code-execution

name	code-execution
description	Execute scientific Python code for computation, data analysis, simulation, and verification. Use when: (1) running statistical analyses, (2) numerical computation, (3) data processing pipelines, (4) verifying calculations, (5) running simulations. NOT for: literature search (use literature-search), writing papers (use paper-writing), or non-computational tasks.
metadata	{"openclaw":{"emoji":"💻","requires":{"bins":["python3"]}}}

Code Execution (Meta Skill)

Execute scientific Python code for computation, analysis, simulation, and verification of results.

Common Imports

import numpy as np
import pandas as pd
from scipy import stats, optimize, integrate, signal
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
import json, csv, sys
from collections import Counter, defaultdict

Pattern 1: Statistical Analysis

import numpy as np
from scipy import stats

data_a, data_b = np.array([...]), np.array([...])
print(f"Group A: mean={np.mean(data_a):.4f}, std={np.std(data_a, ddof=1):.4f}, n={len(data_a)}")
print(f"Group B: mean={np.mean(data_b):.4f}, std={np.std(data_b, ddof=1):.4f}, n={len(data_b)}")

t_stat, p_value = stats.ttest_ind(data_a, data_b, equal_var=False)
print(f"Welch's t-test: t={t_stat:.4f}, p={p_value:.6f}")

# Effect size (Cohen's d)
pooled_std = np.sqrt((np.std(data_a, ddof=1)**2 + np.std(data_b, ddof=1)**2) / 2)
print(f"Cohen's d: {(np.mean(data_a) - np.mean(data_b)) / pooled_std:.4f}")

Pattern 2: Numerical Computation

from scipy import integrate, optimize

result, error = integrate.quad(lambda x: np.exp(-x**2), -np.inf, np.inf)
print(f"Integral result: {result:.6f} (error: {error:.2e})")

solution = optimize.fsolve(lambda v: [v[0]**2+v[1]**2-4, v[0]-v[1]-1], [1, 0])
print(f"Solution: x={solution[0]:.4f}, y={solution[1]:.4f}")

Pattern 3: Data Processing

import pandas as pd
from io import StringIO

df = pd.read_csv(StringIO("col1,col2\n1,2\n3,4"))
df = df.dropna()
df['computed'] = df['col1'] * df['col2']
print(df.groupby('col1').agg({'col2': ['mean', 'std', 'count']}).round(4).to_string())

Pattern 4: Monte Carlo Simulation

np.random.seed(42)
n = 100000
x, y = np.random.uniform(-1, 1, n), np.random.uniform(-1, 1, n)
pi_est = 4 * np.sum(x**2 + y**2 <= 1) / n
print(f"Pi estimate: {pi_est:.6f} (error: {abs(pi_est - np.pi):.6f})")

Pattern 5: Verification

# Verify claimed results against raw data
actual_mean = np.mean(data)
se = np.std(data, ddof=1) / np.sqrt(len(data))
ci = (actual_mean - 1.96*se, actual_mean + 1.96*se)
print(f"Mean: {actual_mean:.2f}, 95% CI: ({ci[0]:.2f}, {ci[1]:.2f})")
print(f"Verification: {'PASS' if abs(claimed - actual_mean) < 0.5 else 'FAIL'}")

Error Handling

try:
    result = perform_computation(data)
except (ValueError, np.linalg.LinAlgError) as e:
    print(f"Error: {e}")
except MemoryError:
    print("Data too large. Consider chunked processing.")

Output Formatting

print("=" * 50)
print(f"RESULTS | n={n} | mean={mean:.4f} | p={p:.6f}")
print("=" * 50)

Sandbox Constraints

1. No network access -- data must be provided inline or on disk.
2. No persistent state -- each execution is independent.
3. Memory limits -- use chunked processing for large data.
4. Time limits -- reduce iterations for long simulations.

Best Practices

1. Set random seeds and report library versions for reproducibility.
2. Use np.float64 and np.log1p for numerical stability.
3. Prefer vectorized NumPy over Python loops.
4. Validate input shapes, types, and ranges before computation.
5. Report appropriate significant figures; do not over-report precision.
6. Check statistical test assumptions before applying parametric methods.
7. Apply Bonferroni/FDR correction for multiple comparisons.