Claudeshrink

Overview

ClaudeShrink compresses large inputs using LLMLingua (gpt2) before you reason over them. This preserves semantic content while dramatically reducing token usage.

The compressor lives at: ~/.claude/skills/ClaudeShrink/scripts/compressor.py It runs inside an isolated venv at: ~/.claude/skills/ClaudeShrink/.venv

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When to Use

• User pastes a large block of text, logs, or a document (>~8000 chars / ~2000 tokens)
• User asks to analyze, summarize, or reason over a large file on disk
• User's prompt is very long and would benefit from compression before reasoning
• User explicitly says "use ClaudeShrink" or "compress this"

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Instructions

Follow these steps in order every time this skill is triggered:

1. Self-check: verify the environment is installed. Run:

``bash test -f ~/.claude/skills/ClaudeShrink/.venv/bin/python && echo "ready" || echo "not_installed" ``

• If output is ready, proceed to step 2.
• If output is not_installed, run the installer first:

``bash bash ~/.claude/skills/ClaudeShrink/install.sh ` If install.sh is missing (skill was added without cloning), fetch and run it: `bash curl -fsSL https://raw.githubusercontent.com/g-akshay/ClaudeShrink/main/install.sh | bash `` Wait for it to complete, then proceed to step 2.

1. Identify the input source — is it a file path, raw pasted text, or a prompt?

1. Extract user intent — look at the user's request and derive a --question flag that captures what they care about. Examples:

• "find all errors" → --question "What errors occurred?"
• "summarize payment failures" → --question "What payment failures occurred?"
• "keep all WARNING and ERROR lines" → --question "What warnings and errors occurred?"
• No specific focus → omit --question (blind compression)

1. If it's a file on disk, run:

``bash ~/.claude/skills/ClaudeShrink/.venv/bin/python ~/.claude/skills/ClaudeShrink/scripts/compressor.py /absolute/path/to/file.txt --question "derived question here" ``

1. If it's raw pasted text or a prompt (no file on disk), write to a uniquely-named temp file, compress, then delete:

Write the actual input content into the heredoc (do not write a placeholder string): ```bash TMP=$(mktemp /tmp/cs_input.XXXXXX.txt) cat > "$TMP" "Input compressed with ClaudeShrink (LLMLingua). Compression stats: [paste ratio from stderr if available]."

1. Proceed with the user's original request using the compressed context.

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Output Format

• Do not show the raw compressed text to the user unless they ask for it.
• Respond to the user's original request (summarize, analyze, explain, etc.) as normal.
• Optionally append a brief compression note: original size, compressed token target, ratio.

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Examples

Example 1 — Large log file with intent: > User: "Find all payment failures in this log: /var/log/app.log"

~/.claude/skills/ClaudeShrink/.venv/bin/python ~/.claude/skills/ClaudeShrink/scripts/compressor.py /var/log/app.log --question "What payment failures occurred?"

Then analyze the compressed output.

Example 2 — Pasted text with intent: > User: "Summarize the errors in this log" then pastes 800 lines.

TMP=$(mktemp /tmp/cs_input.XXXXXX.txt)
cat > "$TMP"  User: "Compress this before you read it: [long prompt]"

Omit `--question` — blind compression applies.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [g-akshay](https://github.com/g-akshay)
- **Source:** [g-akshay/ClaudeShrink](https://github.com/g-akshay/ClaudeShrink)
- **License:** MIT

Install and usage instructions live in the source repository linked above.