Code Execution
Execute Python locally with API access. 90-99% token savings for bulk operations.
When to Use
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Bulk operations (10+ files)
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Complex multi-step workflows
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Iterative processing across many files
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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
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Filesystem (fs ): copy_lines, paste_code, search_replace, batch_copy
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Code Analysis (code ): find_functions, find_classes, analyze_dependencies - returns METADATA only!
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Transformations (transform ): rename_identifier, remove_debug_statements, batch_refactor
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Git (git ): git_status, git_add, git_commit, git_push
Pattern
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Analyze locally (metadata only, not source)
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Process locally (all operations in execution)
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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)}
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%