Autoresearch

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:

Expert Panel (5 Personas)

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

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