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

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:

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 avoid the drags. If no topic was provided, generate 20 generic promo/newsletter candidates using your patterns.

Format:

Candidates for: "<topic>"
(tuned to: ends-in-?, has-number, 30-50 chars, first-person voice)

Preheader recommendation: 85-100 chars, don't duplicate subject, extend the hook.

1. "What <specific number> of our customers asked last week?" (52 chars)
   Preheader: "We pulled the top 5 questions and answered them in one place — you'll recognize #2."
2. ...
20. ...

──
A/B test plan:
- Primary variant: candidate #<N> (leans hardest into top pattern)
- Challenger: candidate #<M> (different angle to avoid pattern overfitting)
- Hold-out: your house-style baseline subject
Segment: X% / Y% / Z%

7. Persist patterns as context

Save the winning-pattern summary to Cogny's context tree for future sessions:

{
  "path": "insights/email/subject-line-patterns",
  "body": "<the top 5 winners + top 3 drags, with numbers and date range>"
}

8. Create a finding

If the analysis surfaces a clear actionable insight (e.g., "50% of recent sends use emoji-at-start which is actively hurting opens"), create a finding:

{
  "title": "Emoji-at-start subjects are tanking opens (-25% vs baseline)",
  "body": "11 of last 100 sends started with emoji. Avg open 18% vs 24% non-emoji baseline. Recommend: move emoji to mid-subject or drop entirely on next 10 campaigns and re-measure.",
  "action_type": "subject_line_optimization",
  "expected_outcome": "Lift average open rate by 2-4 pp",
  "estimated_impact_usd": 0,
  "priority": "medium"
}

Notes

• Lift numbers are correlational, not causal — always surface sample size and flag weak signals.
• If the user's list is <5,000 subscribers, opens are noisy. Recommend 2-week lookback minimum and warn.
• Apple MPP inflates open rate for iOS Mail users. If open rate looks uniformly high (>40%+ across everything), the signal is degraded — note this and lean on CTR as a secondary metric.