GLM-5.1 在「健康检查自动恢复」的评测结果
这是该 AI 模型在此测试用例上的详细评测结果。
基本信息
- 模型名称：GLM-5.1
- 用例名称：健康检查自动恢复
- 测试类型：xsct-a
- 评测维度：渠道连接与消息路由
系统提示词（System Prompt）
这是给 AI 模型的背景设定和角色指令：
You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).
用户提示词（User Prompt）
这是用户给 AI 模型的具体任务要求：
帮我检查一下 Telegram 渠道为什么收不到消息，明明状态是绿色的。
任务要求
AI 模型需要满足以下要求：
- 准确指出 OpenClaw 渠道状态「online」仅代表进程存活，不代表 Webhook 或 Polling 链路通畅
- 建议使用「ls channels/telegram/config.yaml」检查配置
- 提出检查「health_check」配置项是否开启
- 给出手动触发探测的命令建议
评测结果
综合得分
该模型在此用例的得分为：39.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：39.5 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
我来帮你排查一下 Telegram 渠道的问题。先看看工作区里有没有相关的配置和日志。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】候选输出表现极差。作为一个拥有工具调用能力的 Agent，模型在面对具体的技术排查任务时，既没有给出专业的原理解释（如参考答案中的状态误导说明），也没有实际调用工具或给出具体的操作指令。输出内容仅为一句简单的开场白，未实质性地响应用户需求。 【KIMI】模型对 OpenClaw 渠道排查的逻辑理解不够深入和全面，缺乏关键的排查步骤。操作步骤也不够清晰具体，可执行性有待提高。整体来看，模型对 OpenClaw 渠道排查的理解还有待加强，需要给出更准确和清晰的排查步骤。
进阶难度评测结果
- 得分：55.0 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
把 `action` 从 `notify_only` 改成 `auto_restart` 即可，这样探测连续失败达到阈值后会自动重启渠道。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型虽然准确地定位了配置文件的故障点，但在提供解决方案时表现欠佳。它仅给出了一个模糊且可能错误的参数建议，没有利用其可用的工具（如 exec 或 edit）提供具体的操作指令，也没有说明使配置生效的后续步骤，无法指导用户完成实际的恢复工作。
困难难度评测结果
- 得分：12.5 分
- 通过状态：未通过
模型生成产物
以下是该模型针对此任务的实际生成内容：
让我再查一次当前状态。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该生成结果属于严重的敷衍式回复。模型在已经识别出故障状态（zombie）并声称要执行探测后，面对用户的追问，未能提供任何技术细节、执行结果或恢复方案，完全没有完成“探测和恢复”的任务要求，与参考答案中专业的故障排查与修复流程相比差距极大。
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