gh-mhattingpete-claude-skills-marketplace-code-operations-plugin/skills/code-execution/SKILL.md

name, description

name	description
code-execution	Execute Python code locally with marketplace API access for 90%+ token savings on bulk operations. Activates when user requests bulk operations (10+ files), complex multi-step workflows, iterative processing, or mentions efficiency/performance.

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)}

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%