8.4 KiB
8.4 KiB
description: Extract themes and insights from interview transcripts with intelligent agent scaling
argument-hint: [output-name] [transcripts-directory] [--context-file]
Analyze Interview Transcripts
Analyze all interview transcripts in a directory, extract themes, rank insights, and generate a comprehensive report.
Step 1: Discover and Validate Transcripts
Scan the transcripts directory ($2):
- Find all text files (.txt, .md, .doc, .docx)
- Count total transcripts
- Calculate total file size and estimated token count
- Identify any context files (ICP, research goals, methodology docs)
Look for context file ($3):
- If --context-file argument provided, read that file
- If not provided, look for common files in directory:
icp.mdorideal-customer-profile.mdresearch-goals.mdorobjectives.mdmethodology.mdcustomer-profile.mdorpersona.md
Report to user:
## 📁 Transcripts Discovered
**Directory:** $2
**Transcripts found:** [number] files
**Total size:** [size in MB]
**Estimated tokens:** ~[number]k tokens
**Files:**
- [filename 1] ([size])
- [filename 2] ([size])
- [etc.]
**Context documents found:**
- [ICP/research goals/methodology if found, or "None"]
Would you like to proceed with analysis?
Wait for user confirmation.
Step 2: Determine Agent Scaling Strategy
Based on workload, intelligently determine how many parallel agents to use:
Agent Scaling Logic:
If 1-3 transcripts:
→ Use 1 agent (sequential analysis is fine)
If 4-8 transcripts:
→ Use 3 parallel agents
→ Divide transcripts evenly (e.g., Agent 1: transcripts 1-3, Agent 2: 4-6, Agent 3: 7-8)
If 9-15 transcripts:
→ Use 5 parallel agents
→ Divide transcripts evenly
If 16+ transcripts:
→ Use 8 parallel agents (optimal for most systems)
→ Divide transcripts evenly
ALSO consider total token count:
If total tokens > 150k:
→ Increase agent count by 1-2
→ Example: 6 transcripts but 200k tokens → use 5 agents instead of 3
If total tokens < 30k:
→ Decrease agent count
→ Example: 10 short transcripts with 25k tokens → use 3 agents instead of 5
Present strategy to user:
## 🤖 Agent Strategy
Based on [number] transcripts totaling ~[number]k tokens, I recommend using **[number] parallel agents** for efficient analysis.
**How it works:**
- Each agent will analyze [number] transcripts
- Agents work simultaneously (faster than sequential)
- All findings will be synthesized into final report
**Agent assignments:**
- Agent 1: [transcript names]
- Agent 2: [transcript names]
- [etc.]
**Estimated time:** [minutes] minutes
Would you like to:
1. ✅ Proceed with this strategy
2. 🔄 Adjust agent count (tell me your preference)
3. ❌ Cancel
Wait for user approval or adjustment.
Step 3: Launch Theme Extraction Agents
For each agent, use the Task tool with the theme-extractor agent:
Provide each agent with:
- Its assigned transcripts (full text)
- Context file contents (if available)
- Instructions to identify 3-5 major themes
- Request supporting quotes for each theme
Track progress:
## 🔍 Extracting Themes
- ✅ Agent 1: Complete (found 4 themes)
- ✅ Agent 2: Complete (found 5 themes)
- 🔄 Agent 3: In progress
- ⏳ Agent 4: Pending
- ⏳ Agent 5: Pending
Once all agents complete, collect results:
- All themes from all agents
- Supporting quotes
- Frequency counts (how many transcripts mentioned each theme)
Step 4: Synthesize and Rank Themes
Use the insight-ranker agent to:
Consolidate themes:
- Merge duplicate/similar themes across agents
- Example: "Pricing concerns" + "Cost barriers" → "Pricing and cost concerns"
Rank themes by:
- Frequency - How many transcripts mentioned it
- Relevance - Alignment with ICP/research goals (if context provided)
- Impact - Magnitude of the insight (pain point severity, opportunity size)
Present ranked themes to user:
## 🎯 Top Themes Identified
### Theme 1: [Theme Name]
**Mentioned in:** [X] of [Y] transcripts ([percentage]%)
**Relevance:** [High/Medium/Low based on ICP]
**Key insight:** [One sentence summary]
**Representative quote:**
> "[Quote from transcript]"
> — Participant [number/name]
---
### Theme 2: [Theme Name]
[Same format...]
---
[Continue for top 5-7 themes]
---
**Would you like me to:**
1. ✅ Proceed with final report generation
2. 🔎 Deep dive into specific themes (tell me which ones)
3. 🔄 Re-rank themes with different criteria
Wait for user feedback.
Step 5: Generate Comprehensive Report
Create a structured markdown report with:
Report Structure
# Interview Analysis Report: [Output Name]
**Analysis date:** [Date]
**Transcripts analyzed:** [Number]
**Context:** [ICP/research goals summary if available]
---
## Executive Summary
[2-3 paragraph overview of most important findings]
---
## Methodology
**Transcripts:**
- [List of transcripts with dates if available]
**Analysis approach:**
- [Number] parallel agents
- Theme extraction and consolidation
- Ranking by frequency and relevance
**Context documents:**
- [List context files if used]
---
## Key Themes
### 1. [Theme Name] ([frequency]% of interviews)
**Summary:** [Detailed explanation of this theme]
**Why it matters:** [Relevance to ICP/business goals]
**Supporting quotes:**
1. "[Quote]" — Participant [ID]
2. "[Quote]" — Participant [ID]
3. "[Quote]" — Participant [ID]
**Recommendations:**
- [Actionable insight 1]
- [Actionable insight 2]
---
[Repeat for each major theme]
---
## Cross-Cutting Insights
[Patterns that span multiple themes]
---
## Recommendations
1. **[Priority]** [Recommendation based on findings]
2. **[Priority]** [Recommendation based on findings]
[etc.]
---
## Appendix: All Themes
[Comprehensive list of all themes found, including lower-frequency ones]
---
**Analysis performed by:** Interview Transcript Analyzer plugin for Claude Code
**Generated:** [Timestamp]
Save report to: analysis/$1-analysis.md
Step 6: Deliver Results
Report to user:
## ✅ Analysis Complete!
**Report location:** analysis/$1-analysis.md
**Transcripts analyzed:** [number]
**Themes identified:** [number] major themes, [number] total
**Top insights:** [number]
**Report sections:**
- Executive Summary
- Methodology
- [Number] Key Themes (with quotes and recommendations)
- Cross-cutting insights
- Actionable recommendations
**Next steps:**
1. Review the full report
2. Use `/extract-quotes [theme]` to find more quotes on specific themes
3. Use `/rank-insights` to re-rank with different criteria
Error Handling
If no transcripts found:
❌ No transcripts found in directory: $2
Please ensure:
- Directory path is correct
- Transcripts are in supported formats (.txt, .md, .doc, .docx)
- Files are readable
Expected structure:
interviews/
├── interview-1.txt
├── interview-2.txt
└── context/
└── icp.md
If context file specified but not found:
⚠️ Warning: Context file not found: $3
Would you like to:
1. Proceed without context (insights won't be ranked by ICP relevance)
2. Specify a different context file
3. Cancel and add context file first
If agent fails during analysis:
- Save partial results from completed agents
- Report which agent failed and why
- Offer to retry with fewer agents or different allocation
Usage Examples
# Basic analysis
/analyze-interviews customer-feedback ./interviews/
# With ICP context for relevance ranking
/analyze-interviews product-research ./research/transcripts/ --context-file ./research/icp.md
# Large-scale analysis (automatically uses more agents)
/analyze-interviews enterprise-study ./enterprise-interviews/
Tips for Best Results
Organize transcripts clearly:
- Use descriptive filenames (
interview-customer-name.txt) - Include dates if relevant (
2024-01-15-interview-acme-corp.txt) - Keep transcripts in dedicated directory
Provide context:
- ICP document helps rank insights by relevance
- Research goals ensure findings align with objectives
- Methodology doc helps interpret findings appropriately
For large datasets:
- The plugin will automatically scale agents (no manual config needed)
- More transcripts = more agents = faster analysis
- Expected speed: ~15-20 transcripts in 15-20 minutes with 8 agents