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skills/code-execution/SKILL.md
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skills/code-execution/SKILL.md
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name: code-execution
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description: 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.
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---
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# Code Execution
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Execute Python locally with API access. **90-99% token savings** for bulk operations.
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## 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
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## How to Use
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Use direct Python imports in Claude Code:
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```python
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from execution_runtime import fs, code, transform, git
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# Code analysis (metadata only!)
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functions = code.find_functions('app.py', pattern='handle_.*')
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# File operations
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code_block = fs.copy_lines('source.py', 10, 20)
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fs.paste_code('target.py', 50, code_block)
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# Bulk transformations
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result = transform.rename_identifier('.', 'oldName', 'newName', '**/*.py')
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# Git operations
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git.git_add(['.'])
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git.git_commit('feat: refactor code')
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```
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**If not installed:** Run `~/.claude/plugins/marketplaces/mhattingpete-claude-skills/execution-runtime/setup.sh`
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## 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
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## Pattern
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1. **Analyze locally** (metadata only, not source)
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2. **Process locally** (all operations in execution)
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3. **Return summary** (not data!)
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## Examples
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**Bulk refactor (50 files):**
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```python
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from execution_runtime import transform
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result = transform.rename_identifier('.', 'oldName', 'newName', '**/*.py')
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# Returns: {'files_modified': 50, 'total_replacements': 247}
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```
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**Extract functions:**
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```python
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from execution_runtime import code, fs
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functions = code.find_functions('app.py', pattern='.*_util$') # Metadata only!
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for func in functions:
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code_block = fs.copy_lines('app.py', func['start_line'], func['end_line'])
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fs.paste_code('utils.py', -1, code_block)
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result = {'functions_moved': len(functions)}
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```
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**Code audit (100 files):**
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```python
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from execution_runtime import code
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from pathlib import Path
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files = list(Path('.').glob('**/*.py'))
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issues = []
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for file in files:
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deps = code.analyze_dependencies(str(file)) # Metadata only!
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if deps.get('complexity', 0) > 15:
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issues.append({'file': str(file), 'complexity': deps['complexity']})
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result = {'files_audited': len(files), 'high_complexity': len(issues)}
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```
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## Best Practices
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✅ Return summaries, not data
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✅ Use code_analysis (returns metadata, not source)
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✅ Batch operations
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✅ Handle errors, return error count
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❌ Don't return all code to context
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❌ Don't read full source when you need metadata
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❌ Don't process files one by one
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## Token Savings
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| Files | Traditional | Execution | Savings |
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|-------|-------------|-----------|---------|
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| 10 | 5K tokens | 500 | 90% |
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| 50 | 25K tokens | 600 | 97.6% |
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| 100 | 150K tokens | 1K | 99.3% |
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