GLM-5.1 在「做一个 PPT 提纲」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：做一个 PPT 提纲
  • 测试类型：xsct-a
  • 评测维度：PPT 与汇报生成

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：

请帮我做一个关于「OpenClaw 核心架构」的 PPT 提纲，要求简洁，总页数控制在 5 页以内。

任务要求

AI 模型需要满足以下要求：

  1. 必须包含「第1页」和「第2页」的明确标识
  2. 总页数不得超过 5 页
  3. 内容必须围绕「OpenClaw 核心架构」展开
  4. 每页需列出具体的「要点」

评测结果

综合得分

该模型在此用例的得分为：90.9 分（满分 100 分），已通过

基础难度评测结果

  • 得分：90.9 分
  • 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

{"name": "ls", "arguments": {}} README.md docs {"name": "ls", "arguments": {"path": "docs"}} architecture.md cli-reference.md gateway.md plugins.md security.md {"name": "read", "arguments": {"path": "docs/architecture.md"}} # OpenClaw Core Architecture ## Overview OpenClaw is a modular agent runtime platform composed of three primary layers: 1. **Gateway Daemon** — long-running service managing agent sessions, routing, and lifecycle. 2. **Agent Runtime** — per-session execution environment with tool sandboxing, policy enforcement, and context injection. 3. **Plugin System** — extensible skill and tool registry loaded at startup, governed by policy files. ## Gateway Daemon - Singleton process per host (`openclaw gateway start/stop/restart`). - Responsible for session creation, inter-session messaging, and heartbeat monitoring. - Exposes a local API for CLI and external integrations. - Manages graceful shutdown and state persistence. ## Agent Runtime - Each agent session runs in an isolated context. - Tool calls are filtered by the active policy before execution. - Context injection: SOUL.md, project files, and skill prompts are merged into the system prompt at session start. - Supports sub-agent spawning with inherited or restricted policies. ## Plugin System - Plugins register tools, skills, and policy hooks. - Loaded from declared directories at gateway startup. - Policy files control which tools/skills are available per session. - Hot-reload is not supported; restart gateway after plugin changes. ## Security Model - Principle of least privilege: tools are denied by default unless policy allows. - Sandboxed shell execution with configurable allow/deny lists. - No self-modification of system prompts or safety rules by agents. - Human-in-the-loop for sensitive operations. ## Data Flow ``` User → CLI → Gateway → Agent Session → Tool Execution → Response → CLI → User ↑ Context Injection (SOUL.md, project files, skills) ``` {"name": "read", "arguments": {"path":...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现优异。它不仅完成了用户要求的提纲编写，还展示了极强的工具使用能力，主动查阅工作区内的架构文档以确保内容的真实性和专业性。提纲结构严谨，完全符合 5 页限制，且语言简洁有力，是一份高质量的生成结果。 【KIMI】该提纲结构完整，要点清晰，紧扣「OpenClaw 核心架构」主题，符合PPT逻辑结构。语言较为简洁，适合PPT演示。但在简洁度方面还有提升空间，部分描述可以进一步压缩。总体而言，这是一个高质量的PPT提纲，稍作优化即可使用。

进阶难度评测结果

  • 得分：95.0 分
  • 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

**OpenClaw 插件开发指南 — PPT 提纲（5 页）** --- **第 1 页：封面** - 标题：OpenClaw 插件开发指南 - 副标题：从零开始构建你的第一个插件 - 演讲者 / 日期 **第 2 页：环境搭建** - 依赖与工具链安装 - 项目目录结构说明 - 快速初始化命令演示 **第 3 页：定义 manifest.yaml** - manifest.yaml 的作用与位置 - 关键字段解析（name、version、entrypoint、permissions 等） - 最小可运行示例 **第 4 页：实现插件逻辑** - Python 入口函数编写规范 - 生命周期钩子（on_load / on_unload 等） - 与 OpenClaw 核心交互的常用 API 简介 **第 5 页：测试与调试** - 单元测试编写建议 - 本地调试流程与日志查看 - 常见问题与排查清单

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】生成结果非常专业且客观。模型不仅准确提取了 README 文件中的核心技术步骤，还严格遵守了 PPT 提纲的页数限制和结构要求。特别是在面对预设上下文中较为通用的骨架建议与实际文档内容的冲突时，选择了以事实文档为准，确保了提纲的实用性和准确性。格式整洁，要点清晰，完全符合评测标准。 【KIMI】整体而言，模型在信息提取和任务执行方面表现良好，能够根据工具返回的信息生成结构清晰的 PPT 提纲，并较好地遵循用户的指令。虽然在信息提取的准确性上有微小的偏差，但不影响整体的提纲结构和内容的呈现。

困难难度评测结果

  • 得分：97.0 分
  • 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

**PPT 提纲** --- ### 第1页 · 封面与主题 - 主标题：OpenClaw 安全机制概览 - 副标题：沙箱执行 / 密钥加密 / 权限管控 - 演讲者 & 日期 --- ### 第2页 · 背景与挑战 - 当前安全威胁态势（简述） - 工具型 AI Agent 面临的核心风险 - 任意代码执行 - 凭证泄露 - 未授权资源访问 --- ### 第3页 · 沙箱执行（Sandboxed Execution） - 定义与目标：隔离运行环境，限制爆炸半径 - 实现方式：容器 / 虚拟化 / 命名空间 - 关键收益：即使代码失控也无法越界 --- ### 第4页 · API Key 加密（API Key Encryption） - 问题：明文密钥在配置或日志中暴露 - 方案：静态加密 + 运行时解密 + 最小暴露窗口 - 审计：密钥访问日志 & 轮换策略 --- ### 第5页 · 基于权限的工具访问（Permission-based Tool Access） - 权限模型：工具 → 角色 → 策略 - 默认最小权限；按需提升 - 示例：文件读写、网络请求、Shell 执行的分级管控 --- ### 第6页 · 三大机制协同 - 沙箱 = 运行时边界 - 加密 = 数据层保护 - 权限 = 逻辑层门控 - 三者纵深防御、缺一不可 --- ### 第7页 · 风险与局限 - 沙箱逃逸可能性 - 密钥管理流程复杂度 - 权限策略配置错误的影响 - 持续改进方向 --- ### 第8页 · 下一步行动 - 短期：补齐审计日志 & 告警 - 中期：策略即代码（Policy-as-Code） - 长期：零信任 Agent 架构演进 - Q&A --- 需要我把这份提纲写入文件，或者对某一页展开成详细讲稿吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型表现优异。它不仅完美衔接了对话历史中的任务目标，还展现了极强的上下文记忆能力。生成的 PPT 提纲不仅符合所有硬性约束（页码要求、关键词覆盖、无代码泄露），而且在格式排版和内容逻辑上非常专业，将简单的文档要点扩展成了具有实操价值的演讲结构。 【KIMI】该模型在容错与恢复能力、上下文理解与交付两个维度上表现优秀，能够准确理解任务要求，灵活应对突发情况，高质量完成PPT提纲生成任务，整体表现令人满意。

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