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botlearn-healthcheckBotlear 健康检查

Agent Skill

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。

总安装

751

周安装

31

GitHub Stars

9

下载量

246
CodexClaudeCursorGemini CLI

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

unknown

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: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

简介

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。

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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

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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