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botlearn-healthcheck

botlearn-healthcheck — BotLearn autonomous health inspector for OpenClaw instances across 5 domains (hardware, config, security, skills, autonomy); triggers on system check, health report, diagnostics, or scheduled heartbeat inspection.

calvinxhk0 来源安装数☆ 0 仓库 Starsv0.1.5

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install calvinxhk/botlearn-healthcheck

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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

Mode Trigger Behavior
Full Check "health check" / "doctor" / general query All 5 domains in parallel
Targeted Domain 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 Key Source What It Provides
DATA.status scripts/collect-status.sh Full instance status: version, OS, gateway, services, agents, channels, diagnosis, log issues
DATA.env scripts/collect-env.sh OS, memory, disk, CPU, version strings
DATA.config scripts/collect-config.sh Config structure, sections, agent settings
DATA.logs scripts/collect-logs.sh Error rate, anomaly spikes, critical events
`D...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/calvinxhk/botlearn-healthcheck
Version
0.1.5
Commit
bebe2d43d8ad7dc39380d44f7cb959e1e60e6dc5
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

README

@botlearn/botlearn-healthcheck

OpenClaw Autonomous Health Inspector — collects system data across 5 domains, produces traffic-light reports with scores and fix guidance, and walks the user through repairs.

How It Works

The health check runs a 6-phase pipeline, fully autonomous:

Phase 0: Detect language + operating mode (full check or targeted domain)

Phase 1: Data Collection
    → Run 11 collection scripts in parallel
    → Read 10 config/state files directly
    → Store all data as DATA.* context keys
    → Any failure sets DATA.<key> = null, never aborts

Phase 2: Domain Analysis
    → Analyze 5 health domains (all in parallel for full check)
    → Each domain: base score 100, subtract per-check failures
    → Produce: status + score (0-100) + findings + fix hints

Phase 3: Report Generation
    → Save MD + HTML reports to $OPENCLAW_HOME/memory/health-reports/

Phase 4: Report Analysis
    → Present layered output: one-line status → domain grid → issues → deep analysis
    → Compare with historical reports for trend tracking

Phase 5: Fix Cycle
    → Show fix command + rollback → await user confirmation → execute → verify
    → Never modify system state without explicit confirmation

Phase 6: Fix Summary
    → Actions taken, score changes, remaining issues

5 Health Domains

# Domain Score Thresholds What It Checks
1 Hardware Resources ≥80 / 60-79 / <60 Memory, disk, CPU, Node.js version, cache pressure
2 Configuration Health ≥75 / 55-74 / <55 Config validation, gateway, agents, channels, tools, models, session, security posture
3 Security Risks ≥85 / 65-84 / <65 Credential exposure, file permissions, network bind, VCS secrets
4 Skills Completeness ≥80 / 60-79 / <60 Built-in tools, install capability, skill health, botlearn ecosystem
5 Autonomous Intelligence ≥80 / 60-79 / <60 Heartbeat, cron tasks, memory, doctor output, workspace identi...

来源内容

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

| Mode | Trigger | Behavior |
|------|---------|----------|
| Full Check | "health check" / "doctor" / general query | All 5 domains in parallel |
| Targeted | Domain 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 Key | Source | What It Provides |
|-------------|--------|-----------------|
| `DATA.status` | `scripts/collect-status.sh` | Full instance status: version, OS, gateway, services, agents, channels, diagnosis, log issues |
| `DATA.env` | `scripts/collect-env.sh` | OS, memory, disk, CPU, version strings |
| `DATA.config` | `scripts/collect-config.sh` | Config structure, sections, agent settings |
| `DATA.logs` | `scripts/collect-logs.sh` | Error rate, anomaly spikes, critical events |
| `D...
README
# @botlearn/botlearn-healthcheck

OpenClaw Autonomous Health Inspector — collects system data across 5 domains, produces traffic-light reports with scores and fix guidance, and walks the user through repairs.

## How It Works

The health check runs a 6-phase pipeline, fully autonomous:

```
Phase 0: Detect language + operating mode (full check or targeted domain)

Phase 1: Data Collection
    → Run 11 collection scripts in parallel
    → Read 10 config/state files directly
    → Store all data as DATA.* context keys
    → Any failure sets DATA.<key> = null, never aborts

Phase 2: Domain Analysis
    → Analyze 5 health domains (all in parallel for full check)
    → Each domain: base score 100, subtract per-check failures
    → Produce: status + score (0-100) + findings + fix hints

Phase 3: Report Generation
    → Save MD + HTML reports to $OPENCLAW_HOME/memory/health-reports/

Phase 4: Report Analysis
    → Present layered output: one-line status → domain grid → issues → deep analysis
    → Compare with historical reports for trend tracking

Phase 5: Fix Cycle
    → Show fix command + rollback → await user confirmation → execute → verify
    → Never modify system state without explicit confirmation

Phase 6: Fix Summary
    → Actions taken, score changes, remaining issues
```

## 5 Health Domains

| # | Domain | Score Thresholds | What It Checks |
|---|--------|-----------------|---------------|
| 1 | Hardware Resources | ≥80 / 60-79 / <60 | Memory, disk, CPU, Node.js version, cache pressure |
| 2 | Configuration Health | ≥75 / 55-74 / <55 | Config validation, gateway, agents, channels, tools, models, session, security posture |
| 3 | Security Risks | ≥85 / 65-84 / <65 | Credential exposure, file permissions, network bind, VCS secrets |
| 4 | Skills Completeness | ≥80 / 60-79 / <60 | Built-in tools, install capability, skill health, botlearn ecosystem |
| 5 | Autonomous Intelligence | ≥80 / 60-79 / <60 | Heartbeat, cron tasks, memory, doctor output, workspace identi...

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