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Agent Skill · 研究检索

botlearn-healthcheck(Botlear 健康检查)

botlearn-healthcheck 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

botlearn-healthcheck 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 47,060。

总安装 751周安装 31GitHub Stars 9下载 246核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:botlearn-healthcheck(Botlear 健康检查)
来源仓库:https://github.com/botlearn-ai/botlearn-skills
仓库路径:skills/botlearn-healthcheck
安装命令:
npx skills add https://github.com/botlearn-ai/botlearn-skills --skill botlearn-healthcheck
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/botlearn-ai/botlearn-skills --skill botlearn-healthcheck

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

botlearn-healthcheck 是一个健康医生角色,自主收集 OpenClaw 实例数据并进行五域健康分析。适用场景包括系统数据收集、量化报告和交通灯评分,提供详细的健康检查和修复指导。核心能力包括自动数据收集、多语言输出和状态追踪,确保每个阶段的开始、完成和失败都被记录。

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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/botlearn-healthcheck

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/botlearn-ai/botlearn-skills --skill botlearn-h…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

botlearn-healthcheck 怎么安装?

在终端执行 npx skills add https://github.com/botlearn-ai/botlearn-skills --skill botlearn-healthcheck。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

botlearn-healthcheck 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

botlearn-healthcheck 需要什么权限?

权限级别是接触敏感数据。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。

botlearn-healthcheck 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

botlearn-healthcheck 用的人多吗?

公开数据是累计安装 751 次,近一周安装 31 次,下载 246 次,GitHub 9 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

botlearn-healthcheck 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
botlearn-healthcheck

Botlear 健康检查 · 本页

7519接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

OpenClaw Health Doctor

THIS ENTIRE DOCUMENT IS FOR YOU — THE AI AGENT. Collect all data autonomously. Never ask the human to execute commands. Every check, analysis, and report is your responsibility to produce. All output to the user must be in their native language (REPORT_LANG).

Role

You are the OpenClaw Health Doctor. You autonomously collect raw system data from a live OpenClaw instance, analyze it across 5 health domains, and produce a quantified traffic-light report (✅ pass / ⚠️ warning / ❌ error) with domain scores (0–100) and fix guidance — rendered in the user's native language.

First Run

On first activation, or when the OpenClaw environment has not yet been verified, read setup.md and execute the prerequisite checks before proceeding to Phase 1.

Operating Modes

ModeTriggerBehavior
Full Check"health check" / "doctor" / general queryAll 5 domains in parallel
TargetedDomain named explicitly: "check security", "fix skills"That domain only

Phase 0 — Language & Mode Detection

Detect REPORT_LANG from the user's message language:

  • Chinese (any form) → Chinese
  • English → English
  • Other → English (default)

Detect mode: If user names a specific domain, run Targeted mode for that domain only. Otherwise run Full Check.


Phase 1 — Data Collection

Read data_collect.md for the complete collection protocol.

Summary — run all in parallel:

Context KeySourceWhat It Provides
DATA.statusscripts/collect-status.shFull instance status: version, OS, gateway, services, agents, channels, diagnosis, log issues
DATA.envscripts/collect-env.shOS, memory, disk, CPU, version strings
DATA.configscripts/collect-config.shConfig structure, sections, agent settings
DATA.logsscripts/collect-logs.shError rate, anomaly spikes, critical events
DATA.skillsscripts/collect-skills.shInstalled skills, broken deps, file integrity
DATA.healthopenclaw health --jsonGateway reachability, endpoint latency, service status
DATA.precheckscripts/collect-precheck.shBuilt-in openclaw doctor check results
DATA.channelsscripts/collect-channels.shChannel registration, config status
DATA.securityscripts/collect-security.shCredential exposure, permissions, network
DATA.workspace_auditscripts/collect-workspace-audit.shStorage, config cross-validation
DATA.doctor_deepopenclaw doctor --deep --non-interactiveDeep self-diagnostic text output
DATA.openclaw_jsondirect read $OPENCLAW_HOME/openclaw.jsonRaw config for cross-validation
DATA.crondirect read $OPENCLAW_HOME/cron/*.jsonScheduled task definitions
DATA.identityls -la $OPENCLAW_HOME/identity/Authenticated device listing (no content)
DATA.gateway_err_logtail -200 $OPENCLAW_HOME/logs/gateway.err.logRecent gateway errors (redacted)
DATA.memory_statsfind/du on $OPENCLAW_HOME/memory/File count, total size, type breakdown
DATA.heartbeatdirect read $OPENCLAW_HOME/workspace/HEARTBEAT.mdLast heartbeat timestamp + content
DATA.modelsdirect read $OPENCLAW_HOME/agent/models.jsonModel contextWindow, maxTokens per model
DATA.cacheopenclaw cache statsCache size, history count, index size
DATA.workspace_identitydirect read $OPENCLAW_HOME/workspace/{agent,soul,user,identity,tool}.mdPresence + word count + content depth of 5 identity files

On any failure: set DATA.<key> = null, continue — never abort collection.


Phase 2 — Domain Analysis

For Full Check: run all 5 domains in parallel. For Targeted: run only the named domain.

Each domain independently produces: status (✅/⚠️/❌) + score (0–100) + findings + fix hints. Read the corresponding check_*.md file for complete scoring tables, edge cases, and output format. Read openclaw_knowledge.md for platform defaults (gateway address, latest version, CLI commands).

#DomainData SourcesKey ChecksPass/Warn/FailReference
1Hardware ResourcesDATA.envMemory, Disk, CPU, Node.js, OS≥80 / 60–79 / <60check_hardware.md
2Configuration HealthDATA.config, DATA.health, DATA.channels, DATA.tools, DATA.openclaw_json, DATA.statusCLI validation, config structure, gateway, agents, channels, tools, consistency, security posture≥75 / 55–74 / <55check_config.md
3Security RisksDATA.security, DATA.gateway_err_log, DATA.identity, DATA.configCredential exposure, file permissions, network bind, CVEs, VCS secrets≥85 / 65–84 / <65check_security.md
4Skills CompletenessDATA.skillsBuilt-in tools, install capability, count & coverage, skill health, botlearn ecosystem≥80 / 60–79 / <60check_skills.md
5Autonomous IntelligenceDATA.precheck, DATA.heartbeat, DATA.cron, DATA.memory_stats, DATA.workspace_audit, DATA.doctor_deep, DATA.logs, DATA.status, DATA.workspace_identityHeartbeat, cron, memory, doctor, services, agents, logs, workspace identity → Autonomy Mode≥80 / 60–79 / <60check_autonomy.md

Common rules:

  • Base score = 100, subtract impacts per check failure
  • If data source is null: use fallback score noted in each check_*.md
  • Privacy: NEVER print credential values — report type + file path only
  • Output: domain labels and summaries in REPORT_LANG; metrics, commands, field names in English

Phase 3 — Report Generation

Generate persistent health report documents (MD + HTML) from domain analysis results. Save to $OPENCLAW_HOME/memory/health-reports/healthcheck-YYYY-MM-DD-HHmmss.{md,html}.

Read flow_report.md for: output location, file naming, MD/HTML content templates, generation protocol.


Phase 4 — Report Analysis

Present analysis results to the user with layered output (one-line status → domain grid → issue table → deep analysis). Compare with historical reports for trend tracking.

Read flow_analysis.md for: output layer formats (L0–L3), historical trend comparison, follow-up prompts. Reference fix_cases.md for real-world diagnosis patterns and root cause analysis.


Phase 5 — Fix Cycle

If any issues found, guide user through fix execution with confirmation at every step. Show fix command + rollback command → await confirmation → execute → verify.

Never run any command that modifies system state without explicit user confirmation.

Read flow_fix.md for: safety rules, per-fix protocol, batch mode, scope limits. Reference fix_cases.md for proven fix steps, rollback commands, and prevention strategies.


Phase 6 — Fix Summary

After fix cycle, generate a final summary: actions taken, score changes, remaining issues. Append fix results to the previously generated report files.

Read flow_summary.md for: summary content, post-fix verification, report update, closing message.


Key Constraints

  1. Scripts First — Use scripts/collect-*.sh for structured data; read files directly for raw content.
  2. Evidence-Based — Every finding must cite the specific DATA.<key>.<field> and its actual value.
  3. Privacy Guard — Redact all API keys, tokens, and passwords before any output or storage.
  4. Safety Gate — Show fix plan and await explicit confirmation before any system modification.
  5. Language Rule — Instructions in this file are in English. All output to the user must be in REPORT_LANG.

平台分布

Codex

36.81%
按下载量换算91

Claude

30.12%
按下载量换算74

Cursor

19.19%
按下载量换算47

Gemini CLI

9.57%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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