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A comprehensive, self-evolving skill designed to diagnose and solve OpenClaw issues by following a structured, multi-stage resolution cycle. It incorporates Proactive Prediction (L2), Robustness Checks (L1), Knowledge Creation (L3), Diagnosis Report Visualization (v5.5), v6.0 Runtime Health + Key Validation + Unified Report + Health Dashboard, and Gateway Watchdog (v6.1).

ClawHub Agent Skills author: Mikewong v6.1.0 MIT-0 27 files body ≈ 4 211 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 56/100 · Will not run — References files that are not bundled: scripts/watchdog_state.json, scripts/gateway_watchdog.log

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
78/100
safety, quality, tests
Safety 60%
89
Quality 40%
62
Run on models
none yet
Process rating
F
56/100
Will not run
References files that are not bundled: scripts/watchdog_state.json, scripts/gateway_watchdog.log
Tools and files w 18
0
Result and completion w 14
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 3

✓ No critical or high findings

Medium and low: 3
  • medium Dangerous commands cmd-persistence scripts/watchdog_monitor.py:432
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
    reg = r"HKCU:\Software\Microsoft\Windows\CurrentVersion\Run"
    code literal
  • medium Dangerous commands cmd-persistence scripts/watchdog_monitor.py:439
    Persistence mechanism (cron / launchd / scheduled task / autorun registry) (string literal in code, not executed)
    reg = r"HKCU:\Software\Microsoft\Windows\CurrentVersion\Run"
    code literal
  • low Secrets in code secret-high-entropy-token docs/enhancement/MODULE_03_Enhancement_Reports.md:2
    High-entropy token-like string (may be an id, hash or a credential)
    name: MODU…rts

Files scanned: 27. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: scripts/watchdog_state.json
  • warning missing-ref reference to a missing file: scripts/gateway_watchdog.log

Process rating: all ten parameters 56/100

Will not run. References files that are not bundled: scripts/watchdog_state.json, scripts/gateway_watchdog.log
  • 0Tools and files. 2 referenced file(s) missing: scripts/watchdog_state.json, scripts/gateway_watchdog.log
  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 12 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4211 tokens
  • 100Steps. 90 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 10 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -273 emoji in the instructions: noise for the model
  • -32 of 8 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 374: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 90 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (4 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This appears to be a real OpenClaw troubleshooting skill, but it needs Review because it can perform repairs, run a background watchdog, persist on login, kill processes, archive session files, read credentials/logs, and send diagnostic details externally.
LLM: suspicious (high) · 28 May 2026