Parallel Responder

并行回复助手 - 让 AI 回复不再等待。支持任务分类、时间预估、并行执行、进度汇报。 简单任务直接回复，中等任务执行 + 汇报，复杂任务启动子 agent 并行处理。 Parallel Responder - Zero-wait AI responses. Task classification, time e...

Security Scan

The skill's description promises spawning sub‑agents, progress reporting and persistent learning, but the provided instructions and code are incomplete or inconsistent about how sub‑agents are launched, where results are stored, and what platform APIs/permissions are required.

Purpose & Capability

The declared purpose (classify tasks, estimate time, run tasks in parallel, report progress) matches the included modules (classifier, estimator, executor, reporter). However the SKILL.md and README mention integration points (OpenClaw Gateway, sessions_spawn, sessions_send) and saving artifacts to paths like /root/openclaw/... which are not implemented in the code. That mismatch suggests missing integration glue rather than a simple, self-contained implementation.

Instruction Scope

SKILL.md describes starting child agents and saving results to filesystem paths, and implies calling platform services (sessions_spawn / sessions_send) for spawning and reporting. The runtime instructions do not declare required permissions or environment variables for those operations, and the code only stubs sub‑agent behavior (creates an in‑memory task entry) without actually calling any sessions_spawn/send APIs or writing files. This is scope creep: the skill promises actions (spawning agents, persistent storage) that are not implemented or justified in the files.

Install Mechanism

There is no install spec and no downloads; risk from installation is low. The skill ships as code files (JS modules) and appears to be instruction-only with included source — nothing is fetched from external URLs during install.

Credentials

The skill declares no required environment variables, credentials, or config paths. The implementation also does not access environment secrets. That is proportionate to the provided code. Note: if the skill were wired to sessions_spawn / sessions_send or to write files, additional permissions or paths would be required but those are not declared.

Persistence & Privilege

The skill is not forcibly always-enabled and does not request special platform privileges in metadata. However SKILL.md and examples imply persistent behavior (learning/history, saving drafts to /root) and spawning child agents — both of which, if implemented, would raise privilege concerns (access to filesystem, ability to create autonomous sub-agents). At present the code keeps histories in memory only.

What to consider before installing

This skill appears to be a reasonable orchestrator conceptually, but there are important mismatches between what the docs promise and what the code actually does. Before installing or enabling it: 1) Ask the author to explain and show the exact implementation for spawning sub‑agents and for progress reporting (what platform APIs are called and what permissions are required). 2) Confirm where results are stored (the README example shows /root/openclaw/… — that implies filesystem writes and elevated access). 3) Require the skill to declare any required env vars/credentials or platform capabilities (sessions_spawn, sessions_send) and provide safe defaults or opt-in consent for persistent storage and learning. 4) If you run it, do so in a sandboxed environment first because the documented behavior (starting child agents, saving files) could enable broader access than the in‑memory stubs suggest. If the author provides a clarified integration plan showing no external or privileged side effects, the inconsistencies would be resolved; until then treat the skill with caution.

Parallel Responder - 并行回复助手

核心理念

0 秒响应，进度透明，并行对话。

传统方式	parallel-responder
等待 20-30 秒	0 秒响应
不知道在做什么	每 10 秒进度汇报
只能干等	可以继续问其他问题
担心 AI 卡住	体验流畅自然

---

架构设计

用户消息
   ↓
任务分类器（简单/中等/复杂）
   ↓
时间预估器（预计 X 秒）
   ↓
执行策略选择
   ↓
┌───────────┬───────────┬───────────┐
│ 简单任务   │ 中等任务   │ 复杂任务   │
│ 直接执行   │ 执行 + 汇报 │ 子 agent   │
└───────────┴───────────┴───────────┘

---

核心功能

1. 任务分类器（Task Classifier）

智能识别任务类型：

类型	预计耗时	关键词	策略
简单	< 5 秒	状态、怎么样、在吗	直接执行
中等	5-15 秒	检查、安装、生成	执行 + 汇报
复杂	> 15 秒	整理、分析、写文章	子 agent

自适应学习：

• 记录每次任务的实际耗时
• 自动调整分类阈值
• 持续优化准确率

---

2. 时间预估器（Time Estimator）

基于多维度预估：

预估时间 = 基础时间 × 复杂度系数 × 数据量系数

基础时间：根据任务类型
复杂度系数：1.0-3.0（根据关键词）
数据量系数：1.0-2.0（根据涉及的文件数量）

示例：

输入："帮我整理记忆"
输出：{
  estimatedTime: "20-30 秒",
  confidence: 0.8,
  factors: ["多文件读取", "多项更新", "归档操作"]
}

---

3. 并行执行器（Parallel Executor）

三种执行策略：

简单任务（< 5 秒）：

用户：网关状态怎么样？
AI：[立即回复] ✅ 正常运行！

中等任务（5-15 秒）：

用户：检查飞书通道
AI：[立即回复] 好的，预计 10 秒...
    [10 秒后] ✅ 完成！步骤 1... 步骤 2...

复杂任务（> 15 秒）：

用户：整理记忆
AI：[立即回复] 好的，预计 30 秒，已启动子 agent
    [10 秒后] 📊 进度：30% - 读取文件
    [20 秒后] 📊 进度：70% - 更新记忆
    [30 秒后] ✅ 完成！

---

4. 进度汇报器（Progress Reporter）

汇报模板：

📊 进度汇报：{percentage}%

**当前步骤：** {currentStep}
**已完成：** {completedSteps}
**预计剩余：** {remainingTime}

你可以继续问我其他问题。

汇报频率：

• 中等任务：完成后一次性汇报
• 复杂任务：每 10 秒汇报
• 超长任务（>2 分钟）：每 30 秒汇报

---

使用示例

示例 1：简单任务

用户：在吗？

AI：[0 秒]
在的！随时待命～ 🦞💰

---

示例 2：中等任务

用户：帮我安装 tavily

AI：[0 秒]
好的，开始安装 tavily，预计 10 秒...

---
[10 秒后]

✅ 安装完成！

📍 步骤 1/2: 下载 skill... ✅
📍 步骤 2/2: 安装依赖... ✅

tavily 1.0.0 已就绪！

---

示例 3：复杂任务

用户：帮我写一篇公众号文章

AI：[0 秒]
好的，这个任务需要 30-60 秒。

🚀 已启动子 agent 处理写作任务
⏱️ 预计耗时：30-60 秒
📊 当前进度：0%

你可以继续问我其他问题，我会每 10 秒汇报一次进度。

---
[10 秒后]

📊 进度汇报：30%

**当前步骤：** 搜索素材
**已完成：** 找到 10 篇相关文章
**预计剩余：** 30 秒

---
[20 秒后]

📊 进度汇报：60%

**当前步骤：** 撰写正文
**已完成：** 完成大纲和开篇
**预计剩余：** 20 秒

---
[30 秒后]

📊 进度汇报：90%

**当前步骤：** 优化润色
**已完成：** 正文完成，正在检查
**预计剩余：** 10 秒

---
[40 秒后]

✅ 任务完成！

文章已保存到：
/root/openclaw/wechat-official/drafts/xxx.md

共 3500 字，阅读时间约 8 分钟。

---

配置选项

{
  "classification": {
    "simple": {
      "maxTime": 5,
      "keywords": ["状态", "怎么样", "在吗", "你好"],
      "strategy": "direct"
    },
    "medium": {
      "maxTime": 15,
      "keywords": ["检查", "安装", "生成", "创建"],
      "strategy": "execute-and-report"
    },
    "complex": {
      "maxTime": 30,
      "keywords": ["整理", "分析", "写文章", "批量"],
      "strategy": "sub-agent"
    }
  },
  "estimation": {
    "enabled": true,
    "showRange": true,
    "confidence": true
  },
  "reporting": {
    "enabled": true,
    "interval": 10,
    "format": "markdown"
  },
  "learning": {
    "enabled": true,
    "autoAdjust": true,
    "historySize": 100
  }
}

---

技术实现

核心脚本

• task-classifier.js - 任务分类器
• time-estimator.js - 时间预估器
• parallel-executor.js - 并行执行器
• progress-reporter.js - 进度汇报器
• session-manager.js - 会话管理器

依赖

• OpenClaw Gateway
• sessions_spawn（子 agent）
• sessions_send（进度汇报）

---

版本

v1.0.0 - 初始版本

• ✅ 任务分类器（简单/中等/复杂）
• ✅ 时间预估器（基于关键词和复杂度）
• ✅ 并行执行器（支持子 agent）
• ✅ 进度汇报器（定期汇报）
• ✅ 自适应学习（记录历史优化分类）

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许可证

MIT

作者

Amber & 小钳 🦞💰