structured fault tree construction, failure mode prioritization, and dependency analysis to eliminate false leads and identify the specific component behavior and conditions causing system degradation. This produces certification-ready root cause documentation that pinpoints which LRU fails under what conditions—the precision required for engineering fixes.
You can conduct preliminary compressor and turbine stage design using physics-based meanline methods, predict performance across operating envelopes, optimize blade geometry parameters, and troubleshoot thermodynamic matching issues. The skill leverages computational methods and industry design standards to enable rapid concept screening and design iteration from conceptual through 30% design maturity, supporting design reviews with thermodynamically justified decisions without requiring full CFD or FEA analysis.
Collect raw field research data—interview transcripts, survey responses, observation notes—and Claude will synthesize it into organized findings, identify patterns, and generate code or documentation automatically. You provide the raw data and research questions; Claude handles analysis, categorization, and artifact generation.