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xiaojia-skills小甲技能

Agent Skill

xiaojia-skills 用于辅助部署、云资源、容器和基础设施运维,适合在 OpenClaw 中需要检查配置、整理部署步骤或排查环境问题时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,305

周安装

135

GitHub Stars

公开资料未说明

下载量

1,058
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:xiaojia-skills(小甲技能)
来源仓库:https://github.com/tanchaowen84/xiaojia-skills
安装命令:
openclaw skills install xiaojia-skills
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install xiaojia-skills

简介

小甲技能用于对接 JustAI 开放接口,返回结构化 JSON 结果。

  • 适合在部署代理后执行异步营销计划或复杂业务流程。xiaojia-skills 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 需提前配置 openapi 端点与认证信息方可正常使用。
  • 调用过程可能产生外部请求,请评估网络延迟与安全策略。
  • 返回数据格式固定,需在前端做二次解析与错误处理。

SKILL.md

name
xiaojia-skills
description
Call the JustAI openapi async chat endpoints and return structured JSON results. Use when Codex needs to invoke the deployed JustAI agent for marketing plans, Xiaohongshu notes, image generation, collect-info turns, confirm-info card generation, or follow-up turns through /openapi/agent/chat_submit and /openapi/agent/chat_result instead of reasoning locally.
allowed-tools
Bash

Xiaojia Skills

Use the bundled scripts to inspect available projects and skills, then call the deployed JustAI openapi endpoint in two steps:

  1. list_projects.py lists your available资料库 folders/projects
  2. list_skills.py lists your available skill IDs
  3. chat.py submits the task and returns conversation_id
  4. chat_result.py polls chat_result until the task is completed or failed

This keeps the interface stable for slower branches such as card generation, plan generation, notes generation, and image generation.

When running inside Claude Code, ${CLAUDE_SKILL_DIR} resolves to this skill directory. Use that path when invoking the bundled scripts. In Codex or a plain shell, run the same scripts from the installed skill directory.

Workflow

  1. Ensure these environment variables are set before using the script:

- JUSTAI_OPENAPI_BASE_URL - JUSTAI_OPENAPI_API_KEY - Optional: JUSTAI_OPENAPI_TIMEOUT

  1. If the task should be scoped to a specific资料库, run scripts/list_projects.py first and choose one or more project_id.
  2. If the task should preload a specific skill, run scripts/list_skills.py first and choose one or more skill_id.
  3. Run scripts/chat.py with --message, optional --conversation-id, optional repeated --project-id, and optional repeated --skill-id.
  4. Preserve the returned conversation_id.
  5. Run scripts/chat_result.py --conversation-id ... to poll the task result.
  6. Read the returned JSON and use it directly:

- branch tells which real path ran, such as collect_info, confirm_info, generate_plan, generate_notes, or generate_image - result is the primary payload - text is the human-readable summary or fallback text - conversation_id must be preserved for follow-up turns

  1. If the user asks to continue an existing conversation, pass the previous conversation_id back into scripts/chat.py together with the new --message.
  2. For confirm_info results:

- To accept the card and continue, send a natural-language follow-up such as 这些信息没问题,继续生成方案. - To revise the card, send a natural-language correction such as 预算改成3万,目标用户改成25到30岁女性,请更新资料卡片. - Do not attempt structured form submission yet; the openapi layer currently supports message-based continuation only.

Commands

List projects:

python3 "${CLAUDE_SKILL_DIR}/scripts/list_projects.py"

List skills:

python3 "${CLAUDE_SKILL_DIR}/scripts/list_skills.py"

Run a new turn:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" --message "帮我做一份小红书运营方案"

Poll the result:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat_result.py" \
  --conversation-id "existing-conversation-id"

Continue an existing turn:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --conversation-id "existing-conversation-id" \
  --message "继续展开第二部分"

Continue after a confirmation card:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --conversation-id "existing-conversation-id" \
  --message "这些信息没问题,继续生成方案"

Revise a confirmation card in natural language:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --conversation-id "existing-conversation-id" \
  --message "预算改成3万,目标用户改成25到30岁女性,其他不变,请更新资料卡片"

Run a turn scoped to a selected project/folder:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --project-id "fld_demo" \
  --message "请参考这个资料库里的内容,帮我写一篇港理工校园生活图文笔记"

Run a turn with a manually selected skill:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --skill-id "skill_demo" \
  --message "使用这个技能继续分析"

Run a turn with both selected project and skill:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --project-id "fld_demo" \
  --skill-id "skill_demo" \
  --message "优先参考这个资料库并使用这个 skill 帮我生成方案"

Override the timeout for slower branches:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat.py" \
  --message "我是做敏感肌护肤的品牌,先帮我整理资料卡片"

Control polling interval:

python3 "${CLAUDE_SKILL_DIR}/scripts/chat_result.py" \
  --conversation-id "existing-conversation-id" \
  --poll-interval 2 \
  --timeout 300

Guardrails

  • Prefer the scripts over hand-written curl so submit and polling stay consistent.
  • When the task depends on a specific资料库 or手动技能, list them first and pass the exact IDs. The openapi request now统一使用 project_idskill_id 两个字段,值都是字符串数组。
  • Keep conversation_id from the last successful submit response and reuse it for polling and follow-up requests.
  • Treat result as the primary machine-readable payload. Use text only as fallback when result is plain text.
  • confirm_info currently continues through natural-language follow-up on the same conversation_id; there is no structured form_data submission path in openapi yet.
  • If the endpoint returns status=failed, surface message directly instead of retrying blindly.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

87.78%
按下载量换算929

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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