/sequence-performance

name: sequence-performance
version: 1.0.0
description: >
  Email campaign/sequence performance review composite. Pulls campaign data
  (sends, opens, replies, bounces), reads actual email copy and subject lines,
  analyzes reply content (objections, positive interest, questions), and produces
  a diagnostic report covering quantitative metrics, copy quality, lead quality,
  and actionable recommendations. Tool-agnostic — works with Smartlead (MCP),
  Instantly, Outreach, Lemlist, Apollo, or CSV data.
tags: [research]

Sequence Performance

Goes beyond vanity metrics. Most campaign reports tell you open rate and reply rate. This skill reads the actual emails you sent, reads every reply you received, classifies the responses, evaluates your copy, evaluates your lead quality, and tells you specifically what's working, what's not, and what to do about it.

**Three layers of analysis:** 1. **Quantitative:** The numbers — sends, opens, replies, bounces, conversions, by touch and by variant 2. **Qualitative (Copy):** Are the subject lines, email bodies, CTAs, and personalization actually good? 3. **Qualitative (Replies):** What are people actually saying? What objections keep coming up?

When to Use

Use this skill when:

• User says "how's my campaign doing", "sequence performance", "campaign review", "email analytics"
• User says "analyze my outreach", "why isn't my campaign working", "review my email results"
• A campaign has been running for 7+ days and has meaningful data

Phase 0: Intake

Outreach Tool

1. What outreach tool do you use? (Smartlead / Instantly / Outreach.io / Lemlist / Apollo / Other) 2. How do we access campaign data? (MCP tools / API / CSV export / paste metrics)

Campaign Selection

3. Which campaign? (name or ID) 4. Date range? (or "all data")

Your Company Context (for copy evaluation)

5. What does your company do? (one-liner) 6. Who is your ICP? (titles, industries, company size) 7. What problem do you solve? 8. What's your CTA goal? (book meeting, get reply, drive to page)

Benchmark Context

9. Is this cold outreach or warm/nurture? 10. What segment are you selling to? (SMB, mid-market, enterprise)

Step 1: Pull Campaign Data

Pull three categories of data from the user's outreach tool:

A) Campaign Metrics

| Data Point | What We Need | |-----------|-------------| | Total emails sent | By touch (Touch 1, Touch 2, Touch 3, etc.) | | Total unique recipients | Deduplicated count | | Opens | By touch, unique opens vs. total opens | | Replies | By touch, total reply count | | Bounces | Hard bounces + soft bounces | | Unsubscribes | Count | | Clicks | If link tracking is on | | Positive replies | If categorized in the tool | | Meetings booked | If tracked |

**How to pull by tool:**

| Tool | Method | |------|--------| | **Smartlead** (MCP) | `mcp__smartlead__get_campaign_stats`, `mcp__smartlead__get_campaign_sequence_analytics`, `mcp__smartlead__get_campaign_variant_statistics` | | **Instantly / Outreach / Lemlist / Apollo** | Ask user for CSV export or paste metrics | | **Other** | User provides CSV with columns: email, status, opened, replied, bounced |

B) Email Copy (Sequence Content)

Pull the actual templates for every touch:

| Tool | Method | |------|--------| | **Smartlead** (MCP) | `mcp__smartlead__get_campaign_sequences` | | **Others** | User pastes the copy or provides CSV export |

C) Reply Content

Pull the actual text of every reply:

| Tool | Method | |------|--------| | **Smartlead** (MCP) | `mcp__smartlead__get_campaign_leads_history`, `mcp__smartlead__fetch_master_inbox_replies` | | **Others** | User provides reply dump or CSV export |

Human Checkpoint

Campaign: [name]
Status: [active/paused/completed]
Sent: X emails to Y recipients
Replies: Z (full text pulled for analysis)
Touches: N touches, M variants

Data looks complete? (Y/n)

Step 2: Quantitative Analysis

Benchmarks

| Metric | Cold (SMB) | Cold (Mid-Market) | Cold (Enterprise) | Warm/Nurture | |--------|-----------|-------------------|-------------------|-------------| | Open rate | 40-60% | 30-50% | 25-40% | 50-70% | | Reply rate | 3-8% | 2-5% | 1-3% | 10-20% | | Positive reply rate | 1-3% | 0.5-2% | 0.3-1% | 5-10% | | Bounce rate | <3% | <3% | <2% | <1% | | Unsubscribe rate | <1% | <1% | <0.5% | <0.5% |

Calculate

**Overall metrics:** open rate, reply rate, positive reply rate, bounce rate, unsubscribe rate, deliverability rate. Compare each to the benchmark.

**Per-touch breakdown:**

• Touch-level open/reply rates
• Marginal reply rate (replies from THIS touch / people who received this touch but hadn't replied yet)
• Touch contribution (what % of total replies came from each touch)

**Variant analysis (if A/B testing):**

• Open rate and reply rate per variant
• Statistical confidence: <50 sends = "insufficient data", 50-100 = "directional", 100-250 = "likely winner", 250+ = "statistically significant"
• Winner recommendation: scale, keep testing, or kill

Step 3: Reply Analysis

Read every reply, classify it, and extract patterns.

Reply Categories

| Category | Definition | |----------|-----------| | **Positive interest** | Wants to learn more, open to a conversation | | **Meeting request** | Explicitly asks to meet or provides availability | | **Warm / Curious** | Interested but non-committal, asks questions | | **Objection — Timing** | Not now, but potentially later | | **Objection — Budget** | Can't afford or not a priority | | **Objection — Competitor** | Already using a competing solution | | **Objection — Relevance** | Doesn't see the fit | | **Objection — Authority** | Not the right person | | **Not interested** | Flat no | | **Auto-reply / OOO** | Automated response | | **Referral** | Redirects to someone else | | **Question** | Asks about product/offering |

Objection Patterns

• Which objection appears most? (reveals systemic issues)
• Do objections cluster at Touch 1 (bad targeting) vs. Touch 3 (