outbound-optimizer

Installation
SKILL.md

Outbound Optimizer

Diagnose current outbound performance, identify root causes, apply the outbound-sequences skill for improvements, validate output, and recommend A/B tests.

Step 1: Gather Metrics

Collect current performance data:

  1. Metrics: Open rate? Reply rate? Meeting book rate?
  2. Volume: How many sequences/week? Total contacts?
  3. Target: Who are you reaching? (Title, industry, company size)
  4. Channels: Email only? Multi-channel? (Email, phone, LinkedIn)
  5. Sample: Best-performing email or script
  6. Challenge: Low opens? Low replies? No-shows? Wrong prospects?

Step 2: Diagnose Against Benchmarks

Metric Benchmark If Below → Problem
Open rate 40-60% Subject line / deliverability
Reply rate 5-15% Copy / relevance
Positive reply rate 2-5% Targeting / offer
Meeting book rate 1-3% CTA / friction
Show rate 70-80% Confirmation / timing
Connect rate (calls) >5% Timing / list quality

Step 3: Identify Root Cause

Symptom Likely Cause Investigation
Opens low, replies low Subject line problem Test new subject patterns
Opens high, replies low Copy doesn't resonate Review first line, value prop
Replies high, meetings low CTA too aggressive Lower friction ask
Meetings high, shows low Weak confirmation Add reminder sequence
All metrics low Wrong ICP Review targeting criteria

Step 4: Apply Outbound Sequences Skill

Apply outbound-sequences with structured context:

context:
  current_metrics: [open rate, reply rate, meeting rate]
  diagnosed_problem: [subject lines | copy | targeting | CTA]
  target_persona: [title, industry, company size]
  channels: [email | multi-channel]
  value_prop: [what you solve]
  social_proof: [notable customers, results]
  current_best_performer: [paste sample]
  tool: [Apollo, Outreach, Lemlist, etc.]
request:
  - New sequence addressing diagnosed problem
  - Include A/B test variants for problem area

Step 5: Validate Output

Quality Checklist

  • Subject lines are personalized and specific (not generic)
  • First line shows research (references something specific)
  • Emails are under 100 words
  • CTA is clear and low-friction
  • Sequence has 5-7 touches across channels
  • Follow-up emails add new value (not just "checking in")
  • Response handling covers all scenarios
  • Templates are ready to load into tool

Red Flags

Issue Problem Fix
Generic opener "Hope you're well" Specific observation
Long emails Won't be read Under 75 words
Multiple CTAs Confusion Single clear ask
No personalization vars Can't scale Add {{variables}}
Same value in each email No reason to reply New angle per email

Step 6: Recommend A/B Tests

Based on diagnosed problem, recommend:

Problem Area Test Measure
Subject lines 2-3 variations Open rate
First line Personalized vs. direct Reply rate
CTA Meeting vs. question Response rate
Send time Morning vs. afternoon Open rate
Sequence length 5 vs. 7 touches Total reply rate

Metrics Tracking Template

Weekly Review:
Sequences Sent: [X]
Open Rate: [X%] (benchmark: 50%)
Reply Rate: [X%] (benchmark: 10%)
Positive Rate: [X%] (benchmark: 3%)
Meetings Booked: [X]
Pipeline Generated: $[X]

Top Performer: [sequence/template name]
Underperformer: [sequence/template name]

This Week's Test: [what we're testing]
Result: [outcome]

Next Week Actions:
1. [specific optimization]
2. [specific optimization]

Handling Edge Cases

Situation Action
No metrics data Ask for approximate open rate (estimate is fine)
No sample copy Ask for current best email
Multiple problems Focus on earliest funnel stage first (opens before replies)
Output too generic Re-apply skill with more specific persona details
Tool constraints Adjust templates for tool limitations

Deliverables

  1. Diagnosis of current performance
  2. Root cause analysis
  3. Optimized sequence from outbound-sequences skill
  4. A/B testing plan
  5. Metrics tracking template
  6. Weekly review cadence
Weekly Installs
8
GitHub Stars
37
First Seen
Mar 24, 2026