Code Execution

Execute Python locally with API access. 90-99% token savings for bulk operations.

When to Use

• Bulk operations (10+ files)
• Complex multi-step workflows
• Iterative processing across many files
• User mentions efficiency/performance

How to Use

Use direct Python imports in Claude Code:

from execution_runtime import fs, code, transform, git

# Code analysis (metadata only!)
functions = code.find_functions('app.py', pattern='handle_.*')

# File operations
code_block = fs.copy_lines('source.py', 10, 20)
fs.paste_code('target.py', 50, code_block)

# Bulk transformations
result = transform.rename_identifier('.', 'oldName', 'newName', '**/*.py')

# Git operations
git.git_add(['.'])
git.git_commit('feat: refactor code')

If not installed: Run ~/.claude/plugins/marketplaces/mhattingpete-claude-skills/execution-runtime/setup.sh

Available APIs

• Filesystem (fs): copy_lines, paste_code, search_replace, batch_copy
• Code Analysis (code): find_functions, find_classes, analyze_dependencies - returns METADATA only!
• Transformations (transform): rename_identifier, remove_debug_statements, batch_refactor
• Git (git): git_status, git_add, git_commit, git_push

Pattern

1. Analyze locally (metadata only, not source)
2. Process locally (all operations in execution)
3. Return summary (not data!)

Examples

Bulk refactor (50 files):

from execution_runtime import transform
result = transform.rename_identifier('.', 'oldName', 'newName', '**/*.py')
# Returns: {'files_modified': 50, 'total_replacements': 247}

Extract functions:

from execution_runtime import code, fs

functions = code.find_functions('app.py', pattern='.*_util$')  # Metadata only!
for func in functions:
    code_block = fs.copy_lines('app.py', func['start_line'], func['end_line'])
    fs.paste_code('utils.py', -1, code_block)

result = {'functions_moved': len(functions)}