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 Analyze locally (metadata only, not source) Process locally (all operations in execution) Return summary (not data!) Examples

Bulk refactor (50 files):

from execution_runtime import transform result = transform.rename_identifier('.', 'oldName', 'newName', '*/.py')

Returns:

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)}

Code audit (100 files):

from execution_runtime import code from pathlib import Path

files = list(Path('.').glob('*/.py')) issues = []

for file in files: deps = code.analyze_dependencies(str(file)) # Metadata only! if deps.get('complexity', 0) > 15: issues.append({'file': str(file), 'complexity': deps['complexity']})

result = {'files_audited': len(files), 'high_complexity': len(issues)}

Best Practices

✅ Return summaries, not data ✅ Use code_analysis (returns metadata, not source) ✅ Batch operations ✅ Handle errors, return error count

❌ Don't return all code to context ❌ Don't read full source when you need metadata ❌ Don't process files one by one

Token Savings Files Traditional Execution Savings 10 5K tokens 500 90% 50 25K tokens 600 97.6% 100 150K tokens 1K 99.3%