Analyze email threads, call transcripts, and conversations for resonance, adherence to messaging, and competitive differentiation. Use when user says "analyze this call", "how did the email land", "score this thread", "conversation analysis", or pastes conversation content to evaluate.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Analyze email threads, call transcripts, and conversations for resonance, adherence to messaging, and competitive differentiation. Use when user says "analyze this call", "how did the email land", "score this thread", "conversation analysis", or pastes conversation content to evaluate.
/octave-call-analyzer - Conversation Analysis
Analyze email threads, call transcripts, and sales conversations against your Octave library. Evaluates messaging resonance, Motion ICP narrative adherence, and competitive differentiation. Provides actionable insights, suggested improvements, and draft follow-ups.
Principles
Follow these standards during generation. Read each before producing output.
Content and language:
Editorial rules — no AI-isms, banned vocabulary, honest analyst tone
What would you like me to analyze?
1. Paste an email thread
2. Paste a call transcript
3. Paste a chat/message thread
4. Provide a file path
(Paste content below or tell me the file path)
Accept pasted content or read from file. Content can be:
Email thread (with headers or without)
Call transcript (with speaker labels or without)
Chat/messaging thread
Meeting notes
Step 2: Parse and Structure the Content
For Email:
Extract:
Participants (internal vs external)
Thread direction (outbound, inbound, back-and-forth)
Key messages from each party
Current status (awaiting response, ended, etc.)
For Call Transcript:
Extract:
Participants and roles
Speaker segments
Key exchanges
Duration indicators if available
For Chat:
Extract:
Participants
Message sequence
Key exchanges
Step 3: Identify Context
Use MCP tools to gather context:
Research external participants:
# Get external participant info
find_person({
searchMode: "specific_person",
email: "<external email>", # or
firstName: "<name>",
companyName: "<company>"
})
# Get company info
find_company({
domain: "<domain from email>" # or inferred from signature
})
# Match to persona
qualify_person({
person: { email: "<email>", jobTitle: "<title>" },
additionalContext: "Identify which persona this person matches"
})
Get library context:
# Find the right Motion + Motion ICP cell for this conversation
list_motions()
list_motion_icps({ motionOId: "<motion_oId>" })
find_motion_icp({ motionIcpOId: "<motion_icp_oId>", includeLearnings: true })
Step 4: Analyze Against Library
Run three analysis dimensions:
Resonance Analysis
Did our messaging land? What signals indicate engagement or disengagement?
Use MCP to get persona details:
# Search for messaging we used
search_knowledge_base({
query: "<key phrases from our messages>",
entityTypes: ["persona", "use_case"]
})
# Compare to persona pain points
get_entity({ oId: "<matched_persona_oId>" })
Evaluate:
Pain points addressed vs. persona's documented pain points
Value props used vs. available value props
Questions asked vs. recommended discovery questions
Response patterns indicating interest/skepticism
Adherence Analysis
Did we follow the Motion ICP narrative? What did we miss?
Use MCP to get the Motion ICP cell narrative:
# Get the matched Motion ICP cell (persona × segment)
find_motion_icp({ motionIcpOId: "<matched_motion_icp_oId>", includeLearnings: true })
Compare conversation to the Motion ICP narrative:
Strategic narrative alignment
Benefits and impacts delivered vs. available
Pains and consequences surfaced
Methodology / qualifying questions asked
Objection handling approach
Discovery depth
Differentiation Analysis
Did we position against competitors effectively?
Use MCP to get competitor details:
# Check for competitor mentions
search_knowledge_base({
query: "<competitor names or hints from conversation>",
entityTypes: ["competitor"]
})
# Get competitor details
get_entity({ oId: "<competitor_oId>" })
What would you like to do next?
1. Deep dive on a specific analysis area
2. Get more suggestions for [resonance / adherence / differentiation]
3. Refine the follow-up message
4. Generate content to address gaps
5. Compare to another conversation
6. Save insights to deal notes
7. Done
Your choice:
For option 5, pull the other conversation with search_call_transcripts({ companyDomain, query: "<topic>" }) instead of asking the user to paste it again — it returns verbatim, speaker-attributed moments across every indexed call with that account, so you can compare this thread against what was actually said on past calls.
All Motion ICP narrative elements used appropriately
7-8
Good adherence
Most elements used, minor gaps
5-6
Partial adherence
Some elements used, key gaps
3-4
Weak adherence
Few elements used, off-narrative
1-2
Non-adherent
Didn't follow the Motion ICP narrative
Differentiation Score (1-10)
Score
Meaning
Signals
9-10
Strong positioning
Clear differentiation, competitive landmines set
7-8
Good positioning
Some differentiation, mostly positioned
5-6
Neutral
Didn't address competition directly
3-4
Weak positioning
Competitor strengths uncountered
1-2
Poor positioning
Lost competitive ground
MCP Tools Used
Research
find_person - Identify external participants
find_company - Get company context
qualify_person - Match to persona
Library Context
list_motions - List all Motions in the workspace
list_motion_icps - List Motion ICP cells for a Motion
find_motion_icp - Get full Motion ICP cell narrative (Strategic narrative, Pains and consequences, Benefits and impacts, Methodology, References) for adherence analysis
search_call_transcripts - Pull verbatim moments from other calls with this account (or this persona) to compare against the pasted conversation
get_entity_evidence - Real customer language backing a matched persona's pain point or a competitor's claim, for the resonance/differentiation write-up
Content Generation
generate_content - Draft follow-up messages
generate_email - Generate email responses
Input Formats Supported
Email Thread
From: john@acme.com
To: me@company.com
Subject: Re: Quick question about your platform
[Message content]
---
On Jan 15, me@company.com wrote:
> [Previous message]
Call Transcript
[00:00] Sales Rep: Thanks for joining...
[00:15] Prospect: Happy to be here...
or
Sales Rep: Thanks for joining...
John (Acme): Happy to be here...
Chat/Message Thread
Me: Hey John, following up on our conversation
John: Thanks for reaching out
Me: Did you have a chance to review the proposal?
Error Handling
No Content Provided:
Please paste the content you'd like me to analyze, or provide a file path.
I can analyze:
Email threads
Call transcripts
Chat messages
Meeting notes
Cannot Identify Participants:
I couldn't identify the external participant.
Can you tell me:
Who is the prospect? (name, company, title)
What stage is this deal in?
This helps me match to the right Motion ICP cell.
No Matching Motion ICP:
I couldn't find a Motion ICP cell that matches this conversation.
I'll analyze against general best practices, but for better insights:
Tell me which Motion (offering + motion type) this falls under
Or create a Motion for this offering if one don't exist yet
Related Skills
/octave-research - Deep research on participants
/octave-generate - Generate follow-up content
/octave-one-pager - Create collateral to address gaps
/octave-audit - Ensure Motion ICP cells have complete narratives
/octave-pipeline - Deal coaching based on conversation analysis
/octave-insights - Aggregate patterns across many conversations
