Lead Routing logo

Lead Routing

Organization
claude-office-skills
lead-routing

Intelligent lead assignment and routing - AI-powered scoring, territory mapping, round-robin distribution, and workload balancing

Overview

Publisherclaude-office-skills
Repositoryskills
Skill namelead-routing
Stars
514
Forks
97
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 claude-office-skills on GitHub. Read the source before you install it.

Installation

Install the Lead Routing 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/claude-office-skills/skills/tree/main/lead-routing
  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/claude-office-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/lead-routing .claude/skills/lead-routing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lead Routing 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 Lead Routing 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 Lead Routing 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.

Lead Routing

Intelligent lead assignment and routing system with AI-powered scoring, territory mapping, round-robin distribution, and workload balancing. Based on n8n's HubSpot/Salesforce automation templates.

Overview

This skill covers:

  • Lead scoring and qualification
  • Territory-based routing
  • Round-robin distribution
  • Workload balancing
  • SLA monitoring and escalation

Routing Strategies

1. Rule-Based Routing

yaml
routing_rules:
  # By Company Size
  - name: "Enterprise Routing"
    condition:
      company_size: ">= 500"
      OR:
        annual_revenue: ">= $10M"
    assign_to: "Enterprise Team"
    priority: high
    sla: 1_hour
    
  - name: "Mid-Market Routing"
    condition:
      company_size: "100-499"
    assign_to: "Mid-Market Team"
    priority: medium
    sla: 4_hours
    
  - name: "SMB Routing"
    condition:
      company_size: "< 100"
    assign_to: "SMB Team"
    priority: standard
    sla: 24_hours

  # By Geography
  - name: "APAC Routing"
    condition:
      country: ["China", "Japan", "Singapore", "Australia"]
    assign_to: "APAC Team"
    timezone_aware: true
    
  - name: "EMEA Routing"
    condition:
      country: ["UK", "Germany", "France", "Netherlands"]
    assign_to: "EMEA Team"
    
  - name: "Americas Routing"
    condition:
      country: ["US", "Canada", "Brazil", "Mexico"]
    assign_to: "Americas Team"

  # By Industry
  - name: "Healthcare Specialist"
    condition:
      industry: ["Healthcare", "Pharmaceuticals", "Medical Devices"]
    assign_to: "Healthcare Sales"
    
  - name: "Finance Specialist"
    condition:
      industry: ["Banking", "Insurance", "FinTech"]
    assign_to: "Financial Services Sales"

2. Round-Robin Distribution

yaml
round_robin_config:
  team: "SMB Sales"
  members:
    - name: Alice
      capacity: 100%
      max_leads_per_day: 20
      
    - name: Bob
      capacity: 100%
      max_leads_per_day: 20
      
    - name: Carol
      capacity: 50%  # Part-time
      max_leads_per_day: 10
      
  rules:
    distribution: weighted  # or equal
    skip_if:
      - out_of_office: true
      - at_capacity: true
    reset: daily
    
  tracking:
    log_assignments: true
    balance_check: hourly

Distribution Algorithm:

┌─────────────────────────────────────────────────────────────┐
│                   ROUND-ROBIN LOGIC                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. New lead arrives                                        │
│                    │                                        │
│                    ▼                                        │
│  2. Check team availability                                 │
│     - Filter out: OOO, at capacity, off-hours              │
│                    │                                        │
│                    ▼                                        │
│  3. Calculate weighted position                             │
│     - Current assignments today                             │
│     - Capacity percentage                                   │
│     - Last assignment time                                  │
│                    │                                        │
│                    ▼                                        │
│  4. Assign to rep with lowest weighted score               │
│                    │                                        │
│                    ▼                                        │
│  5. Update tracking, notify rep                            │
│                                                             │
└─────────────────────────────────────────────────────────────┘

3. AI-Powered Lead Scoring

yaml
ai_scoring:
  provider: openai
  model: gpt-4
  
  input_factors:
    demographic:
      - company_size
      - industry
      - job_title
      - location
      
    firmographic:
      - annual_revenue
      - employee_count
      - funding_stage
      - tech_stack
      
    behavioral:
      - pages_visited
      - content_downloads
      - email_engagement
      - demo_requests
      
    fit_score:
      - icp_match_percentage
      - competitor_usage
      - budget_authority
      
  scoring_prompt: |
    Score this lead from 0-100 based on:
    
    Our ICP (Ideal Customer Profile):
    - B2B SaaS companies
    - 50-500 employees
    - Series A or later
    - Using {competitor} or {similar_tool}
    
    Lead Data:
    {lead_data}
    
    Return JSON:
    {
      "score": 0-100,
      "fit_score": 0-100,
      "intent_score": 0-100,
      "tier": "A/B/C/D",
      "reasoning": "...",
      "recommended_action": "...",
      "routing_suggestion": "..."
    }

  tier_thresholds:
    A: 80-100  # Hot lead, immediate follow-up
    B: 60-79   # Qualified, standard follow-up
    C: 40-59   # Nurture, marketing sequence
    D: 0-39    # Low priority, long-term nurture

4. Territory Mapping

yaml
territory_map:
  north_america:
    west:
      states: [CA, WA, OR, NV, AZ, CO, UT]
      owner: "West Coast Team"
      reps: [Alice, Bob]
      
    central:
      states: [TX, IL, OH, MI, MN, WI]
      owner: "Central Team"
      reps: [Carol, David]
      
    east:
      states: [NY, MA, PA, FL, GA, NC]
      owner: "East Coast Team"
      reps: [Eve, Frank]
      
  international:
    emea:
      countries: [UK, DE, FR, NL, ES, IT]
      owner: "EMEA Team"
      timezone: "Europe/London"
      
    apac:
      countries: [JP, SG, AU, KR, IN]
      owner: "APAC Team"
      timezone: "Asia/Tokyo"

  overlap_resolution:
    # When lead matches multiple territories
    priority_order:
      1: named_account_owner  # If account already has owner
      2: industry_specialist  # If industry requires specialist
      3: geography           # Default to geography

5. Workload Balancing

yaml
workload_balancer:
  check_frequency: hourly
  
  metrics_tracked:
    - current_open_leads
    - leads_assigned_today
    - leads_assigned_this_week
    - average_response_time
    - conversion_rate
    
  balance_rules:
    max_variance: 20%  # Max difference between reps
    
    rebalance_trigger:
      - variance > max_variance
      - rep_at_capacity
      - rep_underperforming
      
    rebalance_actions:
      - pause_assignments: for_overloaded_rep
      - increase_weight: for_underloaded_rep
      - notify_manager: when_rebalancing
      
  capacity_management:
    per_rep:
      max_open_leads: 50
      max_new_per_day: 15
      max_new_per_week: 60
      
    team_level:
      overflow_queue: true
      overflow_notify: sales_manager
      escalation_threshold: 2_hours

Workflow Implementation

Complete Lead Routing Workflow

yaml
workflow: "Intelligent Lead Router"

trigger:
  - type: hubspot_contact_created
  - type: form_submission
  - type: api_webhook

steps:
  1. enrich_lead:
      providers: [clearbit, zoominfo]
      fields:
        - company_size
        - industry
        - revenue
        - location
        - linkedin_url
        
  2. score_lead:
      method: ai_scoring
      store_result:
        hubspot_property: lead_score
        
  3. determine_tier:
      A_tier: score >= 80
      B_tier: score >= 60
      C_tier: score >= 40
      D_tier: score < 40
      
  4. apply_routing_rules:
      sequence:
        - check: named_account_owner
        - check: industry_specialist
        - check: territory_match
        - check: round_robin_availability
        
  5. assign_owner:
      hubspot:
        update_contact:
          hubspot_owner_id: "{selected_owner_id}"
          lead_status: "New"
          lead_tier: "{tier}"
          routing_reason: "{routing_logic}"
          
  6. create_task:
      hubspot:
        type: CALL
        subject: "Follow up: New {tier} lead - {company}"
        due_date: "{sla_deadline}"
        priority: "{priority_based_on_tier}"
        notes: |
          Lead Score: {score}
          Routing Reason: {routing_reason}
          Key Info: {summary}
          
  7. notify_owner:
      slack_dm:
        message: |
          🎯 *New Lead Assigned*
          
          **{contact_name}** at **{company}**
          Score: {score} ({tier} Tier)
          
          📞 SLA: Respond within {sla_time}
          
          Quick actions:
          • [View in HubSpot]({hubspot_link})
          • [LinkedIn]({linkedin_url})
          • [Schedule Call]({calendly_link})
          
  8. start_sla_timer:
      deadline: "{sla_deadline}"
      escalation_path:
        - 50%_elapsed: reminder_to_owner
        - 80%_elapsed: notify_manager
        - 100%_elapsed: reassign + alert

SLA Management

yaml
sla_tiers:
  tier_a:
    response_time: 1_hour
    escalation_path:
      - 30min: slack_reminder
      - 45min: manager_alert
      - 60min: auto_reassign
      
  tier_b:
    response_time: 4_hours
    escalation_path:
      - 2h: slack_reminder
      - 3h: manager_alert
      - 4h: auto_reassign
      
  tier_c:
    response_time: 24_hours
    escalation_path:
      - 12h: slack_reminder
      - 20h: manager_alert
      - 24h: move_to_queue

sla_reporting:
  metrics:
    - response_time_avg
    - response_time_p90
    - sla_compliance_rate
    - escalation_count
    
  report_frequency: weekly
  recipients: [sales_manager, ops_manager]

Reporting Dashboard

markdown
# Lead Routing Report - {Week}

## Distribution Summary
| Rep | Assigned | Responded | Avg Response | SLA Met |
|-----|----------|-----------|--------------|---------|
| Alice | 45 | 43 | 1.2h | 96% |
| Bob | 42 | 40 | 1.8h | 90% |
| Carol | 38 | 38 | 0.8h | 100% |
| **Total** | **125** | **121** | **1.3h** | **95%** |

## By Tier
| Tier | Count | Avg Score | Converted | Conv Rate |
|------|-------|-----------|-----------|-----------|
| A | 25 | 87 | 12 | 48% |
| B | 45 | 68 | 15 | 33% |
| C | 35 | 52 | 5 | 14% |
| D | 20 | 28 | 1 | 5% |

## Routing Breakdown
- By Territory: 60%
- By Industry: 25%
- Round Robin: 15%

## Issues
- 3 leads waited >SLA (reassigned)
- Alice at 95% capacity (monitor)
- No coverage for Healthcare vertical (gap)

## Recommendations
1. Hire Healthcare specialist
2. Increase Bob's training (response time)
3. Adjust A-tier threshold to 85 (too many false positives)

Output Example

Request: "Route this lead: John Smith, CTO at TechCorp (500 employees, SF, SaaS)"

Output:

markdown
# Lead Routing Decision

## Lead Profile
- **Name**: John Smith
- **Title**: CTO
- **Company**: TechCorp
- **Size**: 500 employees
- **Location**: San Francisco, CA
- **Industry**: SaaS

## AI Scoring
```json
{
  "score": 85,
  "fit_score": 90,
  "intent_score": 80,
  "tier": "A",
  "reasoning": "Strong ICP fit - CTO at 500-person SaaS company in our target market. High authority buyer.",
  "recommended_action": "Immediate outreach - high-value prospect"
}

Routing Decision

Assigned to: Alice Chen (Enterprise West)

Routing Logic:

  1. ✅ Territory: San Francisco → West Coast
  2. ✅ Company Size: 500 → Enterprise tier
  3. ✅ Industry: SaaS → No specialist needed
  4. ✅ Availability: Alice has capacity (18/20 today)

Action Items Created

  1. Task: Follow up call

    • Due: 1 hour (Tier A SLA)
    • Priority: High
  2. Slack Notification: Sent to Alice

  3. SLA Timer: Started (1h countdown)

Recommended Outreach

Subject: Quick question about {pain_point} at TechCorp

Hi John,

Noticed TechCorp is scaling fast - congrats on the growth. 

CTOs at similar SaaS companies often tell us {common_challenge}. 

Would a 15-min call this week make sense to see if we can help?

[Calendly Link]

---

*Lead Routing Skill - Part of Claude Office Skills*

Frequently asked questions

What does the Lead Routing AI skill do?

Intelligent lead assignment and routing - AI-powered scoring, territory mapping, round-robin distribution, and workload balancing

Why use Lead Routing on TypingMind?

Because you install it once and use it with any model. Lead Routing 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 Lead Routing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/claude-office-skills/skills/tree/main/lead-routing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lead Routing?

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 Lead Routing?

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

Is the Lead Routing AI skill free?

Yes. It is published on GitHub by claude-office-skills 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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