# Scrollclaw Score

> Score UGC videos for virality before publishing. 7 criteria, 0-100 each. Only 70+ gets published.

- Skill: `themattberman/scrollclaw-score` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add themattberman/scrollclaw-score`
- Raw SKILL.md: https://api.skillmd.com/api/skills/themattberman/scrollclaw-score/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: TheMattBerman (https://skillmd.com/u/themattberman)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/themattberman/scrollclaw-score

---


# Virality Scoring

Score every video before publishing. Read `references/virality-scoring.md` for the full methodology.

## Criteria

7 dimensions, scored 0-100 each:

1. **Hook Strength** — Does frame 1 + first 3 seconds force a pause?
2. **Emotional Impact** — Does it trigger a visceral response (not just "interesting")?
3. **Pacing** — Does every second earn the next second?
4. **Text Readability** — Are captions legible on mobile, properly timed?
5. **Scroll-Stop Power** — Would this stop a thumb mid-scroll in a feed?
6. **Completion Likelihood** — Will viewers watch to the end?
7. **Shareability** — Would someone send this to a friend?

## Thresholds

| Score | Action |
|-------|--------|
| 70+ | **Publish.** Ship it. |
| 60-69 | Polish — specific fixes can save it. Identify which dimensions are dragging. |
| Below 60 | **Regenerate.** Don't polish a bad clip. Go back to the failing stage. |

## Process

1. Watch the final video on a phone screen, in-app, scrolling past it like a user
2. Score each dimension independently
3. Calculate average
4. If below threshold, identify the weakest dimensions
5. Route back to the appropriate skill:
   - Hook/scroll-stop weak → `/first-frame` (regenerate canonical face)
   - Pacing/completion weak → `/persona` (restructure script)
   - Emotional impact weak → `/persona` (better language from research)
   - Text readability weak → `/assemble` (fix captions)
   - Audio/voice issues → `/assemble` (re-run S2S)

## Brand Memory Integration

### Reads
| File | Purpose |
|------|---------|
| `workspace/campaigns/<slug>/clips/final-*.mp4` | Video(s) to score |
| `workspace/campaigns/<slug>/brief.md` | Original campaign goals — score against intent, not just virality |
| `workspace/campaigns/<slug>/persona-research.md` | Whether the script language matches real customer language |

### Writes
| File | Notes |
|------|-------|
| `workspace/campaigns/<slug>/scores/score-<version>.md` | Score card with per-dimension breakdown and go/no-go |
| `workspace/campaigns/<slug>/learnings.md` | Append-only — what worked, what didn't, threshold notes |

### Context loading

```
📊 Score context loaded:
  ✓ Campaign: ridge-q1
  ✓ Final clips: 2 (workspace/campaigns/ridge-q1/clips/final-*.mp4)
  ✓ Brief: workspace/campaigns/ridge-q1/brief.md
  ✓ Persona research: workspace/campaigns/ridge-q1/persona-research.md
```

### Learnings append format

After every score, append to `workspace/campaigns/<slug>/learnings.md`:

```markdown
## YYYY-MM-DD — <campaign-slug> <format>

**Score:** <average>/100 (<go/no-go>)
**Dimension breakdown:** Hook <n> | Emotional <n> | Pacing <n> | Captions <n> | Scroll-stop <n> | Completion <n> | Share <n>

**What worked:**
- [specific finding]

**What didn't:**
- [specific finding]

**Remediation:** [if no-go: which skill to route back to and why]
```

Never overwrite this file. Only append.

## Contract

### Input
- Required: at least one final assembled clip
- Optional: campaign brief, persona research, prior campaign learnings
- Format: workspace video files plus markdown context
- Source: `/assemble`, `workspace/campaigns/<slug>/brief.md`, and `persona-research.md`

### Output
- Produces: one score card per clip, a go/no-go decision, and appended learnings
- Format: markdown files in `workspace/campaigns/<slug>/scores/` plus append-only entries in `learnings.md`
- Default behavior: score every final clip against the 7 dimensions and route the user back to the failing stage if the score is below 70
- Downstream use: publish/no-publish decision and future campaign learnings

### Validation
- Pre-conditions: final clip exists and is watchable end to end
- Post-conditions: score includes a dimension breakdown, a threshold decision, and clear remediation if needed
- Failure checks: do not recommend publishing without a real breakdown; if the clip is unscorable or missing context, flag the gap explicitly

## Output

- Virality score card in `workspace/campaigns/<slug>/scores/score-<version>.md`
- Go/no-go decision
- Learnings appended to `workspace/campaigns/<slug>/learnings.md`
- If no-go: specific remediation routing

## Batch Scoring

For campaign batches, score all variants and rank. Publish only the 70+ tier. Below-60 variants get regenerated, not tweaked.

```bash
bash scripts/batch-campaign.sh \
  --workspace workspace/campaigns/<slug> \
  --creators workspace/campaigns/<slug>/creators/ \
  --formats "talking-head,pov-demo,podcast-clip" \
  --dual-output
```