intelli-verse-x Working directory
cd ~/.cursor/skills/ams-content-ops
# Windows: cd $env:USERPROFILE\.cursor\skills\ams-content-ops
Cursor install notes
- Skill root: this folder (scripts + references live here).
- Vendor source:
~/.cursor/skills/_vendor/ai-marketing-skills/(git pull to update). - Telemetry is optional; skip
telemetry/*.pyunless you opt in. - Run Python from this skill directory so relative paths resolve.
- Prefer
pythonon Windows ifpython3is missing.
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 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 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.
## 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.
## Reference Files
| File | Purpose | When to read |
|---|---|---|
| `experts/humanizer.md` | AI writing detection rubric (24 patterns) | Every scoring run |
| `experts/[domain].md` | Pre-built expert panels for common domains | When domain matches |
| `scoring-rubrics/content-quality.md` | Content scoring rubric | Content scoring |
| `scoring-rubrics/strategic-quality.md` | Strategy scoring rubric | Strategy scoring |
| `scoring-rubrics/conversion-quality.md` | Landing page/ad/CTA rubric | Conversion scoring |
| `scoring-rubrics/visual-quality.md` | Chart/data viz/infographic rubric | Visual scoring |
| `scoring-rubrics/evaluation-quality.md` | Candidate/assessment rubric | Eval scoring |
| `references/patterns.md` | Learned rejection patterns | Every scoring run |
| `references/expert-assembly.md` | Domain-expert examples for auto-assembly | When building unfamiliar panels |