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在 OpenClaw 中安装
/install parallel-responder
功能描述
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...
安全使用建议
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.
功能分析
Type: OpenClaw Skill
Name: parallel-responder
Version: 1.0.0
The skill is classified as suspicious due to its reliance on the OpenClaw platform's `sessions_spawn` mechanism, as described in `SKILL.md` and implemented in `scripts/parallel-executor.js`. This feature allows the skill to launch 'sub-agents' and pass user messages to them for 'complex tasks'. While `sessions_spawn` is an intended platform capability, passing user-controlled input to dynamically spawned agents without explicit sanitization or strict sandboxing creates a significant attack surface, potentially leading to Remote Code Execution (RCE) if the sub-agents are vulnerable to prompt or command injection. There is no direct evidence of intentional malicious behavior within the provided files, but the high-risk delegation mechanism warrants a 'suspicious' classification.
能力评估
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.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install parallel-responder - 安装完成后,直接呼叫该 Skill 的名称或使用
/parallel-responder触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Parallel Responder v1.0.0 - Initial Release
- Implements intelligent task classification (simple, medium, complex) based on keywords and estimated time.
- Adds a time estimator to predict task duration and handle user expectations.
- Supports parallel execution strategies, including spawning sub-agents for complex tasks.
- Provides real-time progress reporting and updates (immediate reply, scheduled progress reports).
- Features adaptive learning to optimize classification thresholds using historical data.
元数据
常见问题
Parallel Responder 是什么?
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... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 417 次。
如何安装 Parallel Responder?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install parallel-responder」即可一键安装,无需额外配置。
Parallel Responder 是免费的吗?
是的,Parallel Responder 完全免费(开源免费),可自由下载、安装和使用。
Parallel Responder 支持哪些平台?
Parallel Responder 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Parallel Responder?
由 Amber03(@lsa03)开发并维护,当前版本 v1.0.0。
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