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BC pg-game-monitor

Prometheus + Grafana 监控方案，专为**单台物理机部署多个 Java 游戏进程 + 自建 MySQL**的场景设计。Java 进程通过 jstat/jcmd 无侵入采集 JVM 运行时指标（堆内存、新生代、老年代、GC、线程、类内存），MySQL 通过 pymysql 采集（Buffer Pool、进程内存）。监控重点围绕**内存使用率**，适配游戏服高频 GC 和大内存占用的特点。通过 Pushgateway 推送数据，Grafana 可视化，Alertmanager + 飞书 Webhook 告警，支持 Ansible 批量部署。

ClawHub Agent Skills author: freepengyang v1.3.3 MIT-0 16 files · 1 script body ≈ 1 284 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationMySQLSoftware developmentInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
74
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-background-process references/files/monitor_install.sh:326
    Starts a background / autostarted process
    systemctl enable prometheus alertmanager pushgateway grafana-server feishu

Files scanned: 16. 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")
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "required_environment_variables"

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1284 tokens
  • 100Progress reporting. Reports progress

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 280: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
This is a coherent monitoring skill, but it deserves review because it installs persistent system services, handles database/webhook secrets, and sends host metrics externally with incomplete safeguards.
LLM: suspicious (high) · VirusTotal: · 29 May 2026