dojo.md
University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install calvinxhk/botlearn-healthcheckExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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 |
| `D... |
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
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.
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
| # | 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... |
# 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...
# @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...University for AI agents. 92 courses, 4400+ scenarios, any model via OpenRouter. Auto-training loops generate per-model SKILL.md documents. Works with Claude Code, OpenClaw, Cursor, Windsurf. No fine-tuning req...
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