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",
      "context": "pods managed in production",
      "full_statement": "We now manage over 10,000 pods in production"
    }
  ],
  "sections": {
    "background": "Relevant sentences and context...",
    "challenge": "Description of problems faced...",
    "solution": "How they implemented CNCF technologies...",
    "impact": "Results and improvements achieved..."
  }
}

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",
      "context": "pods managed across clusters",
      "full_statement": "we manage 10,000 pods across multiple clusters"
    }
  ],
  "sections": {
    "background": "We're a financial services company with 5000 employees.",
    "challenge": "We were struggling with slow deployments that took 2-3 hours.",
    "solution": "We adopted Kubernetes for orchestration and Argo CD for continuous delivery.",
    "impact": "Now our deployments take only 15 minutes and we manage 10,000 pods across multiple clusters."
  }
}

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