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".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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".
description_zh
Karpathy 风格内容优化，50+ 变体 + 专家评分 + 进化迭代
description_en
Karpathy-style content optimization with 50+ variants, expert scoring, and evolution
version
1.0.0
homepage
https://github.com/ericosiu/ai-marketing-skills
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.
Post-launch real-traffic A/B testing — that requires real analytics, not simulated scoring.
When NOT to use this:
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):
claude-sonnet-4-5 (preferred — fast + smart)
claude-opus-4 (if highest quality needed)
Any claude-3.5+ model if the above aren't available
Step 4: Cross-Breed (Multi-Element)
After all elements have winners:
Assemble the top winner from each element into a complete unit
Generate 5 holistic variants that naturally combine the winning elements
Score the complete units (not just individual parts)
