Rep Profile

Profile Structure

Stored in memory/team.md with a section per rep:

## [Rep Name]

**Role:** [AE / SDR / SE / Manager]
**Start Date:** [When they joined]
**Ramp Stage:** [Ramping / Productive / Senior / Top Performer]
**Deals Closed (All Time):** [N]
**Current Quarter Performance:** [X]% of quota

### Skill Scores (1-5)
| Skill | Score | Trend | Last Assessed |
|-------|-------|-------|---------------|
| Discovery | [1-5] | ↑↓→ | [Date] |
| Objection handling | [1-5] | ↑↓→ | [Date] |
| Demo/presentation | [1-5] | ↑↓→ | [Date] |
| Negotiation/closing | [1-5] | ↑↓→ | [Date] |
| Qualification | [1-5] | ↑↓→ | [Date] |
| Business acumen | [1-5] | ↑↓→ | [Date] |
| Pipeline management | [1-5] | ↑↓→ | [Date] |
| Written communication | [1-5] | ↑↓→ | [Date] |

### Deal Patterns
**Wins when:** [Patterns from their successful deals]
**Loses when:** [Patterns from their losses]
**Sweet spot:** [Deal types/sizes where they excel]
**Growth area:** [Deal types where they struggle]

### Learning Style
**Preferred:** [Doing / Studying / Observing / Discussing]
**Responds well to:** [Specific coaching approaches that work]
**Doesn't respond to:** [Approaches that don't land]

### Current Development Focus
**Primary:** [Skill being developed]
**Secondary:** [Skill queued]
**Progress:** [Description of recent improvement or stalls]

### Interaction Log
| Date | Skill Used | Topic | Outcome |
|------|-----------|-------|---------|
| [Date] | objection-handling | Price objection practice | Improved — less defensive |
| [Date] | discovery-guide | SPIN prep for Acme | Good call, uncovered budget |

Adaptation Rules

When any skill generates output for a rep with a profile, adapt the output:

For New Reps (< 90 days, ramp stage)

• Always include the full framework explanation (don't assume they know SPIN, MEDDIC, etc.)
• Provide templates they can follow word-for-word
• Add context for why each step matters
• Include checklists so nothing gets missed
• Tone: Supportive, educational, encouraging

For Mid-Level Reps (90 days - 2 years)

• Skip basics — reference frameworks by name without re-explaining
• Focus on their weak spots — if they score 2/5 on negotiation, weight content toward that
• Include nuance — edge cases, when to break the rules, situational judgment
• Challenge them — "What would you do differently if the champion left?"
• Tone: Collaborative, coaching-oriented

For Senior Reps (2+ years, top performers)

• Be brief — they don't need hand-holding
• Provide intel, not instructions — competitive data, deal insights, customer patterns
• Focus on advanced scenarios — multi-threaded deals, executive selling, complex negotiations
• Ask their opinion — "You've seen this before — what's worked?"
• Tone: Peer, strategic partner

For Managers

• Data-driven — metrics, trends, comparisons
• Team-level patterns — not just individual deals
• Coaching-ready — frame insights as coaching conversation starters
• Action-oriented — "Here's what to focus on in your 1:1s this week"
• Tone: Strategic, analytical

Building a Profile

From Scratch

When you don't have a profile yet:

1. Ask role and experience level
2. Ask about recent deals (2-3 wins and losses)
3. Ask what they feel strongest/weakest at
4. Ask their manager for input (if available)
5. Create initial profile in memory/team.md

From Interactions

Every time a rep uses the plugin:

• Note what they asked for help with (signals a gap)
• Note what they didn't need help with (signals strength)
• After coaching sessions, update skill scores
• After deal outcomes, update deal patterns
• Track improvement trends over time

From Data (Automatic — Highest Quality)

CRM Data Pull

Check if you have access to CRM tools (look for tools containing search_crm_objects, get_crm_objects, or similar).

If CRM tools ARE available:

1. Pull rep's deals. Search deals filtered by hubspot_owner_id.
   • Properties: dealname, amount, dealstage, closedate, createdate, pipeline, dealtype, hs_deal_stage_probability
   • Separate won, lost, and open deals
2. Calculate performance metrics:
   • Win rate = Closed Won / (Closed Won + Closed Lost)
   • Avg deal size = Mean of amount across won deals
   • Avg cycle length = Mean days from createdate to closedate for won deals
   • Pipeline coverage = Open pipeline value / quota (ask user for quota if needed)
3. Compare to team averages. Pull all reps' deals and compute team-level metrics.
   • Flag where this rep is significantly above or below average
4. Identify stage-specific patterns:
   • Where do their deals stall? (avg days in each stage vs. team)
   • Where do they lose? (stage distribution of lost deals vs. team)
   • Deal types they excel at vs. struggle with
5. Map rep name. Use search_owners to translate owner ID.

Gong Data Pull

Check if you have access to Gong tools (look for tools prefixed with gong_).

If Gong tools ARE available:

1. Pull call stats. Use gong_get_call_stats for the rep's recent period.
   • Total calls, average duration, average questions per call
2. Analyze call patterns. Use gong_search_calls_by_participant with the rep's email, then gong_get_call_details on 5-10 calls:
   • Average talk-to-listen ratio → maps to Discovery & Questioning skill
   • Average questions per call → Discovery skill indicator
   • Competitor mention frequency → Competitive handling skill
   • Next steps confirmation rate → Closing discipline
   • Topic distribution → Where they spend conversation time
3. Build data-driven skill scores:
   • Talk ratio > 55% → Lower Discovery score
   • < 5 questions per call → Lower Discovery score
   • No next steps in > 30% of calls → Lower Closing score
   • Low competitor mention handling → Lower Objection Handling score

Sales Intelligence Data Pull (ZoomInfo / Clay / LinkedIn)

ZoomInfo (check for tools prefixed with zoominfo_):

1. Validate industry expertise. Use zoominfo_search_company on the rep's won deal companies.
   • Which industries does this rep win in most? → vertical specialization signal
   • What company sizes do they close? → segment fit indicator

Clay (check for tools prefixed with clay_):

1. Enrich deal context. Use clay_enrich_company on rep's recent deals.
   • Were their wins at companies with buying signals? → luck vs skill indicator

LinkedIn (check for tools prefixed with linkedin_):

1. Get rep's LinkedIn profile. Use linkedin_get_profile if rep's LinkedIn URL is known.
   • Career history reveals experience level and domain expertise
   • Endorsements/skills signal areas of strength
   • Previous companies/industries → domain knowledge map

Auto-Generated Profile

When tools are connected, auto-generate the profile without asking the user:

"I built [Rep Name]'s profile from data: [X]% win rate (team avg: [Y]%), $[X] avg deal size, [X]-day cycle. Per Gong, their talk-to-listen ratio is [X:Y] across [N] calls, and they ask an average of [N] questions. Their strongest skill appears to be [Skill] and the biggest growth opportunity is [Skill]."

Profile Dashboard

When a manager or rep wants to see the profile:

# Rep Profile: [Name]

**Performance Snapshot**
| Metric | This Quarter | Last Quarter | Team Avg |
|--------|-------------|-------------|----------|
| Quota Attainment | [X]% | [X]% | [X]% |
| Win Rate | [X]% | [X]% | [X]% |
| Avg Deal Size | $[X] | $[X] | $[X] |
| Avg Cycle Length | [X] days | [X] days | [X] days |

**Skill Map** [Visual representation of strengths and gaps]

**Top Priority:** [The one skill that would most impact their numbers]

**Recommended This Week:**
1. [Specific practice exercise using plugin skill]
2. [Call to review for coaching moment]
3. [Content to study]

Related Skills

• sales-coaching → Updates skill scores after coaching sessions
• win-loss-analysis → Updates deal patterns after post-mortems
• All skills → Read rep profile to personalize output depth and focus
• gtm-memory → Rep profiles are stored in the team.md memory file