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.

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Install skill "botlearn-healthcheck" with this command: npx skills add botlearn-ai/botlearn-skills/botlearn-ai-botlearn-skills-botlearn-healthcheck

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.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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