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Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps
---
name: resilient-spreadsheet-workflow-8e99628afa79
description: Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps
---
# Resilient Spreadsheet Workflow
This skill provides a robust workflow for processing spreadsheet files (CSV, XLSX) when `execute_code_sandbox` encounters failures. It uses a combination of tools to ensure reliable execution and clear error diagnosis.
## When to Use
- Processing spreadsheet files when `execute_code_sandbox` returns unknown errors
- Need to debug why spreadsheet operations fail
- Require reliable file I/O with verification at each step
## Step-by-Step Workflow
### Step 1: Initial Exploration with shell_agent
Start by using `shell_agent` to explore the file structure and understand what files exist:
```
Use shell_agent to:
- List files in the working directory
- Identify spreadsheet files (CSV, XLSX)
- Examine file sizes and basic structure
```
**Example task for shell_agent:**
```
Explore the current directory to find all spreadsheet files (CSV, XLSX).
List their sizes and identify which files need processing.
```
### Step 2: Write Processing Script with write_file
Create a standalone script file rather than executing inline code:
```python
# Use write_file to create a script like "process_sheet.py"
content = """
import pandas as pd
import sys
try:
df = pd.read_csv('input.csv') # or pd.read_excel for XLSX
# Perform your processing
result = df.describe()
result.to_csv('output.csv', index=False)
print("Processing complete")
except Exception as e:
print(f"Error: {e}", file=sys.stderr)
sys.exit(1)
"""
write_file(path="process_sheet.py", content=content)
```
### Step 3: Execute with run_shell and Error Redirection
Execute the script with stderr redirected to stdout for complete error capture:
```bash
run_shell command="python process_sheet.py 2>&1"
```
The `2>&1` redirection ensures both stdout and stderr are captured together, making error messages visible.
### Step 4: Isolate Errors with Verification Commands
If Step 3 fails, add targeted verification commands to diagnose the actual problem:
```bash
# Check if Python is available
run_shell command="which python 2>&1"
# Check if pandas is installed
run_shell command="python -c 'import pandas' 2>&1"
# Verify input file exists and is readable
run_shell command="ls -la input.csv 2>&1"
# Check file encoding/content
run_shell command="file input.csv 2>&1"
run_shell command="head -5 input.csv 2>&1"
```
### Step 5: Verify Output Before Reading
Before reading results with `read_file`, verify the output file exists:
```
list_dir(path=".")
# Confirm output file appears in the listing
# Then proceed to read_file
```
## Complete Example
```
1. shell_agent(task="Find and describe all CSV/XLSX files in current directory")
2. write_file(path="analyze_data.py", content="<processing script>")
3. run_shell(command="python analyze_data.py 2>&1")
4. If error: run_shell(command="python -c 'import pandas; print(pandas.__version__)' 2>&1")
5. list_dir(path=".") # Verify output exists
6. read_file(filetype="csv", file_path="output.csv")
```
## Common Error Patterns
| Symptom | Diagnostic Command | Likely Cause |
|---------|-------------------|--------------|
| Module not found | `python -c 'import pandas'` | Missing dependency |
| File not found | `ls -la <filename>` | Wrong path or name |
| Permission denied | `ls -la <filename>` | File permissions |
| Encoding error | `file <filename>` | Wrong file format |
## Best Practices
1. **Always use 2>&1** with run_shell for shell commands to capture full error output
2. **Verify before reading** - use list_dir to confirm file existence before read_file
3. **Break into small steps** - separate exploration, script writing, execution, and verification
4. **Use shell_agent for unknowns** - when directory structure or file types are unclear
5. **Write scripts to files** - more reliable than inline code execution for complex operations---
name: resilient-spreadsheet-workflow-8e99628afa79
description: Resilient multi-step workflow for spreadsheet processing when execute_code_sandbox fails, using shell_agent exploration, file-based scripts, and verification steps
---
# Resilient Spreadsheet Workflow
This skill provides a robust workflow for processing spreadsheet files (CSV, XLSX) when `execute_code_sandbox` encounters failures. It uses a combination of tools to ensure reliable execution and clear error diagnosis.
## When to Use
- Processing spreadsheet files when `execute_code_sandbox` returns unknown errors
- Need to debug why spreadsheet operations fail
- Require reliable file I/O with verification at each step
## Step-by-Step Workflow
### Step 1: Initial Exploration with shell_agent
Start by using `shell_agent` to explore the file structure and understand what files exist:
```
Use shell_agent to:
- List files in the working directory
- Identify spreadsheet files (CSV, XLSX)
- Examine file sizes and basic structure
```
**Example task for shell_agent:**
```
Explore the current directory to find all spreadsheet files (CSV, XLSX).
List their sizes and identify which files need processing.
```
### Step 2: Write Processing Script with write_file
Create a standalone script file rather than executing inline code:
```python
# Use write_file to create a script like "process_sheet.py"
content = """
import pandas as pd
import sys
try:
df = pd.read_csv('input.csv') # or pd.read_excel for XLSX
# Perform your processing
result = df.describe()
result.to_csv('output.csv', index=False)
print("Processing complete")
except Exception as e:
print(f"Error: {e}", file=sys.stderr)
sys.exit(1)
"""
write_file(path="process_sheet.py", content=content)
```
### Step 3: Execute with run_shell and Error Redirection
Execute the script with stderr redirected to stdout for complete error capture:
```bash
run_shell command="python process_sheet.py 2>&1"
```
The `2>&1` redirection ensures both stdout and stderr are captured together, making error messages visible.
### Step 4: Isolate Errors with Verification Commands
If Step 3 fails, add targeted verification commands to diagnose the actual problem:
```bash
# Check if Python is available
run_shell command="which python 2>&1"
# Check if pandas is installed
run_shell command="python -c 'import pandas' 2>&1"
# Verify input file exists and is readable
run_shell command="ls -la input.csv 2>&1"
# Check file encoding/content
run_shell command="file input.csv 2>&1"
run_shell command="head -5 input.csv 2>&1"
```
### Step 5: Verify Output Before Reading
Before reading results with `read_file`, verify the output file exists:
```
list_dir(path=".")
# Confirm output file appears in the listing
# Then proceed to read_file
```
## Complete Example
```
1. shell_agent(task="Find and describe all CSV/XLSX files in current directory")
2. write_file(path="analyze_data.py", content="<processing script>")
3. run_shell(command="python analyze_data.py 2>&1")
4. If error: run_shell(command="python -c 'import pandas; print(pandas.__version__)' 2>&1")
5. list_dir(path=".") # Verify output exists
6. read_file(filetype="csv", file_path="output.csv")
```
## Common Error Patterns
| Symptom | Diagnostic Command | Likely Cause |
|---------|-------------------|--------------|
| Module not found | `python -c 'import pandas'` | Missing dependency |
| File not found | `ls -la <filename>` | Wrong path or name |
| Permission denied | `ls -la <filename>` | File permissions |
| Encoding error | `file <filename>` | Wrong file format |
## Best Practices
1. **Always use 2>&1** with run_shell for shell commands to capture full error output
2. **Verify before reading** - use list_dir to confirm file existence before read_file
3. **Break into small steps** - separate exploration, script writing, execution, and verification
4. **Use shell_agent for unknowns** - when directory structure or file types are unclear
5. **Write scripts to files** - more reliable than inline code execution for complex operationsImported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.