What happened / 发生了什么

上传 Excel 文件（.xlsx）要求分析，Agent 尝试使用 astrbot_execute_python 工具读取文件。代码中包含 print() 语句，但工具返回结果始终为 No output.。这导致 Agent 无法获取文件内容，陷入反复调用同一工具或尝试 Shell 命令的循环，最终无法完成用户的回归分析与 MLE 估计验证任务。

日志显示：

1. astrbot_file_read_tool 正确提示不支持二进制文件。
2. Agent 转而使用 astrbot_execute_python 执行读取 Excel 的代码。
3. 每次执行返回 No output.（stdout 似乎未被捕获）。
4. Agent 之后通过 astrbot_execute_shell 内联 Python 才勉强获得部分输出，但整体任务未能完成。
5. 工具被连续重复调用，系统多次提示重复工具使用警告。

Reproduce / 如何复现？

启动 AstrBot，确保 Python 工具可用

发送任意包含 Python 代码的消息，触发 astrbot_execute_python 工具调用

AstrBot version, deployment method (e.g., Windows Docker Desktop deployment), provider used, and messaging platform used. / AstrBot 版本、部署方式（如 Windows Docker Desktop 部署）、使用的提供商、使用的消息平台适配器

AstrBot 版本: v4.23.1

部署方式:astrbot Launcher 部署

Python 版本: 3.12

OS

Windows

Logs / 报错日志

[2026-04-20 23:05:33.416] [Core] [INFO] [core.event_bus:61]: [default] [webchat(webchat)] sheenaringo/sheenaringo: 解析这个excel文件，里面的两个sheet是进行自回归分析和mle的估计。它做的是否正确？步骤是什么？如果做ols分析怎么做？ [ComponentType.File]
[2026-04-20 23:05:33.417] [Core] [DBUG] [waking_check.stage:158]: enabled_plugins_name: ['*']
[2026-04-20 23:05:33.423] [Core] [DBUG] [method.star_request:44]: plugin -> session_controller - handle_session_control_agent
[2026-04-20 23:05:33.424] [Core] [DBUG] [method.star_request:44]: plugin -> session_controller - handle_empty_mention
[2026-04-20 23:05:33.424] [Core] [DBUG] [method.star_request:44]: plugin -> astrbot_plugin_apis - on_message
[2026-04-20 23:05:33.425] [Core] [DBUG] [method.star_request:44]: plugin -> astrbot_plugin_music - on_search_song
[2026-04-20 23:05:33.428] [Core] [DBUG] [agent_sub_stages.internal:168]: ready to request llm provider
[2026-04-20 23:05:33.428] [Core] [DBUG] [agent_sub_stages.internal:191]: acquired session lock for llm request
[2026-04-20 23:05:33.661] [Core] [DBUG] [pipeline.context_utils:95]: hook(OnLLMRequestEvent) -> astrbot_plugin_stealer - _inject_emotion_instruction
[2026-04-20 23:05:33.662] [Core] [DBUG] [astrbot_plugin_stealer.main:1033]: [Stealer] LLM模式已启用，跳过提示词注入，将使用轻量模型分析
[2026-04-20 23:05:33.662] [Core] [DBUG] [pipeline.context_utils:95]: hook(OnLLMRequestEvent) -> astrbot - decorate_llm_req
[2026-04-20 23:05:33.663] [Core] [INFO] [respond.stage:183]: Prepare to send - sheenaringo/sheenaringo:
[2026-04-20 23:05:33.663] [Core] [INFO] [respond.stage:199]: 应用流式输出(webchat)
[2026-04-20 23:05:33.663] [Core] [DBUG] [runners.base:64]: Agent state transition: AgentState.IDLE -> AgentState.RUNNING
[2026-04-20 23:05:33.663] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [2] system,user
[2026-04-20 23:05:33.666] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [2] system,user
[2026-04-20 23:05:43.292] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_file_read_tool']
[2026-04-20 23:05:43.304] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_file_read_tool，参数：{'path': 'C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx'}
[2026-04-20 23:05:43.328] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_file_read_tool Result: Error reading file: binary files are not supported by this tool.
[2026-04-20 23:05:43.329] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [4] system,user,assistant,tool
[2026-04-20 23:05:43.329] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [4] system,user,assistant,tool
[2026-04-20 23:05:52.259] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:05:52.259] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': '\nimport pandas as pd\nimport numpy as np\n\n# 读取Excel文件\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\n\n# 查看所有sheet名称\nxl = pd.ExcelFile(file_path)\nprint("Sheet名称:", xl.sheet_names)\nprint()\n\n# 读取每个sheet\nfor sheet in xl.sheet_names:\n print(f"===== {sheet} =====")\n df = pd.read_excel(file_path, sheet_name=sheet)\n print(f"形状: {df.shape}")\n print(df.to_string())\n print("\n")\n'}
[2026-04-20 23:05:57.930] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.
[2026-04-20 23:05:57.932] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [6] system,user,assistant,tool,assistant,tool
[2026-04-20 23:05:57.932] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [6] system,user,assistant,tool,assistant,tool
[2026-04-20 23:06:13.377] [Core] [DBUG] [sources.openai_source:583]: completion: ChatCompletion(id='chatcmpl-69e640bd7ea6697f77a00553', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Excel自回归MLE验证及OLS', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None, reasoning_content='用户要求生成一个标题，基于提供的查询。查询是中文的："解析这个excel文件，里面的两个sheet是进行自回归分析和mle的估计。它做的是否正确？步骤是什么？如果做ols分析怎么做？"\n\n这是一个关于统计分析的问题，具体涉及：\n1. Excel文件解析\n2. 自回归分析（AR）\n3. MLE（最大似然估计）\n4. OLS（普通最小二乘法）分析\n\n核心主题是：Excel文件中的自回归分析与MLE估计验证，以及OLS替代方法。\n\n需要生成一个简洁的标题，不超过10个词，使用相同语言（中文）。\n\n可能的标题：\n- Excel自回归分析与MLE估计验证及OLS方法\n- Excel文件AR分析和MLE估计正确性检查\n- 自回归分析与MLE估计验证及OLS替代方案\n\n检查字数：\n1. Excel自回归分析与MLE估计验证及OLS方法 - 14字，太长\n2. Excel AR分析与MLE估计正确性验证 - 12字，稍长\n3. Excel自回归与MLE估计验证及OLS方法 - 13字\n\n精简：\n- Excel自回归与MLE估计验证及OLS - 11字\n- Excel自回归MLE估计验证与OLS方法 - 11字\n- 自回归分析与MLE估计验证及OLS方法 - 12字\n\n再精简：\n- Excel自回归和MLE估计验证及OLS - 10字（刚好）\n- Excel自回归MLE估计验证与OLS分析 - 11字\n\n或者：\n- Excel自回归与MLE估计验证及OLS替代 - 11字\n\n更简洁：\n- Excel自回归MLE估计验证与OLS - 10字（刚好）\n\n或者：\n- Excel自回归与MLE估计验证及OLS方法 - 12字（超了）\n\n再想想：\n- Excel自回归MLE估计验证及OLS方法 - 11字\n\n可以：\n- Excel自回归MLE估计验证与OLS - 10字\n\n或者：\n- Excel自回归分析与MLE估计验证 - 10字（刚好）\n\n用户还问了"如果做ols分析怎么做"，所以应该包含OLS。\n\n- Excel自回归MLE估计验证与OLS方法 - 11字（超了）\n- Excel自回归MLE验证与OLS分析 - 10字\n\n"验证与"可以改为"及"：\n- Excel自回归MLE估计验证及OLS - 10字\n\n或者：\n- 自回归与MLE估计验证及OLS方法 - 11字\n\n再精简：\n- Excel自回归MLE估计验证及OLS - 10字（可以）\n\n或者更通顺：\n- Excel文件自回归MLE估计验证及OLS - 11字（超了）\n\n去掉"文件"：\n- Excel自回归MLE估计验证及OLS - 10字\n\n或者：\n- 自回归分析与MLE估计验证及OLS - 11字\n\n再精简：\n- 自回归MLE估计验证及OLS方法 - 10字\n\n考虑到用户明确提到"Excel文件"，保留Excel更好。\n\n最终选择：\nExcel自回归MLE估计验证及OLS\n\n或者：\nExcel自回归分析与MLE验证及OLS\n\n字数检查：\nExcel(1)自回归(3)MLE(3)估计(2)验证(2)及(1)OLS(3) = 1+3+3+2+2+1+3 = 15字符？不对，中文按字算。\n\nExcel（4个字符，但通常算1个词？不，题目说"words"，但中文没有word概念，应该是"字"）\n\n用户说"no more than 10 words"，对于中文，通常理解为10个字。\n\nExcel自回归MLE估计验证及OLS：\nE-x-c-e-l（5字符，算1个英文单词？）\n不，在中文语境下，"Excel"算1个词，"自回归"算1个词，"MLE"算1个词，"估计"算1个词，"验证"算1个词，"及"算1个词，"OLS"算1个词。共7个词。\n\n或者按字符数：Excel（5字符）+ 自回归（3）+ MLE（3）+ 估计（2）+ 验证（2）+ 及（1）+ OLS（3）= 19字符。但中文标题通常按字数限制。\n\n看例子："解析这个excel文件..."，用户查询中"excel"是小写。\n\n稳妥起见，控制在10个汉字以内。\n\n方案：\n1. Excel自回归MLE验证与OLS（9字）\n2. 自回归MLE估计验证及OLS（9字）\n3. Excel自回归分析与MLE验证（9字）\n\n用户问的是：\n1. 解析Excel文件\n2. 自回归分析和MLE估计是否正确\n3. 步骤是什么\n4. OLS分析怎么做\n\n核心：Excel自回归分析、MLE估计、OLS方法\n\n标题：Excel自回归MLE估计验证与OLS（10字，刚好）\n\n或者：Excel自回归与MLE估计验证（9字）\n\n但少了OLS。用户明确问了OLS。\n\nExcel自回归MLE验证及OLS方法（11字，超了）\n\nExcel自回归MLE验证与OLS（9字）\n\n这个可以。\n\n或者：Excel自回归分析MLE验证及OLS（10字）\n\n"分析"和"验证"都是关键。\n\n再精简：\nExcel自回归MLE验证及OLS（9字）\n\n这个最简洁且完整。\n\n确认语言：用户查询是中文，所以用中文。\n\n最终输出：Excel自回归MLE验证及OLS'))], created=1776697533, model='kimi-k2.5', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=1157, prompt_tokens=156, total_tokens=1313, completion_tokens_details=None, prompt_tokens_details=None))
[2026-04-20 23:06:13.378] [Core] [INFO] [core.astr_main_agent:1020]: Generated chatui title for session f1a7fd46-7f92-464f-b532-698009c6e226: Excel自回归MLE验证及OLS
[2026-04-20 23:06:14.780] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:06:14.781] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': '\nimport pandas as pd\nimport numpy as np\nimport os\n\n# 检查文件是否存在\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\nprint(f"文件存在: {os.path.exists(file_path)}")\nprint(f"文件路径: {file_path}")\n\ntry:\n # 查看所有sheet名称\n xl = pd.ExcelFile(file_path)\n print("Sheet名称:", xl.sheet_names)\n print()\n\n # 读取每个sheet\n for sheet in xl.sheet_names:\n print(f"===== {sheet} =====")\n df = pd.read_excel(file_path, sheet_name=sheet)\n print(f"形状: {df.shape}")\n print(df.head(30).to_string())\n print("\n")\nexcept Exception as e:\n print(f"错误: {e}")\n'}
[2026-04-20 23:06:16.557] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.
[2026-04-20 23:06:16.558] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [8] system,user,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:06:16.559] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [8] system,user,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:09.968] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_shell']
[2026-04-20 23:07:09.969] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_shell，参数：{'command': 'pip install pandas openpyxl xlrd -q'}
[2026-04-20 23:07:14.786] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_shell Result: {"stdout": "", "stderr": "", "exit_code": 0}
[2026-04-20 23:07:14.787] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [10] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:14.787] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [10] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:22.805] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:07:22.806] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': '\nimport pandas as pd\nimport numpy as np\nimport os\n\n# 检查文件是否存在\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\nprint(f"文件存在: {os.path.exists(file_path)}")\n\n# 列出目录内容\ndir_path = os.path.dirname(file_path)\nif os.path.exists(dir_path):\n print(f"\u76ee录内容: {os.listdir(dir_path)}")\n'}
[2026-04-20 23:07:23.938] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.
[2026-04-20 23:07:23.940] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [12] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:23.940] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [12] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:57.423] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:07:57.423] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': 'print("test")'}
[2026-04-20 23:07:57.503] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: test
[2026-04-20 23:07:57.505] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [14] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:07:57.505] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [14] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:06.235] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:08:06.235] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': 'import os\npath = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments"\nprint(os.listdir(path))'}
[2026-04-20 23:08:06.320] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.

[SYSTEM NOTICE] By the way, you have executed the same tool astrbot_execute_python 3 times consecutively. Double-check whether another tool, different arguments, or a summary would move the task forward better.
[2026-04-20 23:08:06.321] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [16] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:06.321] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [16] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:11.446] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_shell']
[2026-04-20 23:08:11.447] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_shell，参数：{'command': 'dir "C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments"'}
[2026-04-20 23:08:11.477] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_shell Result: {"stdout": " 驱动器 C 中的卷是 Windows-SSD\r\n 卷的序列号是 E619-F9F7\r\n\r\n C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments 的目录\r\n\r\n2026/04/20 23:04

[SYSTEM NOTICE] By the way, you have executed the same tool astrbot_execute_python 3 times consecutively. Double-check whether another tool, different arguments, or a summary would move the task forward better.
[2026-04-20 23:08:41.070] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:41.070] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:51.278] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
[2026-04-20 23:08:51.279] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': 'import pandas as pd\n\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\n\nsheet1 = pd.read_excel(file_path, sheet_name='Sheet1')\nresult = sheet1.to_string()\nprint(result[:3000])'}
[2026-04-20 23:08:53.030] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.

[SYSTEM NOTICE] Important: you have executed the same tool astrbot_execute_python 4 times consecutively. Unless this repetition is clearly necessary, stop repeating the same action and either switch tools, refine parameters, or summarize what is still missing.
[2026-04-20 23:08:53.031] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:53.032] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:08:59.100] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_file_read_tool']
[2026-04-20 23:08:59.100] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_file_read_tool，参数：{'path': 'C:/Users/xuedinge/.astrbot_launcher/instances/d6782f85-6b8b-408d-b7ae-625a6885ba91/core/data/skills/xlsx/SKILL.md'}
[2026-04-20 23:08:59.109] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_file_read_tool Result: ---
name: xlsx
description: "Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved."
license: Proprietary. LICENSE.txt has complete terms

Requirements for Outputs

All Excel files

Professional Font

• Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

Zero Formula Errors

• Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

Preserve Existing Templates (when updating templates)

• Study and EXACTLY match existing format, style, and conventions when modifying files
• Never impose standardized formatting on files with established patterns
• Existing template conventions ALWAYS override these guidelines

Financial models

Color Coding Standards

Unless otherwise stated by the user or existing template

Industry-Standard Color Conventions

• Blue text (RGB: 0,0,255): Hardcoded inputs, and numbers users will change for scenarios
• Black text (RGB: 0,0,0): ALL formulas and calculations
• Green text (RGB: 0,128,0): Links pulling from other worksheets within same workbook
• Red text (RGB: 255,0,0): External links to other files
• Yellow background (RGB: 255,255,0): Key assumptions needing attention or cells that need to be updated

Number Formatting Standards

Required Format Rules

• Years: Format as text strings (e.g., "2024" not "2,024")
• Currency: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)")
• Zeros: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-")
• Percentages: Default to 0.0% format (one decimal)
• Multiples: Format as 0.0x for valuation multiples (EV/EBITDA, P/E)
• Negative numbers: Use parentheses (123) not minus -123

Formula Construction Rules

Assumptions Placement

• Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells
• Use cell references instead of hardcoded values in formulas
• Example: Use =B5*(1+$B$6) instead of =B5*1.05

Formula Error Prevention

• Verify all cell references are correct
• Check for off-by-one errors in ranges
• Ensure consistent formulas across all projection periods
• Test with edge cases (zero values, negative numbers)
• Verify no unintended circular references

Documentation Requirements for Hardcodes

• Comment or in cells beside (if end of table). Format: "Source: [System/Document], [Date], [Specific Reference], [URL if applicable]"
• Examples:
  • "Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]"
  • "Source: Company 10-Q, Q2 2025, Exhibit 99.1, [SEC EDGAR URL]"
  • "Source: Bloomberg Terminal, 8/15/2025, AAPL US Equity"
  • "Source: FactSet, 8/20/2025, Consensus Estimates Screen"

XLSX creation, editing, and analysis

Overview

A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.

Important Requirements

LibreOffice Required for Formula Recalculation: You can assume LibreOffice is installed for recalculating formula values using the scripts/recalc.py script. The script automatically configures LibreOffice on first run, including in sandboxed environments where Unix sockets are restricted (handled by scripts/office/soffice.py)

Reading and analyzing data

Data analysis with pandas

For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities:

import pandas as pd

# Read Excel
df = pd.read_excel('file.xlsx')  # Default: first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # All sheets as dict

# Analyze
df.head()      # Preview data
df.info()      # Column info
df.describe()  # Statistics

# Write Excel
df.to_excel('output.xlsx', index=False)

Excel File Workflows

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.

❌ WRONG - Hardcoding Calculated Values

# Bad: Calculating in Python and hardcoding result
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# Bad: Computing growth rate in Python
growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
sheet['C5'] = growth  # Hardcodes 0.15

# Bad: Python calculation for average
avg = sum(values) / len(values)
sheet['D20'] = avg  # Hardcodes 42.5

✅ CORRECT - Using Excel Formulas

# Good: Let Excel calculate the sum
sheet['B10'] = '=SUM(B2:B9)'

# Good: Growth rate as Excel formula
sheet['C5'] = '=(C4-C2)/C2'

# Good: Average using Excel function
sheet['D20'] = '=AVERAGE(D2:D19)'

This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.

Common Workflow

1. Choose tool: pandas for data, openpyxl for formulas/formatting
2. Create/Load: Create new workbook or load existing file
3. Modify: Add/edit data, formulas, and formatting
4. Save: Write to file
5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the scripts/recalc.py script

   python scripts/recalc.py output.xlsx

6. Verify and fix any errors:
   • The script returns JSON with error details
   • If status is errors_found, check error_summary for specific error types and locations
   • Fix the identified errors and recalculate again
   • Common errors to fix:
     • #REF!: Invalid cell references
     • #DIV/0!: Division by zero
     • #VALUE!: Wrong data type in formula
     • #NAME?: Unrecognized formula name

Creating new Excel files

# Using openpyxl for formulas and formatting
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()
sheet = wb.active

# Add data
sheet['A1'] = 'Hello'
sheet['B1'] = 'World'
sheet.append(['Row', 'of', 'data'])

# Add formula
sheet['B2'] = '=SUM(A1:A10)'

# Formatting
sheet['A1'].font = Font(bold=True, color='FF0000')
sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')
sheet['A1'].alignment = Alignment(horizontal='center')

# Column width
sheet.column_dimensions['A'].width = 20

wb.save('output.xlsx')

Editing existing Excel files

# Using openpyxl to preserve formulas and formatting
from openpyxl import load_workbook

# Load existing file
wb = load_workbook('existing.xlsx')
sheet = wb.active  # or wb['SheetName'] for specific sheet

# Working with multiple sheets
for sheet_name in wb.sheetnames:
    sheet = wb[sheet_name]
    print(f"Sheet: {sheet_name}")

# Modify cells
sheet['A1'] = 'New Value'
sheet.insert_rows(2)  # Insert row at position 2
sheet.delete_cols(3)  # Delete column 3

# Add new sheet
new_sheet = wb.create_sheet('NewSheet')
new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')

Recalculating formulas

Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided scripts/recalc.py script to recalculate formulas:

python scripts/recalc.py <excel_file> [timeout_seconds]

Example:

python scripts/recalc.py output.xlsx 30

The script:

• Automatically sets up LibreOffice macro on first run
• Recalculates all formulas in all sheets
• Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
• Returns JSON with detailed error locations and counts
• Works on both Linux and macOS

Formula Verification Checklist

Quick checks to ensure formulas work correctly:

Essential Verification

• Test 2-3 sample references: Verify they pull correct values before building full model
• Column mapping: Confirm Excel columns match (e.g., column 64 = BL, not BK)
• Row offset: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)

Common Pitfalls

• NaN handling: Check for null values with pd.notna()
• Far-right columns: FY data often in columns 50+
• Multiple matches: Search all occurrences, not just first
• Division by zero: Check denominators before using / in formulas (#DIV/0!)
• Wrong references: Verify all cell references point to intended cells (#REF!)
• Cross-sheet references: Use correct format (Sheet1!A1) for linking sheets

Formula Testing Strategy

• Start small: Test formulas on 2-3 cells before applying broadly
• Verify dependencies: Check all cells referenced in formulas exist
• Test edge cases: Include zero, negative, and very large values

Interpreting scripts/recalc.py Output

The script returns JSON with error details:

{
  "status": "success",           // or "errors_found"
  "total_errors": 0,              // Total error count
  "total_formulas": 42,           // Number of formulas in file
  "error_summary": {              // Only present if errors found
    "#REF!": {
      "count": 2,
      "locations": ["Sheet1!B5", "Sheet1!C10"]
    }
  }
}

Best Practices

Library Selection

• pandas: Best for data analysis, bulk operations, and simple data export
• openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

• Cell indices are 1-based (row=1, column=1 refers to cell A1)
• Use data_only=True to read calculated values: load_workbook('file.xlsx', data_only=True)
• Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost
• For large files: Use read_only=True for reading or write_only=True for writing
• Formulas are preserved but not evaluated - use scripts/recalc.py to update values

Working with pandas

• Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})
• For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])
• Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

Code Style Guidelines

IMPORTANT: When generating Python code for Excel operations:

• Write minimal, concise Python code without unnecessary comments
• Avoid verbose variable names and redundant operations
• Avoid unnecessary print statements

For Excel files themselves:

• Add comments to cells with complex formulas or important assumptions
• Document data sources for hardcoded values
• Include notes for key calculations and model sections
  [2026-04-20 23:08:59.111] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:08:59.111] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:09.134] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
  [2026-04-20 23:09:09.135] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': 'import pandas as pd\nimport numpy as np\nimport sys\n\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\n\n# 读取所有sheet\nall_sheets = pd.read_excel(file_path, sheet_name=None)\n\nfor sheet_name, df in all_sheets.items():\n print(f"\n{'='*60}")\n print(f"Sheet: {sheet_name}")\n print(f"{'='*60}")\n print(f"形状: {df.shape[0]} 行 x {df.shape[1]} 列")\n \n # 输出数据\n for idx, row in df.iterrows():\n row_str = " | ".join([str(x) if pd.notna(x) else "" for x in row])\n print(f"Row {idx}: {row_str}")\n'}
  [2026-04-20 23:09:10.777] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.
  [2026-04-20 23:09:10.778] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:10.778] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:25.407] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_python']
  [2026-04-20 23:09:25.407] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_python，参数：{'code': 'import pandas as pd\n\nfile_path = r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx"\n\n# 读取所有sheet\nall_sheets = pd.read_excel(file_path, sheet_name=None)\n\noutput = []\nfor sheet_name, df in all_sheets.items():\n output.append(f"\n{'='*60}")\n output.append(f"Sheet: {sheet_name}")\n output.append(f"{'='*60}")\n output.append(f"形状: {df.shape[0]} 行 x {df.shape[1]} 列")\n output.append("")\n \n for idx, row in df.iterrows():\n row_data = []\n for x in row:\n if pd.notna(x):\n row_data.append(str(x))\n else:\n row_data.append("")\n output.append(f"Row {idx}: {' | '.join(row_data)}")\n\n# 写入文件\nwith open(r"C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\workspaces\webchat_FriendMessage_webchat_sheenaringo_f1a7fd46-7f92-464f-b532-698009c6e226\excel_content.txt", "w", encoding="utf-8") as f:\n f.write("\n".join(output))\n\nprint("Done! File saved.")\nprint(f"\n".join(output[:50])) # 显示前50行'}
  [2026-04-20 23:09:27.007] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_python Result: No output.
  [2026-04-20 23:09:27.009] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:27.009] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:34.582] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_shell']
  [2026-04-20 23:09:34.583] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_shell，参数：{'command': 'cd /d "C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\workspaces\webchat_FriendMessage_webchat_sheenaringo_f1a7fd46-7f92-464f-b532-698009c6e226" && python -c "import pandas as pd; df = pd.read_excel(r'C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx', sheet_name='Sheet1'); print(df.shape); print(df.head(20).to_string())"'}
  [2026-04-20 23:09:36.265] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_shell Result: {"stdout": "(100, 11)\r\n 收益率 (%) 收益率减均值 残差平方 方差 似然 Unnamed: 5 Unnamed: 6 mu omega alpha beta\r\n0 0.32 0.32 0.1024 6.561633e-01 -0.786295 NaN NaN 3.427083e-11 38432.817658 38432.817658 38432.817658\r\n1 -0.57 -0.57 0.3249 6.758654e+04 -6.479523 NaN NaN NaN NaN NaN NaN\r\n2 1.23 1.23 1.5129 2.597592e+09 -11.757864 NaN NaN NaN NaN NaN NaN\r\n3 -0.91 -0.91 0.8281 9.983279e+13 -17.036197 NaN -0.786295 NaN NaN NaN NaN\r\n4 0.68 0.68 0.4624 3.836855e+18 -22.314531 NaN NaN NaN NaN NaN NaN\r\n5 -0.24 -0.24 0.0576 1.474612e+23 -27.592864 NaN NaN NaN NaN NaN NaN\r\n6 1.05 1.05 1.1025 5.667348e+27 -32.871198 NaN NaN NaN NaN NaN NaN\r\n7 -1.32 -1.32 1.7424 2.178121e+32 -38.149531 NaN NaN NaN NaN NaN NaN\r\n8 0.47 0.47 0.2209 8.371134e+36 -43.427865 NaN NaN NaN NaN NaN NaN\r\n9 -0.78 -0.78 0.6084 3.217263e+41 -48.706198 NaN NaN NaN NaN NaN NaN\r\n10 0.94 0.94 0.8836 1.236485e+46 -53.984532 NaN NaN NaN NaN NaN NaN\r\n11 -1.11 -1.11 1.2321 4.752159e+50 -59.262865 NaN NaN NaN NaN NaN NaN\r\n12 0.16 0.16 0.0256 1.826389e+55 -64.541199 NaN NaN NaN NaN NaN NaN\r\n13 0.83 0.83 0.6889 7.019326e+59 -69.819532 NaN NaN NaN NaN NaN NaN\r\n14 -0.49 -0.49 0.2401 2.697725e+64 -75.097866 NaN NaN NaN NaN NaN NaN\r\n15 1.28 1.28 1.6384 1.036812e+69 -80.376199 NaN NaN NaN NaN NaN NaN\r\n16 -0.63 -0.63 0.3969 3.984760e+73 -85.654533 NaN NaN NaN NaN NaN NaN\r\n17 0.39 0.39 0.1521 1.531455e+78 -90.932866 NaN NaN NaN NaN NaN NaN\r\n18 -0.95 -0.95 0.9025 5.885814e+82 -96.211200 NaN NaN NaN NaN NaN NaN\r\n19 0.72 0.72 0.5184 2.262084e+87 -101.489533 NaN NaN NaN NaN NaN NaN\r\n", "stderr": "", "exit_code": 0}
  [2026-04-20 23:09:36.266] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:36.266] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:45.096] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_shell']
  [2026-04-20 23:09:45.096] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_shell，参数：{'command': 'cd /d "C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\workspaces\webchat_FriendMessage_webchat_sheenaringo_f1a7fd46-7f92-464f-b532-698009c6e226" && python -c "import pandas as pd; df = pd.read_excel(r'C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx', sheet_name='Sheet2'); print(df.shape); print(df.to_string())"'}
  [2026-04-20 23:09:46.873] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_shell Result: {"stdout": "(101, 10)\r\n 0 收益率 Unnamed: 2 n 100 q 2 一期方差 二期收益率 二期方差\r\n0 0.0032 0.0032 NaN corr -7.925462e-01 m 394.020000 8.976016e-06 NaN NaN\r\n1 -0.0025 -0.0057 0.0032 NaN -7.925462e+00 NaN NaN 3.485722e-05 -0.0025 8.456464e-06\r\n2 0.0098 0.0123 -0.0057 p 2.220446e-15 NaN NaN 1.463132e-04 0.0066 3.834086e-05\r\n3 0.0007 -0.0091 0.0123 NaN NaN NaN NaN 8.656442e-05 0.0032 7.795264e-06\r\n4 0.0075 0.0068 -0.0091 NaN NaN mu-hat 0.000204 4.350722e-05 -0.0023 7.333264e-06\r\n5 0.0051 -0.0024 0.0068 NaN NaN 单期方差 0.000065 6.780816e-06 0.0044 1.593606e-05\r\n6 0.0156 0.0105 -0.0024 NaN NaN 二期方差 0.000007 1.060076e-04 0.0081 5.916686e-05\r\n7 0.0024 -0.0132 0.0105 NaN NaN NaN -0.895275 1.796672e-04 -0.0027 9.659664e-06\r\n8 0.0071 0.0047 -0.0132 NaN NaN NaN NaN 2.021402e-05 -0.0085 7.935246e-05\r\n9 -0.0007 -0.0078 0.0047 NaN NaN NaN NaN 6.406402e-05 -0.0031 1.230606e-05\r\n10 0.0087 0.0094 -0.0078 NaN NaN NaN NaN 8.456642e-05 0.0016 1.420864e-06\r\n11 -0.0024 -0.0111 0.0094 NaN NaN NaN NaN 1.277804e-04 -0.0017 4.443664e-06\r\n12 -0.0008 0.0016 -0.0111 NaN NaN NaN NaN 1.948816e-06 -0.0095 9.816846e-05\r\n13 0.0075 0.0083 0.0016 NaN NaN NaN NaN 6.554522e-05 0.0099 9.009806e-05\r\n14 0.0026 -0.0049 0.0083 NaN NaN NaN NaN 2.605082e-05 0.0034 8.952064e-06\r\n15 0.0154 0.0128 -0.0049 NaN NaN NaN NaN 1.586592e-04 0.0079 5.613006e-05\r\n16 0.0091 -0.0063 0.0128 NaN NaN NaN NaN 4.230202e-05 0.0065 3.711246e-05\r\n17 0.0130 0.0039 -0.0063 NaN NaN NaN NaN 1.366042e-05 -0.0024 7.884864e-06\r\n18 0.0035 -0.0095 0.0039 NaN NaN NaN NaN 9.416762e-05 -0.0056 3.609606e-05\r\n19 0.0107 0.0072 -0.0095 NaN NaN NaN NaN 4.894402e-05 -0.0023 7.333264e-06\r\n20 -0.0011 -0.0118 0.0072 NaN NaN NaN NaN 1.440960e-04 -0.0046 2.508006e-05\r\n21 0.0043 0.0054 -0.0118 NaN NaN NaN NaN 2.699842e-05 -0.0064 4.634886e-05\r\n22 0.0036 -0.0007 0.0054 NaN NaN NaN NaN 8.172160e-07 0.0047 1.842126e-05\r\n23 0.0150 0.0114 -0.0007 NaN NaN NaN NaN 1.253504e-04 0.0107 1.059253e-04\r\n24 0.0064 -0.0086 0.0114 NaN NaN NaN NaN 7.751042e-05 0.0028 5.721664e-06\r\n25 0.0107 0.0043 -0.0086 NaN NaN NaN NaN 1.677722e-05 -0.0043 2.216526e-05\r\n26 -0.0018 -0.0125 0.0043 NaN NaN NaN NaN 1.613916e-04 -0.0082 7.409766e-05\r\n27 0.0043 0.0061 -0.0125 NaN NaN NaN NaN 3.476282e-05 -0.0064 4.634886e-05\r\n28 0.0030 -0.0013 0.0061 NaN NaN NaN NaN 2.262016e-06 0.0048 1.928966e-05\r\n29 0.0127 0.0097 -0.0013 NaN NaN NaN NaN 9.017402e-05 0.0084 6.387206e-05\r\n30 0.0056 -0.0071 0.0097 NaN NaN NaN NaN 5.334842e-05 0.0026 4.804864e-06\r\n31 0.0158 0.0102 -0.0071 NaN NaN NaN NaN 9.992002e-05 0.0031 7.246864e-06\r\n32 0.0140 -0.0018 0.0102 NaN NaN NaN NaN 4.016016e-06 0.0084 6.387206e-05\r\n33 0.0166 0.0026 -0.0018 NaN NaN NaN NaN 5.740816e-06 0.0008 1.536640e-07\r\n34 0.0062 -0.0104 0.0026 NaN NaN NaN NaN 1.124448e-04 -0.0078 6.737126e-05\r\n35 0.0139 0.0077 -0.0104 NaN NaN NaN NaN 5.619002e-05 -0.0027 9.659664e-06\r\n36 0.0084 -0.0055 0.0077 NaN NaN NaN NaN 3.253562e-05 0.0022 3.211264e-06\r\n37 0.0203 0.0119 -0.0055 NaN NaN NaN NaN 1.367964e-04 0.0064 3.590406e-05\r\n38 0.0161 -0.0042 0.0119 NaN NaN NaN NaN 1.939522e-05 0.0077 5.317326e-05\r\n39 0.0249 0.0088 -0.0042 NaN NaN NaN NaN 7.389122e-05 0.0046 1.757286e-05\r\n40 0.0140 -0.0109 0.0088 NaN NaN NaN NaN 1.232988e-04 -0.0021 6.290064e-06\r\n41 0.0175 0.0035 -0.0109 NaN NaN NaN NaN 1.086362e-05 -0.0074 6.096486e-05\r\n42 0.0110 -0.0065 0.0035 NaN NaN NaN NaN 4.494362e-05 -0.0030 1.161446e-05\r\n43 0.0221 0.0111 -0.0065 NaN NaN NaN NaN 1.187228e-04 0.0046 1.757286e-05\r\n44 0.0140 -0.0081 0.0111 NaN NaN NaN NaN 6.895642e-05 0.0030 6.718464e-06\r\n45 0.0190 0.0050 -0.0081 NaN NaN NaN NaN 2.300162e-05 -0.0031 1.230606e-05\r\n46 0.0098 -0.0092 0.0050 NaN NaN NaN NaN 8.843522e-05 -0.0042 2.123366e-05\r\n47 0.0171 0.0073 -0.0092 NaN NaN NaN NaN 5.035322e-05 -0.0019 5.326864e-06\r\n48 0.0056 -0.0115 0.0073 NaN NaN NaN NaN 1.369836e-04 -0.0042 2.123366e-05\r\n49 0.0115 0.0059 -0.0115 NaN NaN NaN NaN 3.244442e-05 -0.0056 3.609606e-05\r\n50 0.0087 -0.0028 0.0059 NaN NaN NaN NaN 9.024016e-06 0.0031 7.246864e-06\r\n51 0.0194 0.0107 -0.0028 NaN NaN NaN NaN 1.101660e-04 0.0079 5.613006e-05\r\n52 0.0119 -0.0075 0.0107 NaN NaN NaN NaN 5.935162e-05 0.0032 7.795264e-06\r\n53 0.0160 0.0041 -0.0075 NaN NaN NaN NaN 1.517882e-05 -0.0034 1.450086e-05\r\n54 0.0061 -0.0099 0.0041 NaN NaN NaN NaN 1.020908e-04 -0.0058 3.853926e-05\r\n55 0.0128 0.0067 -0.0099 NaN NaN NaN NaN 4.219802e-05 -0.0032 1.301766e-05\r\n56 0.0006 -0.0122 0.0067 NaN NaN NaN NaN 1.538592e-04 -0.0055 3.490446e-05\r\n57 0.0052 0.0046 -0.0122 NaN NaN NaN NaN 1.932482e-05 -0.0076 6.412806e-05\r\n58 0.0038 -0.0014 0.0046 NaN NaN NaN NaN 2.572816e-06 0.0032 7.795264e-06\r\n59 0.0122 0.0084 -0.0014 NaN NaN NaN NaN 6.717442e-05 0.0070 4.345446e-05\r\n60 0.0021 -0.0101 0.0084 NaN NaN NaN NaN 1.061724e-04 -0.0017 4.443664e-06\r\n61 0.0059 0.0038 -0.0101 NaN NaN NaN NaN 1.293122e-05 -0.0063 4.499726e-05\r\n62 -0.0001 -0.0060 0.0038 NaN NaN NaN NaN 3.848962e-05 -0.0022 6.801664e-06\r\n63 0.0117 0.0118 -0.0060 NaN NaN NaN NaN 1.344672e-04 0.0058 2.907366e-05\r\n64 0.0029 -0.0088 0.0118 NaN NaN NaN NaN 8.107202e-05 0.0030 6.718464e-06\r\n65 0.0084 0.0055 -0.0088 NaN NaN NaN NaN 2.804762e-05 -0.0033 1.374926e-05\r\n66 0.0053 -0.0031 0.0055 NaN NaN NaN NaN 1.091642e-05 0.0024 3.968064e-06\r\n67 0.0148 0.0095 -0.0031 NaN NaN NaN NaN 8.641562e-05 0.0064 3.590406e-05\r\n68 0.0041 -0.0107 0.0095 NaN NaN NaN NaN 1.188972e-04 -0.0012 2.585664e-06\r\n69 0.0104 0.0063 -0.0107 NaN NaN NaN NaN 3.716122e-05 -0.0044 2.311686e-05\r\n70 0.0060 -0.0044 0.0063 NaN NaN NaN NaN 2.119682e-05 0.0019 2.226064e-06\r\n71 0.0170 0.0110 -0.0044 NaN NaN NaN NaN 1.165536e-04 0.0066 3.834086e-05\r\n72 0.0118 -0.0052 0.0110 NaN NaN NaN NaN 2.920322e-05 0.0058 2.907366e-05\r\n73 0.0149 0.0031 -0.0052 NaN NaN NaN NaN 8.386816e-06 -0.0021 6.290064e-06\r\n74 0.0036 -0.0113 0.0031 NaN NaN NaN NaN 1.323420e-04 -0.0082 7.409766e-05\r\n75 0.0115 0.0079 -0.0113 NaN NaN NaN NaN 5.922842e-05 -0.0034 1.450086e-05\r\n76 0.0046 -0.0069 0.0079 NaN NaN NaN NaN 5.046682e-05 0.0010 3.504640e-07\r\n77 0.0170 0.0124 -0.0069 NaN NaN NaN NaN 1.487424e-04 0.0055 2.592846e-05\r\n78 0.0133 -0.0037 0.0124 NaN NaN NaN NaN 1.524122e-05 0.0087 6.875726e-05\r\n79 0.0224 0.0091 -0.0037 NaN NaN NaN NaN 7.913882e-05 0.0054 2.492006e-05\r\n80 0.0128 -0.0096 0.0091 NaN NaN NaN NaN 9.611842e-05 -0.0005 8.244640e-07\r\n81 0.0177 0.0049 -0.0096 NaN NaN NaN NaN 2.205242e-05 -0.0047 2.609166e-05\r\n82 0.0094 -0.0083 0.0049 NaN NaN NaN NaN 7.231802e-05 -0.0034 1.450086e-05\r\n83 0.0198 0.0104 -0.0083 NaN NaN NaN NaN 1.039584e-04 0.0021 2.862864e-06\r\n84 0.0136 -0.0062 0.0104 NaN NaN NaN NaN 4.101122e-05 0.0042 1.437926e-05\r\n85 0.0172 0.0036 -0.0062 NaN NaN NaN NaN 1.153282e-05 -0.0026 9.048064e-06\r\n86 0.0052 -0.0120 0.0036 NaN NaN NaN NaN 1.489376e-04 -0.0084 7.758086e-05\r\n87 0.0126 0.0074 -0.0120 NaN NaN NaN NaN 5.178242e-05 -0.0046 2.508006e-05\r\n88 0.0099 -0.0027 0.0074 NaN NaN NaN NaN 8.433216e-06 0.0047 1.842126e-05\r\n89 0.0199 0.0100 -0.0027 NaN NaN NaN NaN 9.596162e-05 0.0073 4.749966e-05\r\n90 0.0109 -0.0090 0.0100 NaN NaN NaN NaN 8.471362e-05 0.0010 3.504640e-07\r\n91 0.0169 0.0060 -0.0090 NaN NaN NaN NaN 3.359362e-05 -0.0030 1.161446e-05\r\n92 0.0124 -0.0045 0.0060 NaN NaN NaN NaN 2.212762e-05 0.0015 1.192464e-06\r\n93 0.0237 0.0113 -0.0045 NaN NaN NaN NaN 1.231212e-04 0.0068 4.085766e-05\r\n94 0.0161 -0.0076 0.0113 NaN NaN NaN NaN 6.090242e-05 0.0037 1.083726e-05\r\n95 0.0214 0.0053 -0.0076 NaN NaN NaN NaN 2.596922e-05 -0.0023 7.333264e-06\r\n96 0.0097 -0.0117 0.0053 NaN NaN NaN NaN 1.417052e-04 -0.0064 4.634886e-05\r\n97 0.0165 0.0068 -0.0117 NaN NaN NaN NaN 4.350722e-05 -0.0049 2.817486e-05\r\n98 0.0154 -0.0011 0.0068 NaN NaN NaN NaN 1.700416e-06 0.0057 2.800526e-05\r\n99 0.0236 0.0082 -0.0011 NaN NaN NaN NaN 6.393602e-05 0.0071 4.478286e-05\r\n100 NaN NaN 0.0082 NaN NaN NaN NaN 4.161600e-08 NaN 1.664640e-07\r\n", "stderr": "", "exit_code": 0}
  [2026-04-20 23:09:46.875] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:09:46.876] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
  [2026-04-20 23:10:08.293] [Core] [INFO] [runners.tool_loop_agent_runner:883]: Agent 使用工具: ['astrbot_execute_shell']
  [2026-04-20 23:10:08.294] [Core] [INFO] [runners.tool_loop_agent_runner:930]: 使用工具：astrbot_execute_shell，参数：{'command': 'cd /d "C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\workspaces\webchat_FriendMessage_webchat_sheenaringo_f1a7fd46-7f92-464f-b532-698009c6e226" && python -c "import pandas as pd; df = pd.read_excel(r'C:\Users\xuedinge\.astrbot_launcher\instances\d6782f85-6b8b-408d-b7ae-625a6885ba91\core\data\attachments\收益率与方差分析_2期数据.xlsx', sheet_name='Sheet1'); print(df.tail(10).to_string())"'}
  [2026-04-20 23:10:09.896] [Core] [INFO] [runners.tool_loop_agent_runner:1128]: Tool astrbot_execute_shell Result: {"stdout": " 收益率 (%) 收益率减均值 残差平方 方差 似然 Unnamed: 5 Unnamed: 6 mu omega alpha beta\r\n90 -0.90 -0.90 0.8100 NaN NaN NaN NaN NaN NaN NaN NaN\r\n91 0.60 0.60 0.3600 NaN NaN NaN NaN NaN NaN NaN NaN\r\n92 -0.45 -0.45 0.2025 NaN NaN NaN NaN NaN NaN NaN NaN\r\n93 1.13 1.13 1.2769 NaN NaN NaN NaN NaN NaN NaN NaN\r\n94 -0.76 -0.76 0.5776 NaN NaN NaN NaN NaN NaN NaN NaN\r\n95 0.53 0.53 0.2809 NaN NaN NaN NaN NaN NaN NaN NaN\r\n96 -1.17 -1.17 1.3689 NaN NaN NaN NaN NaN NaN NaN NaN\r\n97 0.68 0.68 0.4624 NaN NaN NaN NaN NaN NaN NaN NaN\r\n98 -0.21 -0.21 0.0441 NaN NaN NaN NaN NaN NaN NaN NaN\r\n99 0.82 0.82 0.6724 NaN NaN NaN NaN NaN NaN NaN NaN\r\n", "stderr": "", "exit_code": 0}

[SYSTEM NOTICE] By the way, you have executed the same tool astrbot_execute_shell 3 times consecutively. Double-check whether another tool, different arguments, or a summary would move the task forward better.
[2026-04-20 23:10:09.897] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [BefCompact] RunCtx.messages -> [20] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:10:09.898] [Core] [DBUG] [runners.tool_loop_agent_runner:539]: [AftCompact] RunCtx.messages -> [18] system,user,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool,assistant,tool
[2026-04-20 23:10:57.853] [Core] [DBUG] [runners.base:64]: Agent state transition: AgentState.RUNNING -> AgentState.DONE
[2026-04-20 23:10:57.854] [Core] [DBUG] [pipeline.context_utils:95]: hook(OnLLMResponseEvent) -> astrbot - record_llm_resp_to_ltm
[2026-04-20 23:10:57.986] [Core] [DBUG] [result_decorate.stage:165]: hook(on_decorating_result) -> astrbot_plugin_stealer - _prepare_emoji_response
[2026-04-20 23:10:57.986] [Core] [WARN] [v4.23.1] [result_decorate.stage:169]: 启用流式输出时，依赖发送消息前事件钩子的插件可能无法正常工作
[2026-04-20 23:10:57.986] [Core] [DBUG] [astrbot_plugin_stealer.main:1091]: [Stealer] 非 LLM 回复，跳过表情包处理
[2026-04-20 23:10:57.987] [Core] [INFO] [result_decorate.stage:189]: 流式输出已启用，跳过结果装饰阶段
[2026-04-20 23:10:58.040] [Core] [DBUG] [pipeline.scheduler:93]: pipeline 执行完毕。

Are you willing to submit a PR? / 你愿意提交 PR 吗？

• Yes!

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• I have read and agree to abide by the project's Code of Conduct。