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name: dont-be-greedy description: | When a user uploads or references a data file (CSV, JSON, XLSX, TXT, LOG) or any file larger than 100KB, immediately estimate token cost using scripts/estimate_size.py. If >30k tokens, chunk the file and summarize each chunk. If smaller, run quick inspection. Return a safe preview and summary without asking the user what to do. allowed-tools: | bash: python, cat, head, tail, wc, ls, file file: read

Don't Be Greedy

Instructions

Step 1: Estimate Token Cost

Before loading ANY data file:

Copypython scripts/estimate_size.py "<file_path>"

This returns byte count and estimated token count.

Step 2: Apply Strategy Based on Size

Step 3: Execute Appropriate Workflow

Copypython scripts/quick_inspect.py "<file_path>"

Return stats and load file directly.

Copypython scripts/chunker.py "<file_path>"
python scripts/summarize.py "<chunk_file>"

Return overall summary + per-chunk summaries + safe preview of first rows.

Step 4: Return Structured Output

Always provide:

• Overall summary (1-3 paragraphs)
• Safe preview (first N rows/lines)
• Recommendation for next steps
• Chunk information if file was split

NEVER

• Load files without running estimate_size.py first
• Use cat on unknown or large files
• Ask "What would you like me to do with this file?"
• Wait for user direction before acting on file uploads
• Load raw data exceeding 30k tokens into context

ALWAYS

• Run size estimation before any file operation
• Chunk files over 30k tokens automatically
• Provide a safe preview even for large files
• Act immediately when a data file is detected
• Be thorough in first response with summary + preview + recommendation

Examples

Example 1: User uploads large CSV

Input: User says "Analyze this sales data" and uploads a 50MB CSV file

Workflow:

1. Run scripts/estimate_size.py sales.csv → Output: bytes=52428800 (50.0MB) tokens=13107200
2. Way over 30k tokens. Run scripts/chunker.py sales.csv → Creates 6500+ chunks
3. Run scripts/summarize.py on representative chunks
4. Return:
   • Overall summary of data structure and content
   • Safe preview showing first 10 rows
   • Recommendation: "Data contains 1M rows of sales transactions. I've chunked it for processing. Want me to analyze specific columns or date ranges?"

Example 2: User references small JSON config

Input: User asks "Check my config.json for issues"

Workflow:

1. Run scripts/estimate_size.py config.json → Output: bytes=2048 (2.0KB) tokens=512
2. Under 10k tokens. Run scripts/quick_inspect.py config.json
3. Load file directly and analyze
4. Return: Full analysis with any issues found

Example 3: User uploads medium log file

Input: User uploads a 500KB application.log

Workflow:

1. Run scripts/estimate_size.py application.log → Output: bytes=512000 (500.0KB) tokens=128000
2. Over 30k tokens. Run scripts/chunker.py application.log
3. Summarize chunks focusing on errors and warnings
4. Return:
   • Summary of log timespan and key events
   • Count of errors, warnings, info messages
   • Safe preview of recent entries
   • Recommendation for focused analysis