/mkt-autoresearch

name: mkt-autoresearch
description: Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

Autoresearch Skill

Karpathy-style optimization loops for any conversion-focused content. No traffic needed. Simulated expert panel. Minutes, not weeks.

**When to use this:** Pre-launch content optimization. Generate 50+ variants, score with 5 simulated experts, evolve winners, output the best version + full experiment log.

**When NOT to use this:** Post-launch real-traffic A/B testing — that requires real analytics, not simulated scoring.

> **The sequence:** Run autoresearch FIRST to hit 85+ simulated score. Then deploy. Then validate with real traffic.

---

What You'll Produce

Every run outputs 3 files:

| File | Purpose | |------|---------| | `{name}-optimized.{ext}` | The winning optimized content | | `data/{name}-experiments.json` | Full experiment log — all variants + all scores | | `data/{name}-optimization-report.md` | Human-readable summary with winner rationale |

---

Expert Panel (5 Personas)

Score every variant against all 5. Batch all variants into a **single API call** per round.

| # | Persona | Scoring Lens | |---|---------|-------------| | 1 | **CMO at a mid-market B2B company (50M+ revenue)** | "Would this make me stop and engage?" | | 2 | **Skeptical founder** | "Do I believe this? Would I trust this company?" | | 3 | **Conversion rate optimizer** | "Is this clear, specific, and action-driving?" | | 4 | **Senior copywriter** | "Is this compelling, differentiated, and well-crafted?" | | 5 | **Your CEO/founder** | "Direct, ROI-obsessed, no BS. Would I put this on my site?" |

> **Customization:** Replace persona #5 with your own CEO/founder voice. Define their priorities and communication style in a `references/founder-voice.md` file.

Each judge scores 0–100. **Final score = average across all 5 judges.**

---

Round Structure (Per Content Element)

Round 1:
  → Generate 10 variants of the element
  → Batch-score all 10 with the 5-expert panel (1 API call)
  → Rank by average score
  → Keep top 3

Round 2 (Evolution):
  → Analyze what the top 3 did right
  → Generate 10 new variants that push those winning patterns further
  → Batch-score all 10 (1 API call)
  → Keep top 3

Round 3 (If score < threshold):
  → Identify weakest scoring dimension
  → Generate 10 variants optimized for that dimension
  → Batch-score → keep top 1

Multi-element cross-breeding:
  → Take top 1 winner from each element
  → Generate 5 combinations that mix winning elements
  → Score holistically as complete units
  → Output the single best combination

**Stop condition:** Top variant hits minimum score threshold (default: 80) OR 3 rounds complete.

---

Content Types & Score Dimensions

Landing Pages

**Elements to optimize:** Hero headline, subheadline, CTA text, problem section, social proof

**Score dimensions:**

• `first_impression` — Does it grab immediately?
• `clarity` — Is the offer instantly understood?
• `trust` — Does it feel credible?
• `urgency` — Is there a reason to act now?
• `would_convert` — Would the judge actually click?

Email Sequences

**Elements to optimize:** Subject line, opening line, body copy, CTA, PS line

**Score dimensions:**

• `would_open` — Subject line pass rate
• `would_read` — Does the opening hook?
• `would_click` — Is the CTA compelling?
• `would_reply` — Does it feel personal enough to respond to?
• `spam_risk` — Does it feel spammy? (lower = better; invert for final score)

Ad Copy

**Elements to optimize:** Headline, description, CTA

**Score dimensions:**

• `scroll_stopping` — Does it interrupt the scroll?
• `clarity` — Is the value prop clear in 3 seconds?
• `click_worthiness` — Does the judge want to click?
• `relevance` — Does it match likely audience intent?
• `differentiation` — Does it stand out from competitors?

Form Pages

**Elements to optimize:** Headline, subtext, value prop bullets, button text, field order, thank-you copy

**Score dimensions:**

• `first_impression` — Does it feel worth filling out?
• `trust` — Do they believe their info is safe and the offer is real?
• `completion_likelihood` — Would the judge start filling it out?
• `lead_quality` — Would this attract serious prospects (not tire-kickers)?
• `would_fill_out` — Final gut check: would they submit?

---

Step-by-Step Execution Protocol

Step 1: Intake & Parse

Read the source content. Identify content type automatically or confirm with user:

• HTML file → landing page or form page
• Markdown / plain text → email or ad copy
• If ambiguous, ask: "Is this a landing page, email sequence, ad copy, or form page?"

Extract all optimizable elements. List them back to user:

Found 5 elements to optimize:
1. Hero headline: "We help B2B companies grow"
2. Subheadline: "Full-service digital marketing..."
3. CTA: "Get Started"
4. Problem statement: [excerpt]
5. Social proof: [excerpt]

Optimizing: all | Variants per round: 10 | Min score: 80

Step 2: Get API Key

Check for Anthropic API key: `$ANTHROPIC_API_KEY` environment variable.

export ANTHROPIC_API_KEY="your-api-key-here"

Step 3: Run Optimization Rounds

For each element, run the round structure above.

**Critical API efficiency rule:** ALWAYS batch all variants into a single prompt. Never call the API once per variant. A round with 10 variants = 1 API call.

Model preference (in order): 1. `claude-sonnet-4-5` (preferred — fast + smart) 2. `claude-opus-4` (if highest quality needed) 3. Any claude-3.5+ model if the above aren't available

Step 4: Cross-Breed (Multi-Element)