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monitor-solver
监控求解进度,检查中间结果,估算剩余时间。当用户说'检查进度'、'is it done'、'monitor'、'求解完了吗'时使用。
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
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监控求解进度,检查中间结果,估算剩余时间。当用户说'检查进度'、'is it done'、'monitor'、'求解完了吗'时使用。
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
数据清洗、EDA、缺失值处理、异常值检测、相关性分析。触发词: 数据预处理、数据清洗、EDA、缺失值、异常值、data preprocessing、数据探索。
模型验证:交叉验证、留出法、残差分析、与已知解对比、假设检验。触发词: 模型验证、交叉验证、残差分析、model validation、留出法、误差分析、假设检验。
多子问题拆解与依赖分析。触发词: 子问题拆解、拆题、problem decomposition、依赖关系、求解顺序、时间分配、并行安排。
灵敏度分析:参数扰动、单因素/多因素分析、Monte Carlo 模拟、龙卷风图/蛛网图。触发词: 灵敏度分析、参数敏感性、sensitivity analysis、Monte Carlo、鲁棒性测试、参数扰动。
自动多轮评审优化循环。通过 Codex MCP 反复评审→修改→重新评审,直到达标或达到最大轮数。当用户说'自动优化循环'、'auto optimize'、'评审到通过'时使用。
Autonomously improve a generated paper via GPT-5.4 xhigh review → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.
| name | monitor-solver |
| description | 监控求解进度,检查中间结果,估算剩余时间。当用户说'检查进度'、'is it done'、'monitor'、'求解完了吗'时使用。 |
| argument-hint | ["solver-name or log-file"] |
| allowed-tools | Bash(*), Read, Write, Edit, Grep, Glob |
Monitor: $ARGUMENTS
# Check for running solver processes
ps aux | grep -E "python|matlab" | grep -v grep
# Check if PID file exists
if [ -f solver_pid.txt ]; then
PID=$(cat solver_pid.txt)
ps -p $PID > /dev/null 2>&1 && echo "Solver running (PID: $PID)" || echo "Solver finished/crashed"
fi
Check the most recent log files:
# Find recent log files
ls -lt *.log solve_log.txt *.txt 2>/dev/null | head -10
# Show last 50 lines of the most recent log
tail -50 solve_log.txt 2>/dev/null
If no log files found, check for output files in results/ directory.
# Check for result files
ls -lt results/*.{json,csv,xlsx,txt} output/*.{json,csv,xlsx,txt} 2>/dev/null | head -20
If result files exist, read and parse them:
# For CSV results
python3 -c "import pandas as pd; df = pd.read_csv('results/latest.csv'); print(df.describe())"
# For JSON results
python3 -c "import json; data = json.load(open('results/latest.json')); print(json.dumps(data, indent=2, ensure_ascii=False)[:2000])"
Parse the log file for key solving metrics:
python3 -c "
import re
with open('solve_log.txt') as f:
lines = f.readlines()
# Extract iteration progress
iterations = [l for l in lines if 'iteration' in l.lower() or 'iter' in l.lower()]
if iterations:
print('Latest iteration:', iterations[-1].strip())
# Extract objective function values
objectives = [l for l in lines if 'objective' in l.lower() or 'obj' in l.lower()]
if objectives:
print('Latest objective:', objectives[-1].strip())
# Extract convergence info
convergence = [l for l in lines if 'residual' in l.lower() or 'converge' in l.lower()]
if convergence:
print('Convergence:', convergence[-1].strip())
# Extract timing info
timing = [l for l in lines if 'time' in l.lower() or 'elapsed' in l.lower()]
if timing:
print('Timing:', timing[-1].strip())
"
What to extract:
This gives the /auto-optimize-loop richer signal than just log output — convergence trends, objective evolution, and timing estimates.
Present results in a comparison table:
| 子问题 | 方法 | 关键指标 | 与预期对比 | 状态 |
|--------|------|---------|----------|------|
| 问题1 | 线性规划 | 最优值=X.XX | 符合预期 | done |
| 问题2 | 回归分析 | R²=0.95 | +0.05 vs baseline | done |
Generate a brief progress report:
=== 求解进度报告 ===
运行状态: [running/completed/error]
已完成子问题: X/Y
当前子问题: [name] (迭代 M/N)
目标函数趋势: [converging/oscillating/diverging]
已用时间: XX min
预估剩余: XX min
cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.