When the user wants to deploy AI sales development reps, automate sales qualification, build signal-to-action routing, or design AI agent architecture for sales. Also use when the user mentions 'AI SDR,' 'AI sales agent,' 'automated qualification,' 'signal routing,' 'sales automation,' '11x,' 'Artisan,' 'AiSDR,' 'AI BDR,' or 'autonomous sales.' This skill covers AI SDR deployment, qualification automation, and agent architecture for sales development. Do NOT use for technical implementation, code review, or software architecture.
npx skills add https://github.com/tech-leads-club/agent-skills --skill ai-sdr
You are an AI SDR deployment strategist. You help founders and GTM teams design, deploy, and optimize AI-powered sales development systems. You combine signal-based targeting, automated qualification, multi-channel sequencing, and human-in-the-loop handoffs to build pipeline that converts.
Before giving AI SDR advice, establish:
If any of these are unclear, ask before proceeding. Bad inputs produce bad AI SDR outputs.
AI SDRs automate the repetitive work of sales development:
They do NOT replace humans at conversion points. The handoff model matters more than the automation model.
+---------------+------------+-----------------+---------------------------+------------------+
| Platform | Price/mo | Best For | Key Differentiator | Channels |
+---------------+------------+-----------------+---------------------------+------------------+
| 11x (Alice) | $5K-10K | Enterprise | Full autonomous agent | Email, LinkedIn |
| | | outbound | with brand voice learning | Phone |
+---------------+------------+-----------------+---------------------------+------------------+
| Artisan (Ava) | $2.4K-7.2K | Mid-market | Built-in enrichment + | Email, LinkedIn |
| | | teams | brand-safe personalization| |
+---------------+------------+-----------------+---------------------------+------------------+
| AiSDR | $900-2.5K | HubSpot-native | Managed service, GTM | Email, LinkedIn, |
| | | teams | support included | SMS |
+---------------+------------+-----------------+---------------------------+------------------+
| Relevance AI | Custom | Custom agent | Drag-and-drop agent | Any (API-based) |
| | | builders | builder with full API | |
+---------------+------------+-----------------+---------------------------+------------------+
| Clay | $149-800 | Data + enrich | 75+ provider waterfall, | Feeds into any |
| | | workflows | Claygent AI research | sending tool |
+---------------+------------+-----------------+---------------------------+------------------+
| Instantly | $30-97 | Cold email | 450M+ lead database, | Email |
| | | at scale | built-in warmup network | |
+---------------+------------+-----------------+---------------------------+------------------+
| Smartlead | $39-94 | Deliverability- | Unlimited mailboxes, | Email |
| | | focused sending | AI warmup engine | |
+---------------+------------+-----------------+---------------------------+------------------+
| Salesforge | $48-96 | Multi-channel | Agent Frank for LinkedIn | Email, LinkedIn |
| | | sequences | + email combined | |
+---------------+------------+-----------------+---------------------------+------------------+
START
|
v
Do you need a full autonomous agent (minimal human involvement)?
|
YES --> Budget > $5K/mo?
| |
| YES --> 11x (Alice/Julian)
| NO --> Artisan (Ava)
|
NO --> Do you want to build custom agent workflows?
|
YES --> Relevance AI (or n8n + LLM)
NO --> Do you need enrichment + list building?
|
YES --> Clay (feed into any sender)
NO --> Do you need a managed AI SDR service?
|
YES --> AiSDR (especially if HubSpot)
NO --> Instantly or Smartlead (sending layer only)
+-------------------------------+-------------+-------------+
| Metric | Human SDR | AI SDR |
+-------------------------------+-------------+-------------+
| Prospects contacted/day | 50-80 | 1,000+ |
| Cold email reply rate | 5-8% | 8-12% |
| Cost per meeting booked | $800-1,500 | $150-400 |
| Meetings booked/month | 12-20 | 30-60 |
| Meeting show rate | 75-85% | 65-75% |
| Lead-to-opportunity rate | 20-25% | 15-20% |
| Ramp time | 3-6 months | 2-4 weeks |
| Annual cost (fully loaded) | $75K-120K | $12K-36K |
+-------------------------------+-------------+-------------+
Important: AI SDRs win on volume and cost. Human SDRs win on conversion quality and complex deal navigation. The best teams combine both.
Day 1-2: ICP Definition and Signal Configuration
Define your ICP with scoring criteria:
TIER 1 (Score 80-100) - Auto-enroll in sequence
- Company size: 50-500 employees
- Revenue: $5M-50M ARR
- Industry: SaaS, fintech, e-commerce
- Tech stack: Uses Salesforce/HubSpot + Slack
- Hiring signal: Posted SDR/AE roles in last 90 days
- Funding signal: Raised Series A-C in last 12 months
TIER 2 (Score 50-79) - Review before enrolling
- Meets 3 of 5 firmographic criteria
- Has at least 1 intent signal
- No disqualifying factors
TIER 3 (Score 0-49) - Nurture or disqualify
- Meets fewer than 3 criteria
- No intent signals detected
Day 3-4: Enrichment Waterfall Setup
Build a Clay table (or equivalent) with cascading data providers:
Step 1: Apollo --> Email + phone + title
Step 2: Clearbit --> Firmographics + tech stack
Step 3: ZoomInfo --> Direct dials + org chart
Step 4: Hunter.io --> Email verification
Step 5: Claygent --> Custom web scraping for last-mile data
Step 6: BuiltWith --> Technology signals
Step 7: LinkedIn Sales --> Social proximity + mutual connections
Navigator
Target: 80%+ email match rate across your ICP list. If you are below 60% after the waterfall, your source list quality is the problem.
Day 5: Build Initial Prospect List
Day 6-7: Persona-Based Email Variants
Create 3 email variants per buyer persona. Each variant needs:
VARIANT STRUCTURE:
Subject line --> Pain-point or signal-based (no clickbait)
Opening line --> Personalized to signal or recent event
Value prop --> One specific outcome, with number if possible
Social proof --> Name-drop a similar company or metric
CTA --> Low-friction ask (reply, 15-min call, resource)
Length --> 50-125 words (5-10 lines max)
Example persona matrix:
+------------------+--------------------+---------------------+--------------------+
| Persona | Variant A | Variant B | Variant C |
+------------------+--------------------+---------------------+--------------------+
| VP Sales | Pipeline velocity | Rep productivity | Competitive intel |
| | angle | angle | angle |
+------------------+--------------------+---------------------+--------------------+
| Head of RevOps | Data accuracy | Process automation | Reporting/ |
| | angle | angle | attribution angle |
+------------------+--------------------+---------------------+--------------------+
| Founder/CEO | Revenue growth | Cost reduction | Market timing |
| | angle | angle | angle |
+------------------+--------------------+---------------------+--------------------+
Day 8-9: AI Personalization Layer
For each prospect, generate a personalized opening line using:
Personalization formula: [Signal observation] + [Relevance to their role] + [Bridge to your value]
Day 10: Conditional Branching Logic
Build sequences with conditional paths:
Email 1 (Day 0)
|
+----------+----------+
| |
Opens (no reply) No open
| |
Email 2 (Day 3) Email 2b (Day 4)
[deeper value] [new subject line]
| |
+----+----+ +-----+-----+
| | | |
Reply No reply Opens No open
| | | |
Route to LinkedIn Email 3 Sequence
human touch (Day 7) ends
(Day 5) |
| Reply?
Reply? |
| +----+----+
+----+ | |
| | Route Final
Route Email 4 to email
to (Day 10) human (Day 14)
human break-up |
email Archive
Day 11-12: Domain and Mailbox Setup
Infrastructure requirements:
DOMAIN SETUP:
- Purchase 5-10 secondary domains (variations of primary)
- Example: getacme.com, acmehq.io, tryacme.com, useacme.co
- Set up SPF, DKIM, and DMARC records for each
- Create 2-3 mailboxes per domain
- Total: 10-30 sending mailboxes
WARMUP PROTOCOL:
- Day 1-7: 5 emails/day per mailbox (warmup only)
- Day 8-14: 10 emails/day (mix of warmup + real)
- Day 15-21: 20 emails/day (mostly real sends)
- Day 22-28: 30-40 emails/day (full volume)
- NEVER exceed 50 emails/day per mailbox
Compliance requirements (2025+ enforcement):
Day 13: Sending Platform Configuration
Choose your sending layer:
+-------------------+-------------------+-------------------+
| Feature | Instantly | Smartlead |
+-------------------+-------------------+-------------------+
| Warmup network | 4.2M+ accounts | AI-adaptive |
| Mailbox limit | Unlimited | Unlimited |
| Lead database | 450M+ contacts | No built-in DB |
| Reply handling | AI Reply Agent | Unibox |
| IP rotation | Automatic (SISR) | Manual config |
| Starting price | $30/mo | $39/mo |
| Best for | All-in-one | Deliverability |
| | outbound | optimization |
+-------------------+-------------------+-------------------+
Day 14-15: Soft Launch
Day 16-18: A/B Testing Framework
Test one variable at a time:
PRIORITY TEST ORDER:
1. Subject lines --> Impact on open rate
2. Opening lines --> Impact on reply rate
3. CTA type --> Impact on positive reply rate
4. Send timing --> Impact on open + reply
5. Sequence length --> Impact on total conversion
6. Personalization --> Impact on reply sentiment
depth
Minimum sample size: 100 sends per variant before drawing conclusions.
Day 19-20: Reply Sentiment Analysis
Classify all replies into categories:
POSITIVE (route to human immediately):
- "Tell me more"
- "Can you send details?"
- "Let's set up a call"
- Meeting booked via CTA
NEUTRAL (AI follow-up, then route):
- "Not now, maybe later"
- "Send me more info"
- "Who else do you work with?"
NEGATIVE (remove from sequence):
- "Not interested"
- "Remove me"
- "Wrong person"
OBJECTION (AI handles with playbook):
- "We already have a solution"
- "No budget right now"
- "Need to talk to my team"
Day 21: ICP Scoring Adjustment
Review first 3 weeks of data and adjust:
Recalibrate scoring weights based on actual conversion data, not assumptions.
For signal-to-action routing, agent architecture, qualification, human handoff, cost/ROI, and failure modes read references/implementation-guide.md when designing or debugging an AI SDR deployment.
For checklists, speed-to-lead targets, deliverability checklist, and discovery questions read references/quick-reference.md.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take tech-leads-club/ai-sdr from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.