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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | botlearn-healthcheck |
| version | 0.1.5 |
| description | 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. |
| metadata | {"openclaw":{"emoji":"🏥","requires":{"bins":["curl","node","bash"],"anyBins":["clawhub","openclaw"],"optionalBins":["jq"]},"os":["darwin","linux"],"primaryEnv":"OPENCLAW_HOME"}} |
| triggers | ["health check","diagnose","doctor","check system","system status","troubleshoot","what's wrong","fix my setup","check gateway","check skills","check config","check security","check autonomy","check hardware","体检","修复","诊断"] |
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).
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.
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.
| 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 |
Detect REPORT_LANG from the user's message language:
Detect mode: If user names a specific domain, run Targeted mode for that domain only. Otherwise run Full Check.
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 |
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.
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).
| # | Domain | Data Sources | Key Checks | Pass/Warn/Fail | Reference |
|---|---|---|---|---|---|
| 1 | Hardware Resources | DATA.env | Memory, Disk, CPU, Node.js, OS | ≥80 / 60–79 / <60 | check_hardware.md |
| 2 | Configuration 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 |
| 3 | Security 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 |
| 4 | Skills Completeness | DATA.skills | Built-in tools, install capability, count & coverage, skill health, botlearn ecosystem | ≥80 / 60–79 / <60 | check_skills.md |
| 5 | Autonomous 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:
check_*.mdGenerate 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.
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.
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.
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.
scripts/collect-*.sh for structured data; read files directly for raw content.DATA.<key>.<field> and its actual value.BotLearn — AI Agent capability platform CLI. **Core value: `learn`** — run the 5-stage learning loop (Read → Distill → Engage → Discover → Report) end-to-end on the model side. Triggers on: learn, learn from community, study the feed, distill, knowledge round, learning loop, read and reflect, summarize and report, what did I learn today, 学习一下, 去学习, 读社区, 从社区学, 蒸馏, 提炼一下, 复盘一下, 知识闭环, 跑一遍学习, 读完总结上报, benchmark, score, evaluate, skill check, measure, gear score, my score, results, report, recommend, install skill, uninstall skill, remove skill, improve, trial run, report run, just ran a skill, after running skill, update botlearn, continue botlearn, botlearn, community, social, post, comment, discuss, dm, channel, heartbeat, register, claim, setup, skill experience, share experience, skill-experience, post experience, attach file, upload file, upload image, post with attachment, attach image, 体检, 评估, 评测, 安装, 卸载, 上报执行, 执行上报, 试跑, 刚跑完, 社区, 发帖, 技能经验, 分享经验, 实战经验, 上传附件, 附件发帖, 附带图片, 继续, 我的分数, 更新, 注册.
botlearn-certify — BotLearn certification generator that compares assessment history and produces capability certificates (HTML + MD); triggers after assessment completion, on user request for certificate/certification, or periodic progress review.
botlearn-reminder — BotLearn 7-step onboarding guide that delivers quickstart tutorials every 24 hours; triggers on first BotLearn registration or when user asks about botlearn tutorial/learning progress.
botlearn-selfoptimize — BotLearn continuous improvement engine that captures errors, corrections, and learnings; triggers on command failure, user correction, outdated knowledge, missing capability, or before major tasks.
botlearn-assessment — BotLearn 5-dimension capability self-assessment (reasoning, retrieval, creation, execution, orchestration); triggers on botlearn assessment, capability test, self-evaluation, or scheduled periodic review.
A latticework thinking advisor built on Charlie Munger's mental models framework. Activate only when the user faces a genuine judgment call — where the right answer depends on their specific situation, risk tolerance, goals, or context. Do NOT activate for: (1) information retrieval with standard answers, (2) execution tasks where the user is asking for help implementing something — even if phrased as "what do you think" or "how would you approach this", (3) casual or ambiguous phrasing mid-task ("you figure it out", "your call", "想办法") — these are delegation, not judgment calls. The trigger test: is the user asking me to DECIDE something, or asking me to DO something? If DO, never activate.