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Skills 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.

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/asterisk622/xiaoding-botlearn-healthcheck" ~/.claude/skills/clawdbot-skills-botlearn-healthcheck-bcf96c && rm -rf "$T"
manifest: skills/asterisk622/xiaoding-botlearn-healthcheck/SKILL.md
source content

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.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
DATA.skills
scripts/collect-skills.sh
Installed skills, broken deps, file integrity
DATA.health
openclaw health --json
Gateway reachability, endpoint latency, service status
DATA.precheck
scripts/collect-precheck.sh
Built-in openclaw doctor check results
DATA.channels
scripts/collect-channels.sh
Channel registration, config status
DATA.security
scripts/collect-security.sh
Credential exposure, permissions, network
DATA.workspace_audit
scripts/collect-workspace-audit.sh
Storage, config cross-validation
DATA.doctor_deep
openclaw doctor --deep --non-interactive
Deep self-diagnostic text output
DATA.openclaw_json
direct read 
$OPENCLAW_HOME/openclaw.json
Raw config for cross-validation
DATA.cron
direct read 
$OPENCLAW_HOME/cron/*.json
Scheduled task definitions
DATA.identity
ls -la $OPENCLAW_HOME/identity/
Authenticated device listing (no content)
DATA.gateway_err_log
tail -200 $OPENCLAW_HOME/logs/gateway.err.log
Recent gateway errors (redacted)
DATA.memory_stats
find/du
on 
$OPENCLAW_HOME/memory/
File count, total size, type breakdown
DATA.heartbeat
direct read 
$OPENCLAW_HOME/workspace/HEARTBEAT.md
Last heartbeat timestamp + content
DATA.models
direct read 
$OPENCLAW_HOME/agent/models.json
Model contextWindow, maxTokens per model
DATA.cache
openclaw cache stats
Cache size, history count, index size
DATA.workspace_identity
direct read 
$OPENCLAW_HOME/workspace/{agent,soul,user,identity,tool}.md
Presence + 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 Resources
DATA.env
Memory, Disk, CPU, Node.js, OS≥80 / 60–79 / <60
check_hardware.md
2Configuration Health
DATA.config
, 
DATA.health
, 
DATA.channels
, 
DATA.tools
, 
DATA.openclaw_json
, 
DATA.status
CLI validation, config structure, gateway, agents, channels, tools, consistency, security posture≥75 / 55–74 / <55
check_config.md
3Security Risks
DATA.security
, 
DATA.gateway_err_log
, 
DATA.identity
, 
DATA.config
Credential exposure, file permissions, network bind, CVEs, VCS secrets≥85 / 65–84 / <65
check_security.md
4Skills Completeness
DATA.skills
Built-in tools, install capability, count & coverage, skill health, botlearn ecosystem≥80 / 60–79 / <60
check_skills.md
5Autonomous Intelligence
DATA.precheck
, 
DATA.heartbeat
, 
DATA.cron
, 
DATA.memory_stats
, 
DATA.workspace_audit
, 
DATA.doctor_deep
, 
DATA.logs
, 
DATA.status
, 
DATA.workspace_identity
Heartbeat, cron, memory, doctor, services, agents, logs, workspace identity → Autonomy Mode≥80 / 60–79 / <60
check_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.