/subject-line-lab

name: subject-line-lab
description: Mine your actual subject-line history from Klaviyo / Mailchimp / Rule / Get a Newsletter, find the patterns that work for YOUR list, and generate tuned candidates
version: "1.0.0"
author: Cogny AI
requires: cogny-mcp
platforms: [klaviyo, mailchimp, rule, get-a-newsletter]
user-invocable: true
argument-hint: "[campaign topic]"
allowed-tools:
  # Cogny Cloud (aggregated) namespace
  - mcp__cogny__klaviyo__*
  - mcp__cogny__mailchimp__*
  - mcp__cogny__rule__*
  - mcp__cogny__get_a_newsletter__*
  - mcp__cogny__create_finding
  - mcp__cogny__write_context_node
  - mcp__cogny__read_context_node
  # Cogny Solo / Lite (per-ESP direct) namespace
  - mcp__klaviyo__*
  - mcp__mailchimp__*
  - mcp__rule__*
  - mcp__get_a_newsletter__*
  - Bash
  - Read
  - Write

Subject Line Lab

Stop A/B testing blind. This skill reads **your actual last 100+ sends**, finds the subject-line patterns that correlate with open rate on *your* list, and generates 20 tuned candidates for your next campaign.

**Requires:** Cogny MCP + a connected ESP (Klaviyo, Mailchimp, Rule, or Get a Newsletter). [Sign up](https://cogny.com)

Usage

`/subject-line-lab` — mine patterns and generate generic candidates `/subject-line-lab "Black Friday doors open"` — mine patterns and generate candidates for a specific campaign topic

Prerequisites Check

Detect which ESP is connected. Check **both** namespaces (Cloud-aggregated via `mcp__cogny__<svc>__*` and Solo/Lite per-ESP via `mcp__<svc>__*`):

• Klaviyo → `mcp__cogny__klaviyo__*` or `mcp__klaviyo__*`
• Mailchimp → `mcp__cogny__mailchimp__*` or `mcp__mailchimp__*`
• Rule → `mcp__cogny__rule__*` or `mcp__rule__*`
• Get a Newsletter → `mcp__cogny__get_a_newsletter__*` or `mcp__get_a_newsletter__*`

If none are connected:

This skill requires a connected ESP via Cogny MCP.
Connect Klaviyo, Mailchimp, Rule, or Get a Newsletter at https://cogny.com

If multiple are connected, prompt the user which one to analyze (or analyze all, labeled).

ESP tool adapter

Each ESP exposes subject-line history differently. Use the right tool per connected service:

| ESP | Send history tool | Notes | |-----|-------------------|-------| | Klaviyo | `list_campaigns` (channel=email) then `get_campaign` | Tools are bare-named. Use `list_events` for deeper open/click metrics. | | Mailchimp | `tool_list_reports` then `tool_get_report` | Reports are the source of truth for opens/clicks per send. | | Rule | `tool_list_campaigns` then `tool_get_campaign_statistics` | Statistics tool returns opens/clicks/bounces. | | Get a Newsletter | `tool_list_sent` then `tool_get_sent` and `tool_get_report` | "Campaigns" don't exist as an object — iterate `list_sent` for subject+date, `get_report` per send for metrics. |

Steps

1. Pull send history

For the connected ESP, pull the last **100 campaign sends** (or as many as available). For each, capture:

• Subject line
• Preheader (if available)
• Send date + time (recipient local time if available)
• Recipient count
• Unique opens → open rate
• Unique clicks → CTR
• Segment / list name
• Campaign type (promo, newsletter, announcement, transactional-adjacent)

Skip transactional sends and automated flow emails — they distort the signal. Focus on broadcast campaigns.

2. Compute baseline

• **Median open rate** across the sample
• **Mean open rate** (flag skew if mean ≠ median by >20%)
• **Sample size warning** — if <30 sends, warn the user: "small sample, patterns may be noise"

3. Extract subject-line features

For each subject line, tag these features (use keyword/regex heuristics — no ML needed):

**Structural:**

• Length (chars): <30 / 30-50 / 50-70 / >70
• Word count
• Title Case / Sentence case / ALL CAPS / lowercase
• Ends in `?` / `!` / `.` / no punctuation

**Content:**

• Contains emoji (count, type)
• Contains number / digit
• Contains `%` or `$` or currency (promo signal)
• Contains personalization token (`{{ first_name }}`, `[Firstname]`)
• Question vs statement vs command
• First-person ("I", "we", "my") vs second-person ("you", "your")
• Urgency words: "today", "tonight", "last chance", "ends", "hours", "don't miss"
• FOMO / scarcity: "only", "limited", "few left", "selling fast"
• Curiosity gap: starts with "why", "how", "the truth about", "what I learned"
• Benefit-led: leads with a noun describing an outcome
• Social proof: "customers", "members", a number of people

**Temporal:**

• Day of week sent
• Hour of day (bucketed: early morning / morning / midday / afternoon / evening / late)

4. Compute lift per feature

For each feature, compute:

• **Count in sample**
• **Mean open rate when feature is present**
• **Mean open rate when feature is absent**
• **Lift** (% difference vs non-feature baseline)
• **Significance note** — flag as "strong" if n≥10 in both groups and lift is ≥10% relative; "weak" otherwise

Rank features by absolute lift.

5. Identify winning patterns

Output the top 5 **positive** patterns and top 3 **negative** patterns with specific numbers:

Patterns that worked for your list (last 100 sends):

🟢 WINNERS
1. Subject lines ending in "?" → 34% open rate vs 21% baseline (+62%, n=14 strong)
2. Subject lines with numbers → 29% open rate vs 21% (+38%, n=22 strong)
3. Length 30-50 chars → 27% open rate vs 19% (+42%, n=31 strong)
4. First-person voice ("I", "we") → 26% open rate vs 22% (+18%, n=18 strong)
5. Sent Tue 09:00-11:00 → 28% open rate vs 22% (+27%, n=12 weak)

🔴 DRAGS
1. ALL CAPS words → 14% open rate vs 23% (-39%, n=8 weak)
2. Emoji at start → 18% open rate vs 24% (-25%, n=11 strong)
3. Urgency words ("last chance", "today only") → 16% open rate vs 22% (-27%, n=9 weak)

Your best historical subject: "<actual subject>" at <open rate>% (<date>)
Your worst: "<actual subject>" at <open rate>% (<date>)

6. Generate tuned candidates

If a campaign topic was provided, generate **20 subject line candidates** that follow the winning patterns and avoi