Foxwell Meta Creative Framework

Based on Andrew Foxwell's public method. Not affiliated with or endorsed by Andrew Foxwell.

When to use

Use this skill to turn an account's economics, production capacity and performance history into a practical Meta creative testing system. It supports framework selection, creative briefs, Yapper scripts, winner evaluation and controlled scaling. The deliverable is a decision-ready plan with assumptions, trade-offs, budgets, review criteria and the next production batch.

Use it when a team cannot decide between separate testing ad sets and testing alongside existing winners, when creative production has outgrown the account structure, or when a successful angle needs more formats to support acquisition. It also helps diagnose why tests produce attractive small samples but fail to become scalable ads.

This is a synthesis of publicly accessible Foxwell Digital and Foxwell Founders material, including named community contributors. Read the source ledger before attributing a rule to Andrew personally. Keep source-backed guidance distinct from the package's suggested operational safeguards. The method is conditional: the objective is repeatedly discovering and scaling useful creative within business constraints, rather than defending one permanent account structure.

Procedure

1. Establish the assignment and baseline. Read the intake guidance. Capture daily spend, test allowance, target acquisition economics, account maturity, weekly concepts and variants, conversion delay and management capacity. Identify missing inputs explicitly. If information is unavailable, produce a conditional plan with stated assumptions rather than inventing account facts.
2. Check the economics before increasing traffic. Use the economics rules to separate affordable acquisition cost from platform ROAS. Identify contribution margin, AOV and first-time buyer performance. A proposed budget increase needs an economic reason and evidence that the business can support it.
3. Choose a framework by fit. Apply the decision table to compare ABO, CBO with minimum spends, direct competition and manual bid testing. Recommend one primary approach, explain its trade-off, and name the condition that would make another approach preferable. Use a hybrid only when the two lanes have distinct jobs.
4. Make the test affordable. Follow budget and exposure planning. Set simultaneous batches, daily allocation, review window and maximum acceptable loss. Separate batch exposure from individual ad exposure. Reduce the batch count when the budget cannot support interpretable learning.
5. Write an explicit creative hypothesis. Follow creative research and diversification. Distinguish an exploratory new angle from a controlled iteration. Preserve the effective customer psychology while changing the visual entry point. Explain what each test can teach and what it cannot establish.
6. Build a diverse, credible production batch. Use the format guidance to select founder, employee, testimonial, demonstration, static, partnership or other appropriate formats. Apply the scale-potential quality gate. Do not count cosmetic variations as meaningful diversity or invent customer experiences to fill a brief.
7. Brief Yappers when appropriate. Use the Yapper specification for a natural single-take delivery: unconventional hook, specific lived problem, product bridge and gentle invitation. Include speaker context, substantiated proof, hook variants and a recording note. Preserve conversational delivery without manufacturing testimonials.
8. Define evaluation before launch. Use winner and iteration rules. Record economics, delivery, purchases, attribution and diagnostic video metrics. Distinguish insufficient exposure, a relative improvement and an economically qualified winner. Plan monitoring for sudden allocation to new ads in large campaigns.
9. Specify the scaling path and fallback. Follow the scaling rules. Choose scale in place, graduate, or both, and explain how results will be checked at the destination. Watch concentration and acquisition performance. A winner in testing does not automatically retain delivery elsewhere.
10. Produce the deliverable. Fill the output template, keeping sourced guidance separate from local assumptions. Use the worked examples to calibrate specificity and budget arithmetic. Include owners, review dates, decision triggers and a learning log so the next cycle can use the findings.
11. Review and revise the system. Run the pass/fail checklist. Repair unsupported attribution, unfunded tests and unclear decisions. If discovery or scaling stalls, propose a bounded comparison of testing approaches. Treat changes to live campaigns or paid production as separate actions requiring the user's applicable authorization.

Files in this skill

• SLUG.txt: Canonical package identifier.
• SKILL.md: Activation guidance and the procedure.
• references/method.md: Principles, selection rules, formats, evaluation and edge cases.
• references/sources.md: Five fetched public sources, contributors and evidence boundaries.
• examples/worked-examples.md: Two invented illustrations with weak drafts and improved decisions.
• templates/output-template.md: Fill-in deliverable with field guidance.
• checklists/review-checklist.md: Pass/fail gates, common failures and repairs.