Expert Panel — Iterative Multi-Expert Scoring

Reusable scoring engine. Other mktg skills (copy-optimize, cro-audit, pr) reference this for rigorous multi-expert scoring instead of maintaining parallel rubrics. The pre-built panels below (PR, Landing Page, Social, Strategy) are the shared scoring mechanic those skills call into.

Takes any content and scores it through a panel of simulated domain experts. Loops until the score hits 90+ or 3 rounds are exhausted. The humanizer expert is always included at 1.5x weight.

Input

Accept content in any of these forms:

• Pasted directly as the argument
• A file path -> read the file
• A URL -> fetch the content
• Content type hint (optional): landing-page, email, social-post, article, pr, strategy, ad-copy

If no content type is specified, infer from the content itself.

Step 1: Intake

Identify:

• Content type: what are we scoring?
• Goal: what should this content achieve?
• Audience: who reads this?
• Context: any constraints (brand voice, platform limits, regulations)?

State these in 4 lines max before proceeding.

Step 2: Auto-Assemble the Panel

Select 7-10 experts based on content type and domain. Always include:

1. Humanizer (mandatory, weight 1.5x) -- Detects AI writing patterns. Scores how human the text sounds. Uses the 24-pattern detection framework from writing-unslop.
2. Brand Voice Match (mandatory) -- Does this sound like the brand/person, or like generic AI output?

Then add 5-8 domain experts from the pre-built panels below.

Landing Page

• Conversion Rate Optimizer (CTA, friction, clarity)
• Headline Specialist (hook, specificity, scroll-stopping)
• Social Proof Analyst (trust signals, testimonials, credibility)
• UX/Mobile Expert (readability, hierarchy, mobile rendering)
• Competitive Positioning Expert (differentiation, unique value)

Email (onboarding, newsletter, campaign)

• Subject Line Specialist (open rate, curiosity, relevance)
• Email Deliverability Expert (spam signals, formatting, length)
• Copywriter (flow, tone, CTA placement)
• Audience Empathy Expert (relevance to reader, pain points)
• Retention Strategist (nurture sequence logic, timing)

Social Post (X, LinkedIn)

• Platform Algorithm Expert (format, timing, engagement signals)
• Hook Specialist (first line, scroll-stopping power)
• Audience Growth Expert (shareability, quote-worthiness)
• Authenticity Analyst (does it sound like a person or a brand?)

Article / Blog

• Editor (structure, flow, argument strength)
• SEO Analyst (keyword relevance, search intent match)
• Reader Engagement Expert (hook, readability, value density)
• Subject Matter Expert (accuracy, depth, credibility)

PR / Communications

• Journalist Perspective (would I cover this? is there news value?)
• Headline Analyst (click-worthiness, specificity)
• Data Density Expert (facts vs. fluff ratio)
• Crisis/Risk Reviewer (could this backfire?)

Strategy / Business

• Data Foundation Expert (are claims data-backed?)
• ROI Analyst (is the business case clear?)
• Risk Assessor (what could go wrong?)
• Actionability Expert (can someone execute this tomorrow?)

For domains not listed above, assemble a panel using this principle:

• 3-4 craft experts (how to make good content of this type)
• 2-3 domain experts (the specific market/audience/subject)
• Humanizer (always)
• Brand Voice Match (always)
• Cap at 10, merge overlapping roles

Step 3: Select Scoring Rubric

Choose the rubric based on content type. Each has 4 dimensions worth 0-25 points (total 0-100):

Content Quality (articles, social posts, newsletters)

Conversion Quality (landing pages, emails, ads, CTAs)

Strategic Quality (strategies, plans, briefs)

Evaluation Quality (reviews, audits, assessments)

Step 4: Score (Recursive Loop)

Round structure

For each round, produce:

1. Expert scores table:

2. Rubric scores: the 4-dimension breakdown

3. Weighted aggregate: average of all expert scores (humanizer counted at 1.5x weight)

4. Top 3 weaknesses: ranked, with specific fix suggestions

5. If score < 90: revise the content addressing the top 3 weaknesses, then run the next round

6. If score >= 90: finalize

Scoring rules

• Scores must be brutally honest. No padding to reach 90.
• First round scores are typically 55-75. That's normal.
• Each round should show measurable improvement on the identified weaknesses.
• Max 3 rounds. If still under 90 after round 3, ship with the best version and note remaining issues.
• The humanizer score gates everything: if humanizer < 80, the content does not ship regardless of aggregate score.

Quality gates

• < 70: Do not ship. Major rewrite needed.
• 70-79: Shippable with caveats. Note what's weak.
• 80-89: Good. Minor polish possible but not blocking.
• 90+: Ship with confidence.

Step 5: Output

---
**Content type**: [type]
**Panel**: [list of expert names]
**Rubric**: [which rubric used]
**Final score**: XX/100 (after N rounds)

### Rubric breakdown
| Dimension | Score |
|-----------|-------|
| ... | /25 |
| ... | /25 |
| ... | /25 |
| ... | /25 |

### Round history
[Round 1: XX/100 -> top issues -> Round 2: XX/100 -> ...]

### Expert verdicts (final round)
| Expert | Score | Verdict |
|--------|-------|---------|
| ... | ... | ... |

---

[The final scored/revised content]

---

### Remaining issues (if score < 90)
- [what's still weak and why]

Step 6: Feedback-to-Source

When scoring content produced by another skill (e.g., mktg-copy-optimize output, writing-xpost draft):

• Generate a 3-5 line improvement brief that the source skill can use
• Format: "To improve: [specific change]. Because: [expert rationale]."

Memory: Learned Patterns

After each scoring session, if the user approves or rejects the final output, note the pattern in ~/.claude/skills/mktg-expert-panel/patterns.md

Format:

## [Pattern Name]
- **Type:** approval | rejection
- **Content types:** [which types]
- **Rule:** [what to always/never do]
- **Date:** [YYYY-MM-DD]