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Transcript Analysis

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kenny589
transcript-analysis

Extract actionable intelligence from sales call transcripts. Systematic frameworks for mining discovery calls, demos, and follow-ups to improve messaging, objection handling, and close rates.

Overview

Publisherkenny589
Repositorygtm-flywheel
Skill nametranscript-analysis
Stars
71
Forks
26
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kenny589 on GitHub. Read the source before you install it.

Installation

Install the Transcript Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kenny589/gtm-flywheel/tree/main/sales-intelligence/transcript-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kenny589/gtm-flywheel.git /tmp/gtm-flywheel
mkdir -p .claude/skills
cp -r /tmp/gtm-flywheel/sales-intelligence/transcript-analysis .claude/skills/transcript-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Transcript Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Transcript Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Transcript Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Transcript Analysis

When to Use

  • After completing a batch of discovery calls or demos (10+ transcripts)
  • Building training materials for new sales hires
  • Identifying messaging gaps between what you say and what prospects care about
  • Improving cold email copy based on the exact language prospects use
  • Preparing competitive battlecards from what prospects say about alternatives

Framework

Why Transcripts Are a Goldmine

Your sales call transcripts contain the answers to every outbound question:

  • What language do prospects use? → Use it in your cold emails
  • What objections come up repeatedly? → Pre-handle them in your outreach
  • What triggers made them take the call? → Target those triggers in your campaigns
  • What do they say about competitors? → Build battlecards from their words
  • What outcomes do they actually care about? → Lead with those in your value prop

Most teams record calls and never look at the transcripts again. The intelligence sits there, unused.


The Transcript Analysis Framework: 7 Extraction Layers

For every batch of transcripts (minimum 10), run through these seven extraction layers:

Layer 1: Pain Point Extraction

Goal: Identify the specific pains prospects describe in their own words.

What to ExtractHow to Find ItExample
Primary painThe first problem they mention unprompted"Our biggest challenge is we can't predict pipeline"
Secondary painsProblems that come up when probed"And honestly, our SDRs are spending too much time on manual research"
Pain languageThe exact words and phrases used"flying blind," "leaky bucket," "hamster wheel"
Pain urgencyHow pressing the problem feels to them"We need to fix this before board review in Q2"
Pain quantificationAny numbers they attach to the problem"We're losing 20 hours a week on manual reporting"

Analysis template:

Pain Frequency Analysis (across {{N}} transcripts):

| Pain | Frequency (% of calls) | Avg Urgency (1-5) | Sample Quotes |
|------|----------------------|-------------------|---------------|
| ___ | __% | __ | "___" |
| ___ | __% | __ | "___" |
| ___ | __% | __ | "___" |

Top 3 pains (appear in 60%+ of calls):
1. ___
2. ___
3. ___
Layer 2: Trigger Event Extraction

Goal: Understand what made the prospect agree to the call right now.

What to ExtractHow to Find ItExample
The immediate triggerAsk: "What prompted you to take this call?""We just lost our third deal to {{competitor}} this quarter"
The underlying shiftWhat changed in their business"We hired a new CRO and she wants to overhaul outbound"
The timeline driverWhy now, not 3 months ago"We have budget allocated for Q2"
The exploration stageHow far along they are"We've been looking at tools for about 2 weeks"

Analysis template:

Trigger Analysis (across {{N}} transcripts):

| Trigger Type | Frequency | Timeline Urgency | Best Outreach Timing |
|-------------|-----------|-----------------|---------------------|
| ___ | __% | ___ | ___ |
| ___ | __% | ___ | ___ |
| ___ | __% | ___ | ___ |

How this feeds outbound: The triggers your best prospects describe become your trigger-based campaign targets. If 60% of discovery calls were triggered by "hired a new VP Sales," that's your #1 trigger campaign.

Layer 3: Language Mining

Goal: Build a vocabulary bank of the exact words and phrases your prospects use.

CategoryProspect LanguageYour Current LanguageGap?
Problem description"We're throwing spaghetti at the wall""Your outreach lacks targeting precision"Yes — use their version
Success vision"I want to walk into the board meeting with real numbers""Achieve predictable pipeline generation"Yes — use their version
Evaluation criteria"Does it actually work for companies our size?""Enterprise-grade scalability"Yes — use their version
Buying motivation"I'm tired of guessing""Data-driven decision making"Yes — use their version

Build the language bank:

LANGUAGE BANK (from {{N}} transcripts)

Problem Phrases (use in cold email pain lines):
- "___"
- "___"
- "___"

Success Phrases (use in value prop lines):
- "___"
- "___"
- "___"

Urgency Phrases (use in CTAs):
- "___"
- "___"
- "___"

Objection Phrases (use in follow-up handling):
- "___"
- "___"
- "___"
Layer 4: Objection Mapping

Goal: Catalog every objection and how it was (or wasn't) handled.

ObjectionFrequencyWhen It AppearsBest Response (from transcripts)Outcome
"We're already using {{competitor}}"__%Early in call"___ response that worked ___"Continued / Lost
"We don't have budget right now"__%Mid-late call"___ response that worked ___"Continued / Lost
"We want to build this in-house"__%Early in call"___ response that worked ___"Continued / Lost
"I need to check with my team"__%End of call"___ response that worked ___"Continued / Lost

See the Objection Mining skill for detailed analysis frameworks.

Layer 5: Competitive Intelligence

Goal: Learn what prospects say about alternatives — in their words, not yours.

CompetitorMentions (% of calls)What Prospects LikeWhat Prospects DislikeSwitching Triggers
_____%"___""___""___"
_____%"___""___""___"
_____%"___""___""___"

Direct quotes are more powerful than your interpretations. When a prospect says "{{competitor}} is fine for basic stuff but falls apart at scale," that's a messaging angle you couldn't manufacture.

Layer 6: Decision Process Mapping

Goal: Understand how your prospects actually make buying decisions.

ElementWhat to ExtractAnalysis
Decision makersWho else is involved?"I'd need to loop in our VP Eng and Finance"
TimelineHow long do they think it will take?"We'd want to start a pilot by end of Q2"
Budget processHow is budget allocated?"Anything under $50K I can approve. Above that, it goes to the CFO"
Evaluation criteriaWhat are they comparing on?"Integration with Salesforce is non-negotiable"
Deal killersWhat would make them say no?"If it requires us to change our CRM workflow"
ChampionsWho internally is pushing for this?"My Head of RevOps has been asking for this for months"
Layer 7: Win/Loss Pattern Recognition

Goal: Identify what separates calls that convert from calls that don't.

FactorWon CallsLost Calls
Call durationAvg: __ minAvg: __ min
Prospect talk ratio__%__%
Questions asked by prospect__ avg__ avg
Primary pain mentioned______
Trigger event______
Next step agreedYes: __%Yes: __%
Decision timeline______
Competitor mentioned______

Operationalizing Transcript Insights

Turn analysis into action:

InsightActionWhere It Goes
Top pain pointsRewrite cold email pain lines using prospect languageCopy Playbook
Trigger eventsBuild trigger-based campaignsCampaign briefs
Language bankUpdate all outreach templatesEmail templates, LinkedIn scripts
Objection patternsCreate pre-handling in email sequencesSequence Step 2-3 copy
Competitive intelligenceBuild/update battlecardsSales enablement docs
Decision processOptimize follow-up timing and stakeholder mappingSales playbook
Win/loss patternsTrain team on what separates wins from lossesTeam training

Transcript Analysis Cadence

FrequencyActivityMinimum Sample
WeeklyQuick scan of last 5 calls for urgent insights5 transcripts
MonthlyFull 7-layer analysis on the month's calls15-20 transcripts
QuarterlyTrend analysis: how are pains, triggers, and objections shifting?All quarterly calls
Ad hocDeep dive on a specific question (e.g., "why are we losing to {{competitor}}?")Relevant transcripts only

Templates

Transcript Analysis Report

# Transcript Analysis Report
# Period: {{date_range}}
# Transcripts Analyzed: {{N}}
# Analyst: {{name}}

## Key Findings

### Top 3 Pains (by frequency)
1. {{pain}} — {{frequency}}% of calls — "{{sample_quote}}"
2. {{pain}} — {{frequency}}% of calls — "{{sample_quote}}"
3. {{pain}} — {{frequency}}% of calls — "{{sample_quote}}"

### Top 3 Triggers
1. {{trigger}} — {{frequency}}%
2. {{trigger}} — {{frequency}}%
3. {{trigger}} — {{frequency}}%

### Language Updates Needed
| Current Copy | Should Be | Source Transcript |
|-------------|-----------|------------------|
| "___" | "___" | {{call_id}} |

### Competitive Updates
| Competitor | New Intelligence | Action |
|-----------|-----------------|--------|
| ___ | "___" | ___ |

### Recommendations
1. ___
2. ___
3. ___

Tips

  • You need at least 10 transcripts before patterns are reliable. Below that, you're working with anecdotes. Above 20, patterns become robust.
  • Use AI to do the initial extraction (pain points, trigger events, language), but have a human validate the insights. AI is good at pattern matching; humans are good at judging which patterns actually matter.
  • The highest-value extraction is the language bank. When your cold emails use the exact phrases prospects use on sales calls, reply rates increase measurably. This is because you're speaking their language, not translating through marketing jargon.
  • Don't just analyze discovery calls. Analyze LOST deals too. The transcripts from deals you lost often contain the most honest feedback about your positioning, pricing, and competitive weaknesses.
  • Share transcript insights with your marketing team. The pains and language from sales calls should inform blog content, ad copy, landing pages, and webinar topics — not just outbound emails.

Progressive disclosure: load call recording platform integrations and AI extraction prompts only when analyzing transcripts from a specific source.

Frequently asked questions

What does the Transcript Analysis AI skill do?

Extract actionable intelligence from sales call transcripts. Systematic frameworks for mining discovery calls, demos, and follow-ups to improve messaging, objection handling, and close rates.

Why use Transcript Analysis on TypingMind?

Because you install it once and use it with any model. Transcript Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Transcript Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kenny589/gtm-flywheel/tree/main/sales-intelligence/transcript-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Transcript Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Transcript Analysis?

As many as you like. As long as a model supports skills, you can use Transcript Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Transcript Analysis AI skill free?

Yes. It is published on GitHub by kenny589 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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