tomevault-io /octave:train - Sales Training Ground
Practice and learn your GTM playbook through interactive role-play simulations and knowledge quizzes — all grounded in your real library data. Role-play against buyer personas with realistic objections, or quiz yourself on value props, competitive positioning, and discovery techniques.
Usage
/octave:train [mode] [--persona <name>] [--competitor <name>] [--difficulty easy|medium|hard]
Modes
/octave:train # Interactive - pick a mode
/octave:train roleplay # Simulate a buyer conversation
/octave:train roleplay --persona "CTO" # Role-play with a specific persona
/octave:train roleplay --scenario discovery # Practice discovery calls
/octave:train quiz # Test your GTM knowledge
/octave:train quiz --topic objections # Quiz on objection handling
/octave:train quiz --competitor "Acme" # Competitive knowledge check
Instructions
When the user runs /octave:train:
Step 1: Choose Mode
If no mode specified, ask:
AskUserQuestion({
questions: [{
question: "What do you want to practice?",
header: "Train mode",
options: [
{ label: "Role-Play", description: "Simulate a sales conversation — I'll play the buyer and give you feedback" },
{ label: "Quiz", description: "Test your knowledge of personas, objections, value props, and competitive positioning" },
{ label: "Guided Learning", description: "Walk me through a topic from your playbook — teach me like I'm a new hire" }
],
multiSelect: false
}]
})
Mode: Role-Play
Simulate realistic buyer conversations using persona data from the library.
Step RP-1: Setup the Scenario
Ask for scenario parameters (use AskUserQuestion for structured choices):
AskUserQuestion({
questions: [
{
question: "What scenario do you want to practice?",
header: "Scenario",
options: [
{ label: "Discovery call", description: "First conversation — qualify the opportunity and uncover pain" },
{ label: "Objection handling", description: "Practice responding to tough objections mid-deal" },
{ label: "Demo pitch", description: "Present your product's value to a skeptical buyer" },
{ label: "Competitive displacement", description: "Sell against a competitor the buyer currently uses" }
],
multiSelect: false
},
{
question: "How tough should I be?",
header: "Difficulty",
options: [
{ label: "Friendly", description: "Interested buyer, open to learning — good for building confidence" },
{ label: "Skeptical (Recommended)", description: "Realistic buyer who pushes back and needs convincing" },
{ label: "Hostile", description: "Tough buyer — time-pressed, has objections, hard to impress" }
],
multiSelect: false
}
]
})
If no persona specified, present available personas:
# Get personas from library
list_all_entities({ entityType: "persona" })
Ask:
AskUserQuestion({
questions: [{
question: "Which buyer persona should I play?",
header: "Persona",
options: [
{ label: "[Persona 1 name]", description: "[Title] — [key concern]" },
{ label: "[Persona 2 name]", description: "[Title] — [key concern]" },
{ label: "[Persona 3 name]", description: "[Title] — [key concern]" }
],
multiSelect: false
}]
})
Step RP-2: Load Persona Intelligence
# Get full persona details
get_entity({ oId: "<persona_oId>" })
# Get matching playbook with value props
search_knowledge_base({ query: "<persona name> <scenario>", entityTypes: ["playbook"] })
get_playbook({ oId: "<playbook_oId>", includeValueProps: true })
# Get real objections from conversations (to make role-play realistic)
list_findings({
query: "objections pushback concerns",
startDate: "<180 days ago>",
eventFilters: {
personas: ["<persona_oId>"]
}
})
# Get product details
list_all_entities({ entityType: "product" })
get_entity({ oId: "<product_oId>" })
# Get competitor details (for competitive scenarios)
get_entity({ oId: "<competitor_oId>" }) // if competitive scenario
# Get proof points (to evaluate if rep uses them)
search_knowledge_base({ query: "<persona> results metrics", entityTypes: ["proof_point", "reference"] })
Step RP-3: Set the Scene
Present the scenario context, then begin:
ROLE-PLAY: [Scenario] with [Persona Name]
==========================================
THE SCENE
---------
You're on a [scenario type] with [Persona Name], [Title] at [fictional company].
About the buyer:
- Role: [Title and responsibilities]
- Company: [Brief fictional company description relevant to your ICP]
- Situation: [Scenario-specific context]
- Mood: [Based on difficulty level]
About their company:
- Industry: [From persona's typical segment]
- Size: [Typical company size for this persona]
- Current challenge: [Key pain point from persona data]
[If competitive scenario:]
- Currently using: [Competitor name]
- Their satisfaction level: [Mixed/frustrated/somewhat happy]
RULES
-----
- I'll respond as the buyer would — using real objections from your conversation data
- The conversation will last 8-12 exchanges
- I'll end the conversation naturally when it reaches a conclusion
- After the role-play, I'll give you a detailed scorecard
---
The call starts now. [Persona Name] says:
"[Opening line based on scenario and difficulty]"
How to play the buyer:
Use the loaded persona data to respond realistically:
- Reference real pain points from the persona entity
- Raise real objections from conversation findings data
- React based on difficulty level:
- Friendly: Engaged, asks questions, shares information willingly
- Skeptical: Needs proof, challenges claims, asks "why should I care?"
- Hostile: Short answers, pushes on price, questions ROI, brings up competitors
- Respond naturally to what the user says — don't just cycle through objections
- If the user makes a strong point, acknowledge it (even skeptical buyers respond to good selling)
- If the user fumbles, the buyer gets more distant/disengaged
End the conversation after 8-12 exchanges with a natural conclusion:
- Friendly: "This sounds interesting, let's set up a follow-up"
- Skeptical: "I need to think about it" or "Send me some materials"
- Hostile: "I don't think this is for us" (unless the user sold well)
Step RP-4: Scorecard
After the role-play ends, provide detailed feedback:
ROLE-PLAY SCORECARD
====================
Overall: [Score /100]
Difficulty: [Friendly/Skeptical/Hostile]
Scenario: [Type]
---
WHAT YOU DID WELL
-----------------
1. [Specific thing they did well with quote from the conversation]
2. [Another strong moment]
3. [Good technique used]
---
AREAS TO IMPROVE
-----------------
1. [Missed opportunity] — You could have said: "[Better response]"
2. [Weak moment] — Here's why: [Explanation]
3. [Technique to try] — "[Example of better approach]"
---
ELEMENT SCORES
--------------
| Element | Score | Notes |
|---------|-------|-------|
| Opening / rapport | [1-5] | [Brief feedback] |
| Discovery questions | [1-5] | [Brief feedback] |
| Pain identification | [1-5] | [Brief feedback] |
| Value articulation | [1-5] | [Brief feedback] |
| Proof point usage | [1-5] | [Brief feedback] |
| Objection handling | [1-5] | [Brief feedback] |
| Competitive positioning | [1-5] | [Brief feedback, if applicable] |
| Call control | [1-5] | [Brief feedback] |
| Next steps / CTA | [1-5] | [Brief feedback] |
---
KEY MOMENTS
-----------
Moment: "[Quote from the conversation]"
Assessment: [What worked or didn't, with reference to playbook guidance]
Moment: "[Another quote]"
Assessment: [Feedback]
---
OBJECTIONS ENCOUNTERED
-----------------------
| Objection | Your Response | Ideal Response (from playbook) |
|-----------|--------------|-------------------------------|
| "[Objection 1]" | [What they said] | [What the playbook suggests] |
| "[Objection 2]" | [What they said] | [Better approach] |
---
PROOF POINTS YOU COULD HAVE USED
----------------------------------
- [Proof point from library that was relevant but not mentioned]
- [Another unused proof point]
---
Want to:
1. Try again (same scenario, apply the feedback)
2. Try a harder difficulty
3. Try a different persona
4. Practice a specific objection from this session
5. Switch to quiz mode
6. Done
Mode: Quiz
Test knowledge of the user's own GTM library.
Step Q-1: Choose Topic
AskUserQuestion({
questions: [{
question: "What do you want to be quizzed on?",
header: "Quiz topic",
options: [
{ label: "Personas", description: "Pain points, priorities, buying triggers, and how to sell to each persona" },
{ label: "Objection handling", description: "Common objections and how to respond — from your playbook and real conversations" },
{ label: "Competitive positioning", description: "Differentiators, trap questions, and counters for each competitor" },
{ label: "Full GTM review", description: "Mix of everything — personas, products, value props, objections, competitors" }
],
multiSelect: false
}]
})
Also ask for quiz format:
AskUserQuestion({
questions: [{
question: "What format?",
header: "Format",
options: [
{ label: "Rapid fire (Recommended)", description: "Quick question-answer, 10 questions, scored at the end" },
{ label: "Scenario-based", description: "Situational questions — 'A prospect says X, what do you do?'" },
{ label: "Deep dive", description: "Fewer questions but explain your reasoning — I'll coach you on each answer" }
],
multiSelect: false
}]
})
Step Q-2: Load Quiz Material
# Load based on topic
# For Personas:
list_all_entities({ entityType: "persona" })
get_entity({ oId: "<persona_oId>" }) // for each persona
# For Objections:
list_findings({
query: "objections pushback concerns pricing",
startDate: "<180 days ago>"
})
get_playbook({ oId: "<playbook_oId>", includeValueProps: true })
# For Competitive:
list_all_entities({ entityType: "competitor" })
get_entity({ oId: "<competitor_oId>" }) // for each competitor
# For Full GTM:
list_all_entities({ entityType: "persona" })
list_all_entities({ entityType: "product" })
list_all_entities({ entityType: "competitor" })
search_knowledge_base({ query: "value propositions proof points" })
list_all_entities({ entityType: "use_case" })
Step Q-3: Run the Quiz
Rapid Fire Format (10 questions):
Ask each question one at a time. After the user answers, score immediately, then move on.
Question types per topic:
Personas:
- "What are the top 3 pain points for [Persona]?"
- "What's the primary KPI that [Persona] is measured on?"
- "Which value prop resonates most with [Persona]?"
- "What's the best discovery question to open with for [Persona]?"
- "What proof point would you use with [Persona]?"
Objections:
- "A prospect says '[Real objection from data]'. How do you respond?"
- "What's the biggest pricing objection you'll face, and what's the counter?"
- "A [Persona] says 'We're happy with what we have.' What do you say?"
- "How do you handle 'It's too expensive' without discounting?"
- "What reframe do you use when they say '[Feature] is missing'?"
Competitive:
- "What are our top 3 differentiators vs [Competitor]?"
- "A prospect says '[Competitor] is cheaper.' How do you respond?"
- "What trap question exposes [Competitor]'s weakness in [area]?"
- "When should you NOT try to displace [Competitor]?"
- "What proof point works best in a head-to-head against [Competitor]?"
Full GTM:
- Mix of all the above, rotating through topics
Present each question as:
QUESTION [N]/10
===============
[Question text]
Your answer:
After the user answers:
[Correct / Partially correct / Needs work]
Your answer: "[Their answer summarized]"
Best answer: "[From library data]"
[Brief coaching note if needed]
---
Next question...
Scenario-Based Format (5 scenarios):
SCENARIO [N]/5
==============
You're on a [call type] with [Persona Name], [Title] at a [company description].
[Setup: 2-3 sentences of context]
They say: "[Realistic statement or objection from conversation data]"
What do you do? (Describe your response and reasoning)
After the user answers, provide detailed coaching:
YOUR RESPONSE
-------------
[Summary of what they said]
ASSESSMENT: [Strong / Good / Needs work]
What worked:
- [Specific positive element]
What to improve:
- [Specific gap with coaching]
Ideal approach:
"[Best response from playbook + conversation evidence]"
Why this works better:
[Explanation tied to persona pain points and value props]
Deep Dive Format (3 questions):
Same as rapid fire, but after each answer, enter a coaching dialogue:
- Ask follow-up questions
- Challenge their reasoning
- Share what works from conversation data
- Connect to playbook guidance
Step Q-4: Quiz Results
QUIZ RESULTS
=============
Score: [X]/[Total] ([Percentage]%)
Topic: [Topic]
Format: [Format]
---
BREAKDOWN
---------
| # | Question | Score | Notes |
|---|----------|-------|-------|
| 1 | [Brief question] | [Correct/Partial/Wrong] | [One-line note] |
| 2 | [Brief question] | [Correct/Partial/Wrong] | [One-line note] |
| ... | ... | ... | ... |
---
STRENGTHS
---------
- [Area where they scored well]
- [Another strength]
GAPS TO FOCUS ON
-----------------
- [Topic they struggled with] — Review: [specific library entity or playbook section]
- [Another gap] — Practice: [specific recommendation]
---
RECOMMENDED NEXT STEPS
-----------------------
1. Review [specific entity] in your library: /octave:library show [entity]
2. Role-play [specific scenario] to practice: /octave:train roleplay --scenario [type]
3. Read the [playbook name] playbook for [gap area]
---
Want to:
1. Retake this quiz
2. Try a different topic
3. Switch to role-play mode
4. Deep dive on a question I got wrong
5. Done
Mode: Guided Learning
Walk through a topic from the library like a training session.
Step GL-1: Choose Topic
AskUserQuestion({
questions: [{
question: "What do you want to learn about?",
header: "Topic",
options: [
{ label: "A persona", description: "Deep walkthrough of how to sell to a specific buyer type" },
{ label: "A competitor", description: "Learn competitive positioning, differentiators, and counters" },
{ label: "A playbook", description: "Walk through a playbook's strategy, value props, and objection handling" },
{ label: "Your product", description: "Master your product's capabilities, use cases, and proof points" }
],
multiSelect: false
}]
})
Step GL-2: Load and Teach
Fetch the relevant entity and present it as a structured training walkthrough:
# Load the entity
get_entity({ oId: "<entity_oId>" })
# Load related playbook
get_playbook({ oId: "<playbook_oId>", includeValueProps: true })
# Load real conversation examples
list_findings({
query: "<topic>",
startDate: "<180 days ago>"
})
# Load proof points
search_knowledge_base({ query: "<topic>", entityTypes: ["proof_point", "reference"] })
Present as an interactive lesson:
TRAINING: Selling to [Persona Name]
=====================================
LESSON 1: Who They Are
-----------------------
[Overview of the persona — role, responsibilities, what they care about]
Quick check: Can you name their top 3 priorities? (Try before scrolling down)
Answer:
1. [Priority 1]
2. [Priority 2]
3. [Priority 3]
---
LESSON 2: Their Pain Points
-----------------------------
[Detailed pain points with context]
From real conversations:
- "[Quote from conversation findings]" — shows [insight]
---
LESSON 3: How to Open
-----------------------
[Best discovery questions for this persona]
[What to listen for]
[Common mistakes to avoid]
---
LESSON 4: Value Props That Work
---------------------------------
[Top value props from playbook, with proof points]
From the field:
- [What's been resonating in real conversations]
- [What's NOT working]
---
LESSON 5: Objections to Expect
---------------------------------
[Common objections with responses]
From real deals:
- [Objections from won deals and how they were handled]
- [Objections from lost deals and what went wrong]
---
LESSON 6: Competitive Angles
------------------------------
[If competitors exist]
[Key differentiators vs each competitor for this persona]
---
SUMMARY CARD
--------------
[Condensed reference card — everything above in 10 lines]
---
Ready to test yourself?
1. Quick quiz on this persona
2. Role-play as this persona (I'll play the buyer)
3. Learn about a different topic
4. Done
MCP Tools Used
Library Context
list_all_entities- List personas, products, competitors, use casesget_entity- Full entity details (persona pain points, competitor weaknesses, etc.)get_playbook- Playbook with value props, discovery questions, objection handlinglist_value_props- Value propositions by personasearch_knowledge_base- Proof points, references, messaging
Conversation Evidence
list_findings- Real objections, pain points, and signals from calls/emailslist_events- Deal outcomes (win/loss evidence for coaching)get_event_detail- Specific interaction details for training examples
Content Generation
generate_content- Generate scenario descriptions, coaching feedback
Error Handling
No Personas in Library:
No personas found in your library.
Role-play and quizzes work best with persona data. Add personas first:
/octave:library create personaI can still run a general sales quiz using your product info.
No Conversation Data:
No conversation data available yet.
I'll use your library data for role-play and quizzes. As your team logs calls and emails, training will get richer with real-world objections and patterns.
No Competitors:
No competitors in your library.
Competitive quizzes and displacement role-plays need competitor data. Add competitors:
/octave:library create competitorI can still quiz you on personas, value props, and general objection handling.
Sparse Library:
Your library has limited data for a full training session.
Start with:
/octave:library create product- Add your product/octave:library create persona- Add buyer personas/octave:library create competitor- Add competitorsEven with just a product and one persona, I can run basic training.
Related Skills
/octave:enablement- Generate training materials (cheat sheets, objection guides, discovery banks)/octave:battlecard- Deep competitive intelligence for competitive training/octave:insights- Surface real field intelligence to inform training/octave:wins-losses- Win/loss patterns to learn from/octave:research- Research a real prospect before a live call/octave:generate- Generate real outreach after practicing
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