بنقرة واحدة
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。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
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。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.