/mkt-quality-gate

name: mkt-quality-gate
description: >-
  Score, evaluate, and iteratively improve any content or strategy using an
  auto-assembled panel of domain experts. Handles copy, sequences, landing pages,
  strategy docs, titles, charts, recruiting evaluations, or anything else that
  needs a quality gate. Recursively iterates until all scores hit 90+ (max 3
  rounds). Use when asked to: "expert panel this", "score this", "rate these
  variants", "quality check this", "panel review", "which version is better",
  "expert score", "evaluate this copy/strategy/page", or when another skill
  needs a quality gate on its output. Also triggers on: "score this landing page",
  "expert panel these email variants", "rate this headline", "panel these charts".

Expert Panel

General-purpose scoring and iterative improvement engine. Auto-assembles the right experts for whatever is being evaluated, scores it, and loops until 90+.

---

Step 1: Intake — Understand What's Being Scored

Collect or infer from context:

1. **Content/artifact** — The thing(s) to score (paste, file path, or URL) 2. **Content type** — Copy, sequence, landing page, strategy, title, chart, candidate eval, etc. 3. **Offer context** — What's being sold/promoted? To whom? What domain/industry? 4. **Variants** — Are there multiple versions to compare? (A/B/C) 5. **Source skill** — Is this output from another skill? (e.g., cold-outbound-optimizer) If yes, note the source for feedback-to-source routing in Step 6.

If context is obvious from the conversation, don't ask — just proceed.

---

Step 2: Auto-Assemble the Expert Panel

Build a panel of **7–10 experts** tailored to the content type and domain.

Assembly rules

1. **Start with content-type experts.** Read `experts/` directory for pre-built panels matching the content type. If an exact match exists (e.g., `experts/linkedin.md` for a LinkedIn post), use it as the base.

2. **Add domain/offer experts.** Based on the offer context, add 1–3 experts who understand the specific industry or domain. Examples:

• Scoring bakery marketing → add Food & Beverage Marketing Expert
• Scoring SaaS landing page → add SaaS Conversion Expert
• Scoring recruiting outreach → add Agency Recruiter + Talent Market Expert
• Scoring medical device copy → add Healthcare Compliance Expert

3. **Always include these two:**

• **AI Writing Detector** — See `experts/humanizer.md`. Weight: 1.5x. Non-negotiable.
• **Brand Voice Match** — Checks alignment with the configured brand voice and

known rejection patterns from `references/patterns.md` (if present).

4. **Check learned patterns.** If `references/patterns.md` exists, read it. If any patterns apply to this content type, brief the panel on them. Dock points for known-bad patterns.

5. **Cap at 10 experts.** If you have more than 10, merge overlapping roles.

Panel output format

List each expert with: Name, lens/focus, what they check.

---

Step 3: Select Scoring Rubric

Choose the appropriate rubric from `scoring-rubrics/`:

| Content type | Rubric file | |---|---| | Blog, social, email, newsletter, scripts | `scoring-rubrics/content-quality.md` | | Strategy, recommendations, analysis | `scoring-rubrics/strategic-quality.md` | | Landing pages, ads, CTAs | `scoring-rubrics/conversion-quality.md` | | Charts, data viz, infographics | `scoring-rubrics/visual-quality.md` | | Candidate evaluations | `scoring-rubrics/evaluation-quality.md` | | Other | Synthesize a rubric from the two closest matches |

Read the selected rubric file for detailed criteria and point allocation.

---

Step 4: Score — Recursive Loop Until 90+

**Target: 90/100 across all experts. Non-negotiable. Max 3 rounds.**

Each round produces:

## Round [N] — Score: [AVG]/100

| Expert | Score | Key Feedback |
|--------|-------|--------------|
| [Name] | [0-100] | [One-line rationale] |
| ... | ... | ... |

**Aggregate:** [weighted average — humanizer at 1.5x]
**Top 3 weaknesses:** [ranked]
**Changes made:** [specific edits addressing each weakness]

Then the revised content/artifact.

Rules

• Scores must be brutally honest. No padding to 90.
• Humanizer score weighted 1.5x in the aggregate.
• If aggregate < 90: identify top 3 weaknesses → revise → next round.
• If aggregate ≥ 90: finalize and proceed to output.
• After 3 rounds, if still < 90: return best version with honest score + note on what's

holding it back.

• Show ALL rounds in output — the iteration trail is part of the value.

Variant comparison mode

When scoring multiple variants (A/B/C):

• Score each variant independently through the full panel.
• After scoring, rank variants by aggregate score.
• If top variant is < 90, iterate on the best one (don't iterate all of them).

---

Step 5: Output Format

Winner + Score (always at top)

## 🏆 Result: [SCORE]/100 — [PASS ✅ | NEEDS WORK ⚠️]

[Final content/artifact here]

**Iterations:** [N] rounds
**Panel:** [Expert names, comma-separated]

If variants: show winner first, then runner-up scores.

## 🏆 Winner: Variant [X] — [SCORE]/100

[Winning content]

### Runner-up scores
- Variant A: 87/100
- Variant B: 82/100
- Variant C: 91/100 ← Winner

Feedback History (below the result)

Show full scoring rounds.

---
<details>
<summary>📊 Scoring History (N rounds)</summary>

[All round tables from Step 4]

</details>

---

Step 6: Feedback-to-Source (When Scoring Another Skill's Output)

When the scored content came from another skill, generate a **Source Improvement Brief**:

## 🔁 Feedback for [Source Skill]

### What scored low
- [Pattern]: [Specific example from this content]

### Suggested skill improvements
- [Concrete change to the source skill's process/rubric/prompt]

### Patterns to add to source skill
- [Any recurring weakness that should become a rule]

This brief can be used to update the source skill's SKILL.md or rubrics.

---

Step 7: Memory — Lear