shorts

Files (5)

• SKILL.md
• caption-styles.md
• platform-specs.md
• remotion-patterns.md
• scoring-rubric.md

SKILL.md (first lines)

# shorts — Interactive Shortform Video Creator

You are an interactive shortform video producer. You guide the user through a 10-step
pipeline where YOU (Claude) analyze the transcript, identify the best segments, present
them for approval, snap boundaries to natural audio cut points, and render premium
vertical videos with animated captions.

## Pre-Flight

Before starting, locate the project root:
```bash
# Try common locations in priority order
SHORTS_ROOT=""
for dir in "$HOME/.claude/skills/shorts" "$HOME/.claude/skills/claude-shorts" "$HOME/claude-shorts" "$(pwd)"; do
    if [ -f "$dir/SKILL.md" ]; then
        SHORTS_ROOT="$dir"
        break
    fi
done
if [ -z "$SHORTS_ROOT" ]; then
    echo "ERROR: shorts skill project root not found. Please run from the project directory or install with install.sh"
fi
```

Set up the temp directory (configurable via `SHORTS_TMP` environment variable):
```bash
SHORTS_TMP="${SHORTS_TMP:-/tmp/claude-shorts}"
mkdir -p "$SHORTS_TMP/clips"
```

## 10-Step Interactive Pipeline

### Step 1: PREFLIGHT

Run safety checks on the input video:

```bash
bash "$SHORTS_ROOT/scripts/preflight.sh" INPUT_FILE [OUTPUT_DIR]
```

If preflight fails, report errors and stop. If warnings exist, report them and ask
the user whether to proceed.

Also detect GPU capabilities:
```bash
bash "$SHORTS_ROOT/scripts/detect_gpu.sh"
```

Report to user: input duration, resolution, GPU status, estimated processing time.

### Step 2: TRANSCRIBE

Transcribe with faster-whisper (GPU-accelerated, word-level timestamps).
Audio extraction is handled internally by transcribe.py:
```bash
VENV="$HOME/.video-skill"
[ -d "$VENV" ] || VENV="$HOME/.shorts-skill"
source "$VENV/bin/activate"

python3 "$SHORTS_ROOT/scripts/transcribe.py" INPUT_FILE \
    --output $SHORTS_TMP/transcript.json
```

Output is dual-format JSON:
- `segments[]` — WhisperX-style with word timestamps (for Claude to read)
- `captions[]` — Remotion-native `{text, startMs, endMs}` array (for rendering)

Report to user: transcription time, word count, language detected.

### Step 3: DETECT CONTENT TYPE

Auto-detect whether the video is talking-head, screen recording, or podcast:
```bash
python3 "$SHORTS_ROOT/scripts/detect_content.py" INPUT_FILE \
    --output $SHORTS_TMP/content_type.json
```

Report detected type to user. Ask if they want to override.
- **talking-head**: Face-tracked center crop to 9:16
- **screen**: Letterboxed framed layout (content centered, dark padding)
- **podcast**: Side-by-side speaker tracking or center crop

### Step 4: ANALYZE — Claude Reads Transcript

Read the full transcript directly:
```
Read $SHORTS_TMP/transcript.json
```

Also load the scoring rubric:
```
Read $SHORTS_ROOT/references/scoring-rubric.md
```

Score 8-12 candidate segments (15-55 seconds each) on 5 dimensions:

| Dimension | Weight | What to look for |
|-----------|--------|------------------|
| Hook strength | 0.30 | Bold claims, curiosity gaps, value promises, pattern interrupts |
| Standalone coherence | 0.25 | Makes complete sense without any context from the rest of the video |
| Emotional intensity | 0.20 | Strong opinions, surprise reveals, humor, passion |
| Value density | 0.15 | Actionable insights, data points, frameworks per second |
| Payoff quality | 0.10 | Satisfying conclusion — punchline, reveal, call-to-action |

**Weighted score** = sum of (dimension_score * weight), scale 0-100.

For each candidate, identify:
- Start/end timestamps (to the nearest second)
- A suggested hook line (first 3 seconds of text overlay)
- Brief rationale (1 sentence explaining why this segment works)

**Transcript cleanup:** While analyzing, also produce cleaned captions for rendering.
Read the `captions[]` array from transcript.json, then:
1. Remove filler words (um, uh, you know, like, sort of, I mean, right, basically, actually)
2. Fix obvious transcription errors based on surrounding context
3. Consolidate incomplete sentence fragments where appropriate
4. **Keep all timestamps unchanged** — only modify the `text` field

Write the cleaned transcript to `$SHORTS_TMP/transcript_cleaned.json` using the same

From 45ck/content-machine, MIT.

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