/prompt-test

name: prompt-test
description: "A/B test content variations by quality score: create a named test, log each variant (scored via eval-runner.py on hallucination, content quality, and readability), and get a winner declaration with margin of victory, confidence level, per-dimension trade-offs, and auto-reject flags. Produces a decision-ready recommendation plus reusable insights about which approach wins for this brand. Triggers on \"/digital-marketing-pro:prompt-test\", \"which headline style works better\", \"A/B test these subject lines\", \"compare two versions of this copy\", \"show the results of my content test\". Reads the brand profile and guidelines for evaluation context; compares eval scores, not live audience performance — pair with /digital-marketing-pro:ab-test-plan for real-traffic experiments."

/digital-marketing-pro:prompt-test

Purpose

A/B test content output variations by comparing quality scores across different prompt approaches, headline styles, CTA phrasing, or complete content strategy variations. Create named tests, log variants with their evaluation scores, and determine which approach produces the best quality results.

This command brings experimental rigor to content creation. Instead of guessing which headline style, subject line approach, or content structure works best, you run a structured test: define the experiment, log each variant with its quality scores, and get a statistically grounded recommendation on which approach to adopt. Useful for testing subject line styles (curiosity vs. benefit-driven), headline approaches (question vs. statement vs. how-to), CTA phrasing (urgency vs. value vs. social proof), tone variations (formal vs. conversational), or complete content strategy A/B comparisons.

Input Required

The user must provide (or will be prompted for):

• **Action**: What to do — `create` (set up a new test), `log` (add a variant to an existing test), `results` (get comparison and winner), or `list` (show all tests)
• **Test name**: A descriptive name for the experiment (e.g., "Q1 email subject line style", "homepage headline approach") — required for `create`, `log`, and `results`
• **Variant label**: Identifier for this variant (e.g., "A", "B", "C", "control", "curiosity-driven", "benefit-led") — required for `log`
• **Content for the variant**: The actual content to evaluate — text inline, file path, or pasted content block — required for `log`
• **Variant description**: Brief explanation of the approach or strategy this variant represents (e.g., "Uses curiosity gap with no product mention", "Leads with quantified benefit") — required for `log`
• **Content type**: The type of content being tested (email subject line, headline, ad copy, CTA, full article, etc.) — optional, applied during evaluation for dimension weighting
• **Evidence file**: Supporting data or research that informs the test hypothesis — optional, passed to evaluation for context

Process

1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. Check for guidelines at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files (voice-and-tone rules, messaging hierarchy, channel style guides). Check for custom templates at `~/.claude-marketing/brands/{slug}/templates/`. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults. 2. **For `create` action**: Set up a new test by running `python "${CLAUDE_PLUGIN_ROOT}/scripts/prompt-ab-tester.py" --brand {slug} --action create-test --test-name "{name}"`. This initializes the test record with metadata (creation date, brand, content type) and prepares it for variant logging. Confirm the test was created and remind the user to log variants with `/digital-marketing-pro:prompt-test` using the `log` action. 3. **For `log` action**: First evaluate the variant content for quality by running `python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py" --brand {slug} --action run-quick --text "{content}" --content-type "{type}"` (use `--file "{path}"` instead of `--text` if the variant is a file). This produces per-dimension scores for the three quick dimensions (hallucination, content_quality, readability) and a composite score. Note: `run-quick` ignores evidence files — if an evidence file was provided and claim verification matters for this test, use `--action run-full --evidence "{evidence_path}"` instead. Then log the variant with its scores by running `python "${CLAUDE_PLUGIN_ROOT}/scripts/prompt-ab-tester.py" --brand {slug} --action log-variant --test-name "{name}" --variant "{label}" --data '{"description":"{description}","scores":{scores_json}}'`. Present the individual variant scores to the user immediately so they can see how this variant performed before logging additional variants. 4. **For `results` action**: Pull the full comparison by running `python "${CLAUDE_PLUGIN_ROOT}/scripts/prompt-ab-tester.py" --brand {slug} --action get-results --test-name "{name}"`. Analyze the results:

• Identify the winning variant by highest composite score
• Calculate the margin of victory (percentage difference between winner and runner-up)
• Assess statistical significance — if variants are within 5% of each other, flag as "too close to call" and recommend additional testing or tiebreaker criteria
• Break down per-dimension performance to show where each variant excels or falls short (e.g., Variant A wins on brand_voice but Variant B wins on readability)
• Identify the specific strengths of the winning approach that can be applied to future content
• Flag any variants that fell below the configured auto-reject threshol