analyzelisted
Install: claude install-skill Shweta-Mishra-ai/tokenmizer
Analyze a file using TokenMizer's file intelligence layer.
## IMPORTANT rule
**Never ask the user to paste the file content.** Always call TokenMizer to analyze it from the path. Pasting a 50,000-row CSV = 400,000 tokens. TokenMizer reduces it to ~450 tokens.
## What to do
Parse $ARGUMENTS:
- First word = file path
- Remaining words = query (what user wants to know)
```bash
FILE_PATH=$(echo "$ARGUMENTS" | awk '{print $1}')
QUERY=$(echo "$ARGUMENTS" | cut -d' ' -f2-)
python3 -c "
from tokenmizer.filters.file_intelligence import FileIntelligence
fi = FileIntelligence()
result = fi.process(
open('${FILE_PATH}', 'rb').read(),
'${FILE_PATH}'.split('/')[-1],
token_budget=600,
query='${QUERY}'
)
print(f'File: {result.file_type} | {result.original_tokens:,} → {result.extracted_tokens} tokens ({result.savings_pct:.0f}% saved)')
print()
print(result.content)
"
```
## Token savings by file type
| Type | Typical savings |
|---|---|
| CSV (50k rows) | 99.9% |
| PDF (200 pages) | 98.8% |
| Excel (10 sheets) | 99.7% |
| JSON (1k items) | 95% |
| Code (large file) | 60-80% |
## If TokenMizer not installed
```bash
pip install "tokenmizer[anthropic]"
```
## Examples of $ARGUMENTS
- `/data/sales.csv` → analyze with no specific query
- `/data/sales.csv which regions are underperforming` → targeted analysis
- `/reports/Q1.pdf key findings and risks` → relevant page extraction
- `/data/users.xlsx find inactive accounts` → per-sheet analysis