AI SDR Skill workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages packages/skills-catalog/skills/(gtm)/ai-sdr from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses EXTERNAL_SOURCE.json plus ORIGIN.md as the provenance anchor for review.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, Section 1: AI SDR Landscape (2025-2026), Section 2: The 4-Week AI SDR Deployment Program.
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | EXTERNAL_SOURCE.json | Confirms repository, branch, commit, and imported path before touching the copied workflow |
| Provenance review | ORIGIN.md | Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | references/implementation-guide.md | Starts with the smallest copied file that materially changes execution |
| Supporting context | references/quick-reference.md | Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills | Helps the operator switch to a stronger native skill when the task drifts |
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Before giving AI SDR advice, establish:
If any of these are unclear, ask before proceeding. Bad inputs produce bad AI SDR outputs.
Use @ai-sdr to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Review @ai-sdr against EXTERNAL_SOURCE.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Use @ai-sdr for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Review @ai-sdr using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/ai-sdr, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open EXTERNAL_SOURCE.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
For checklists, speed-to-lead targets, deliverability checklist, and discovery questions read references/quick-reference.md.
@accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.@ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.@ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.@ai-seo - Use when the work is better handled by that native specialization after this imported skill establishes context.Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
|---|---|---|
references | copied reference notes, guides, or background material from upstream | references/implementation-guide.md |
examples | worked examples or reusable prompts copied from upstream | examples/n/a |
scripts | upstream helper scripts that change execution or validation | scripts/n/a |
agents | routing or delegation notes that are genuinely part of the imported package | agents/n/a |
assets | supporting assets or schemas copied from the source package | assets/n/a |
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.