# Rep Profile > Hyper-personalization engine that adapts all enablement content to each rep's skill level, experience, deal patterns, and learning style. Use this skill whenever interacting with a specific rep — it adjusts the depth, complexity, and focus of every other skill's output. Also trigger when a manager wants to understand a rep's development trajectory, when building personalized coaching plans, or when someone says "adapt this for [rep name]", "what does [rep] need to work on", or when onboarding a new rep. This skill should be checked automatically by other skills to personalize their output. Use when this capability is needed. - Skill: `tomevault-io/rep-profile` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/rep-profile` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/rep-profile/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/rep-profile --- # Rep Profile Makes every interaction feel like it was designed specifically for this rep. A first-week SDR and a ten-year AE should get fundamentally different experiences from the same plugin — different depth, different language, different focus areas, different challenges. ## Why This Matters "Hyper-personalized learning" isn't about adding a name to a template. It means: - A rep who crushes discovery but struggles with closing gets coaching focused on negotiation - A rep who just joined gets scaffolded frameworks; a veteran gets contextual nudges - A rep who learns by doing gets role-play practice; one who learns by studying gets frameworks and examples - Content complexity scales with the rep's experience and comfort level --- ## How It Works ``` ┌─────────────────────────────────────────────────────────────────┐ │ REP PROFILE │ ├─────────────────────────────────────────────────────────────────┤ │ PROFILE COMPONENTS │ │ • Skill assessment (scored competencies) │ │ • Experience level (tenure, deals closed, ramp stage) │ │ • Deal patterns (what they win, what they lose, why) │ │ • Learning style (doing, studying, observing, discussing) │ │ • Development plan (current focus areas and progress) │ │ • Interaction history (what help they've asked for before) │ ├─────────────────────────────────────────────────────────────────┤ │ ADAPTATION RULES │ │ New rep → More structure, more scaffolding, explicit frameworks │ │ Mid-level → Balanced guidance, focus on weak spots │ │ Senior rep → Brief nudges, advanced scenarios, edge cases │ │ Manager → Coaching lens, team patterns, data-driven insights │ ├─────────────────────────────────────────────────────────────────┤ │ SUPERCHARGED (when you connect your tools) │ │ + ~~CRM: Deal history, win rates, cycle lengths, quota data │ │ + ~~CRM: Stage-specific patterns and performance vs team avg │ │ + ~~conversation intelligence (Gong): Talk-to-listen ratios │ │ + ~~conversation intelligence (Gong): Questions per call │ │ + ~~conversation intelligence (Gong): Competitor handling skill │ │ + ~~conversation intelligence (Gong): Next steps discipline │ │ + ~~data enrichment (LinkedIn): Career history and expertise │ │ + ~~data enrichment (ZoomInfo): Industry vertical experience │ │ + ~~chat: Coaching conversations and peer feedback │ └─────────────────────────────────────────────────────────────────┘ ``` --- ## Profile Structure Stored in `memory/team.md` with a section per rep: ```markdown ## [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: ```markdown # 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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/jbalbu01) — claim your Tome and manage your conversions.