transcript-analysis

name	transcript-analysis
description	Analyzes transcripts to extract structured data
version	1.0.0

Transcript Analysis Skill

Purpose

Extract structured information from corrected video transcripts to enable case study generation:

• Identify CNCF projects used
• Extract quantitative metrics
• Classify content into sections

Analysis Tasks

1. Identify CNCF Projects

Find all mentions of CNCF projects and understand their usage context.

Common CNCF Projects:

• Kubernetes, Prometheus, Envoy, CoreDNS, containerd
• Fluentd, Jaeger, Vitess, Helm, Argo CD, Flux
• Cilium, Linkerd, Istio, etcd, CRI-O, Harbor
• Falco, Dragonfly, Rook, TiKV, gRPC, CNI
• Knative, OpenTelemetry

For each project found, extract:

• Project name (exact capitalization)
• Usage context (what it's used for)
• Any specific features or benefits mentioned

Example:

{
  "name": "Kubernetes",
  "usage_context": "container orchestration and workload scheduling"
}

2. Extract Quantitative Metrics

Find all measurable achievements and improvements.

Metric Types:

Percentages:

• "50% reduction in..."
• "3x increase in..."
• "99.9% uptime"

Time Savings:

• "from 2 hours to 15 minutes"
• "deployment time reduced by 30 minutes"
• "faster by 5x"

Scale:

• "10,000 pods in production"
• "1 million requests per second"
• "100 microservices"

Cost:

• "$100,000 saved annually"
• "reduced costs by 40%"
• "infrastructure costs decreased"

Reliability:

• "zero downtime deployments"
• "99.99% availability"
• "reduced incidents by 80%"

Format for each metric:

{
  "value": "50%",
  "type": "percentage",
  "context": "reduction in deployment time",
  "full_statement": "We saw a 50% reduction in deployment time after adopting Argo CD"
}

3. Classify Content into Sections

Analyze the transcript and extract content for each section type.

Section Types:

Background:

• Company overview and industry
• Business context and scale
• Why they're using CNCF technologies
• Team size and structure

Keywords: "we are", "our company", "we work with", "our team", "in our industry"

Challenge:

• Problems they faced
• Pain points and limitations
• Technical debt or constraints
• Business pressures

Keywords: "the problem", "we faced", "difficulty", "challenge", "struggled", "couldn't"

Solution:

• CNCF technologies adopted
• Implementation approach
• Architecture changes
• How they solved problems

Keywords: "we implemented", "we adopted", "we deployed", "we chose", "solution", "approach"

Impact:

• Results achieved
• Metrics and improvements
• Business outcomes
• Lessons learned

Keywords: "we achieved", "we saw", "improvement", "results", "now we can", "benefit"

Output Format

Return a JSON object with this structure:

{
  "cncf_projects": [
    {
      "name": "Kubernetes",
      "usage_context": "container orchestration platform for microservices"
    },
    {
      "name": "Argo CD",
      "usage_context": "GitOps continuous delivery for Kubernetes"
    }
  ],
  "key_metrics": [
    {
      "value": "50%",
      "type": "percentage",
      "context": "reduction in deployment time",
      "full_statement": "We reduced deployment time by 50%"
    },
    {
      "value": "10,000",
      "type": "scale",
       
       
    
  
   
     
     
     
     
  

Processing Guidelines

1. Read entire transcript - Understand full context
2. Identify all CNCF projects - Case-insensitive search
3. Extract metrics aggressively - Don't miss quantitative data
4. Classify by strongest signal - Sentences can belong to multiple sections
5. Preserve original wording - Use actual quotes when possible
6. Be comprehensive - Include all relevant information

Quality Checklist

• All CNCF projects identified (minimum 2)
• Usage context provided for each project
• At least 1 quantitative metric extracted
• All 4 section types have content
• Background explains company context
• Challenge describes specific problems
• Solution details CNCF implementation
• Impact includes measurable results

Example Input

We're a financial services company with 5000 employees. We were struggling
with slow deployments that took 2-3 hours. We adopted Kubernetes for
orchestration and Argo CD for continuous delivery. Now our deployments
take only 15 minutes and we manage 10,000 pods across multiple clusters.

Example Output

{
  "cncf_projects": [
    {
      "name": "Kubernetes",
      "usage_context": "container orchestration"
    },
    {
      "name": "Argo CD",
      "usage_context": "continuous delivery"
    }
  ],
  "key_metrics": [
    {
      "value": "2-3 hours to 15 minutes",
      "type": "time_savings",
      "context": "deployment time",
      "full_statement": "deployments took 2-3 hours, now take only 15 minutes"
    },
    {
      "value": "10,000",
      "type": "scale",
       
       
    
  
   
     
     
     
     
  

Important Notes

• This analysis feeds into the case-study-generation skill
• Quality here directly impacts final case study quality
• Be thorough - missing metrics or projects degrades output
• When unsure, include rather than exclude
• Preserve technical accuracy - don't interpret or guess