Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
openclaw-behavior-plan
Generates structured behavior plans for OpenClaw agents based on user requirements. Use when the user asks to create a plan, design agent behavior, plan mult...
skill openclawclawhub Free
Generates structured behavior plans for OpenClaw agents based on user requirements. Use when the user asks to create a plan, design agent behavior, plan mult...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- examples.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Umfl
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
OpenClaw 行为计划生成（openclaw-behavior-plan）
触发时：输出「正在触发 openclaw-behavior-plan skill」。 根据用户描述的目标或需求，生成适合 OpenClaw Agent 执行的结构化行为计划。计划应可映射到 TOOLS.md/SKILLS.md 中的工具与技能，支持多步推理循环（Load → Call → Parse → Execute → Append → Loop）。
1. 计划结构模板
- 生成计划时使用以下结构：
- # 行为计划：[任务标题]
- ## 目标
- [一句话描述用户期望的最终结果]
- ## 前置条件
- [ ] 所需工具/技能是否可用
- [ ] 必要信息是否已获取
- ## 执行步骤
- ### 步骤 1: [步骤名称]
- **目的**: [本步要达成什么]
- **工具/技能**: [execute_shell | search_web | read_file | 某 skill 的 action]
- **输入**: [参数或依赖]
- **预期输出**: [本步完成后得到什么]
- ### 步骤 2: [步骤名称]
- ...
- ### 步骤 N: [步骤名称]
- ...
- ## 异常与回退
- 若 [某步骤] 失败 → [备选方案或重试策略]
- ## 完成标准
- [ ] [可验证的完成条件 1]
- [ ] [可验证的完成条件 2]
2. 生成原则
步骤可执行：每步对应 TOOLS.md 或 SKILLS.md 中的具体能力，避免抽象描述。 依赖顺序：后步骤依赖前步骤的输出时，明确写出「依赖步骤 N 的 [输出]」。 工具选择： 需要当前信息 → search_web 需要读/写文件 → read_file / write_file 需要执行脚本 → execute_shell 需要第三方服务 → 对应 skill（如 calendar、email、slack） 合理粒度：单步不宜过大（难以失败定位），不宜过小（增加循环次数）。 异常处理：对可能失败的步骤（网络、权限、格式错误）给出回退或重试说明。
3. 与 OpenClaw 推理循环的对应
计划步骤推理循环中的体现步骤 1 执行Load → Call → Parse(tool_call) → Execute → Append步骤 2 执行下一轮 Loop，基于步骤 1 结果继续完成标准LLM 产出 final 文本、无待执行 tool_call 时停止
4. 输出格式
直接输出完整计划（Markdown），无需额外包装。 若用户需求模糊，先列出 1–2 个澄清问题，再生成计划。 若涉及敏感操作（如 execute_shell 删除、修改系统），在计划中标注「需用户确认」。
5. 更多示例
详见 examples.md。
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- examples.md