--- name: evolution-foundation/mkt-autoresearch source: https://app.decimal.ai/s/evolution-foundation-mkt-autoresearch@1/SKILL.md source_sha256: 479152e31956 --- # 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. ```bash 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) After all elements have winners: 1. Assemble the top winner from each element into a complete unit 2. Generate 5 holistic variants that naturally combine the winning elements 3. Score the complete units (not just individual parts) 4. Pick the winner with the highest holistic score ### Step 5: Write Output Files ```bash # Create output directory mkdir -p data # Write optimized content # Write experiments JSON # Write optimization report ``` **Experiments JSON structure:** ```json { "run_id": "autoresearch-{name}-{timestamp}", "content_type": "landing_page", "source_file": "path/to/original", "min_score_threshold": 80, "rounds": [ { "round": 1, "element": "hero_headline", "variants": [ { "id": 1, "text": "...", "scores": { "cmo": 72, "skeptical_founder": 68, "cro": 75, "copywriter": 70, "founder": 65 }, "avg_score": 70 } ], "top_3": [1, 4, 7], "winner_score": 82 } ], "final_winner": { "hero_headline": "...", "subheadline": "...", "cta": "...", "holistic_score": 87 } } ``` ### Step 6: Report Back Summarize results to user: - Final winning score - Biggest score jump (which element improved most) - Top 2 runner-up alternatives (in case winner doesn't feel right) - Path to all 3 output files - Clear next step --- ## User Options | Option | Default | Description | |--------|---------|-------------| | `elements` | all | Which elements to optimize | | `variants_per_round` | 10 | How many variants to generate per round | | `min_score` | 80 | Stop when this score is hit | | `rounds` | 3 | Max rounds before stopping | | `auto_apply` | false | Whether to overwrite the source file with winners | | `content_type` | auto-detect | Force a content type if auto-detect is wrong | --- ## Quality Gates - **< 70:** Don't ship. Something fundamental is broken. - **70-79:** Marginal. One more round targeting the lowest-scoring dimension. - **80-84:** Good. Shippable. Validate with real traffic. - **85-89:** Strong. Ship with confidence. - **90+:** Rare. Ship immediately. --- ## Anti-Patterns to Avoid - **Never call the API once per variant.** Always batch. A 10-variant round = 1 call. - **Don't over-optimize for one dimension.** If you're hitting 95 on clarity but 45 on trust, the overall score is misleading. - **Don't run more than 5 rounds.** If you're not hitting 80 after 3 rounds, the problem is strategic (wrong positioning), not tactical (wrong words). - **Don't cross-breed until each element has its own winner.** Premature cross-breeding creates incoherent combinations.