ai-sdr
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.
What this skill does
# AI SDR Skill
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 Starting
Before giving AI SDR advice, establish:
1. **Current sales motion** - Inbound-led, outbound-led, product-led, or hybrid?
2. **Team size** - Solo founder, small team (2-5), or scaled org (10+)?
3. **ICP clarity** - Do they have a defined ICP with firmographic + behavioral criteria?
4. **Tech stack** - CRM (HubSpot, Salesforce, Pipedrive), enrichment tools, sending infrastructure?
5. **Budget range** - Bootstrap ($500-1K/mo), growth ($1K-5K/mo), or scale ($5K+/mo)?
6. **Volume targets** - How many qualified meetings per month do they need?
7. **Data quality** - Clean CRM data vs. starting from scratch?
If any of these are unclear, ask before proceeding. Bad inputs produce bad AI SDR outputs.
---
## Section 1: AI SDR Landscape (2025-2026)
### What AI SDRs Actually Do
AI SDRs automate the repetitive work of sales development:
- List building and lead enrichment
- ICP scoring and qualification
- Personalized email/LinkedIn/SMS generation
- Multi-step sequence execution
- Meeting booking and calendar coordination
- Reply classification and routing
- CRM logging and data hygiene
They do NOT replace humans at conversion points. The handoff model matters more than the automation model.
### Platform Comparison Table
```
+---------------+------------+-----------------+---------------------------+------------------+
| 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 | |
+---------------+------------+-----------------+---------------------------+------------------+
```
### Platform Selection Decision Framework
```
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)
```
### Key Metrics Benchmarks
```
+-------------------------------+-------------+-------------+
| 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.
---
## Section 2: The 4-Week AI SDR Deployment Program
### Week 1: Foundation (Signal Setup + List Building)
**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**
- Pull 500 ICP-scored prospects into your enrichment workflow
- Score each prospect against your tier criteria
- Tag with relevant signals (funding, hiring, tech adoption, content engagement)
- Export Tier 1 prospects (target: 150-200) for Week 2 sequencing
### Week 2: Content (Sequence Creation + Personalization)
**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 mRelated in Design
contribute
IncludedLocal-only OSS contribution command center. Auto-refreshes the user's in-flight PR and issue state on invoke so conversations start with full context — no need to brief Claude on what's in flight. Helps the user find issues to contribute to on GitHub, builds per-repo dossiers of what each upstream expects (CLA, DCO, branch convention, AI policy, draft-first, review bots, issue templates), runs deterministic gates before any external action so AI-assisted contributions don't reach maintainers as slop. State is markdown-only: candidate files at ~/.contribute-system/candidates/, repo dossiers at ~/.contribute-system/research/, append-only event log at ~/.contribute-system/log.jsonl. No database, no cloud calls. Use when the user asks about their PRs / issues / contributions, wants to find new work to take on, claim an issue, build/refresh a repo's dossier, or draft a Design Issue or PR. Trigger with "/contribute", "what's my PR status", "find a contribution", "claim issue X", "draft a Design Issue for Y", "refresh dossier for Z".
architectural-analysis
IncludedUser-triggered deep architectural analysis of a codebase or scoped subtree across eight modes — information architecture, data flow, integration points, UI surfaces, interaction patterns, data model, control flow, and failure modes. This skill should be used when the user asks to "diagram this codebase," "map the architecture," "show the data flow," "give me an ERD," "trace control flow," "find the integration points," "verify the layout pattern," "audit the UX architecture," or any similar request whose primary deliverable is mermaid diagrams plus cited reports under docs/architecture/. Dispatches haiku/sonnet sub-agents in parallel for per-mode exploration, then verifies every citation mechanically before any node lands in a diagram. Not for one-off prose explanations of code (use code-explanation) or for high-level system design from scratch (use system-design).
mcp
IncludedModel Context Protocol (MCP) server development and tool management. Languages: Python, TypeScript. Capabilities: build MCP servers, integrate external APIs, discover/execute MCP tools, manage multi-server configs, design agent-centric tools. Actions: create, build, integrate, discover, execute, configure MCP servers/tools. Keywords: MCP, Model Context Protocol, MCP server, MCP tool, stdio transport, SSE transport, tool discovery, resource provider, prompt template, external API integration, Gemini CLI MCP, Claude MCP, agent tools, tool execution, server config. Use when: building MCP servers, integrating external APIs as MCP tools, discovering available MCP tools, executing MCP capabilities, configuring multi-server setups, designing tools for AI agents.
react-native-skia
IncludedDesign, build, debug, and optimise high-polish animated graphics in React Native or Expo using @shopify/react-native-skia, Reanimated, and Gesture Handler. Use when the user wants canvas-driven UI, shaders, paths, rich text, image filters, sprite fields, Skottie, video frames, snapshots, web CanvasKit setup, or performance tuning for custom motion-heavy elements such as loaders, hero art, cards, charts, progress indicators, particle systems, or gesture-driven surfaces. Also use when the user asks for fluid, glow, glass, blob, parallax, 60fps/120fps, or GPU-friendly animated effects in React Native, even if they do not explicitly say "Skia". Do not use for ordinary form/layout work with standard views.
plaid
IncludedProduct Led AI Development — guides founders from idea to launched product. Six capabilities: Idea (discover a product idea), Validate (pressure-test the idea against fatal flaws, problem reality, competition, and 2-week MVP feasibility), Plan (vision intake + document generation), Design (translate image references into a design.md spec), Launch (go-to-market strategy), and Build (roadmap execution). Use when someone says "PLAID", "plaid idea", "help me find an idea", "product idea", "idea from my business", "idea from my expertise", "plaid validate", "validate my idea", "pressure-test", "is this idea good", "find fatal flaws", "validate the problem", "plan a product", "define my vision", "generate a PRD", "product strategy", "plaid design", "design from image", "translate image to design", "create design.md", "extract design tokens", "plaid launch", "go-to-market", "launch plan", "GTM strategy", "launch playbook", "plaid build", "build the app", "start building", or "execute the roadmap".
nextjs-framer-motion-animations
IncludedAdds production-safe Motion for React or Framer Motion animations to Next.js apps, including reveal, hover and tap micro-interactions, whileInView, stagger, AnimatePresence, layout and layoutId transitions, reorder, scroll-linked UI, and lightweight route-content transitions. Use when the user asks to add, refactor, or debug Motion or Framer Motion in App Router or Pages Router codebases, especially around server/client boundaries, reduced motion, LazyMotion, bundle size, hydration, or route transitions. Avoid for GSAP-style timelines, WebGL or 3D scenes, heavy scroll storytelling, or CSS-only effects unless Motion is explicitly requested.