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".
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".
description_zh
内容质量评分与专家评审面板，递归迭代至 90+ 分
description_en
Content quality scoring with expert panel review, iterates until 90+ score
version
1.0.0
homepage
https://github.com/ericosiu/ai-marketing-skills
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null ||
python3 telemetry/telemetry_init.py 2>/dev/null ||
true
# Telemetry opt-in (first run only, then remembers your choice)
true
Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.
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:
Content/artifact — The thing(s) to score (paste, file path, or URL)
Content type — Copy, sequence, landing page, strategy, title, chart, candidate eval, etc.
Offer context — What's being sold/promoted? To whom? What domain/industry?
Variants — Are there multiple versions to compare? (A/B/C)
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
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.
Add domain/offer experts. Based on the offer context, add 1–3 experts who understand
the specific industry or domain. Examples:
Scoring medical device copy → add Healthcare Compliance Expert
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).
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.
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.
---
<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 — Learn from Approvals and Rejections
After the user approves or rejects panel output:
On approval (score ≥ 90, user accepts)
Note what worked. No action needed unless a new positive pattern emerges.
On rejection (user overrides the panel or rejects 90+ content)
Ask why (or infer from context).
Add a new pattern to references/patterns.md using this format:
## [Pattern Name]-**Type:** rejection | preference | override
-**Content types:** [which types this applies to]
-**Rule:** [What to always/never do]
-**Example:** [The specific instance that triggered this]
-**Date:** [YYYY-MM-DD]
-**Point dock:** [-N points when detected]
Confirm: "Added pattern: [one-line summary]. Panel will dock [N] points for this going forward."
Pattern enforcement
Every scoring round, check references/patterns.md against the content. Apply point docks
before expert scoring begins. This means known-bad patterns are penalized even if individual
experts miss them.
