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BC ai-monitor-pro

AI monitoring for construction sites and IT infrastructure: dashboards, alerts, incident playbooks, photo reports, SLA and capacity control.

ClawHub Agent Skills author: RAAIPRO v3.5.3 MIT-0 25 files · 3 scripts body ≈ 10 012 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData 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
80/100
safety, quality, tests
Safety 60%
100
Quality 40%
50
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 0

✓ No critical or high findings

Files scanned: 24. 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 body-long SKILL.md body ≈ 10012 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "price"
  • note frontmatter-key unknown frontmatter key "price_currency"

Process rating: all ten parameters 50/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (ai-monitor-pro) differs from the folder (raai-ai-monitor-pro)
  • 40Execution cost. Instruction body is 10012 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 12 steps
  • 100Running it twice. No mutating operations

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)
  • +4Structure: 1 headings, hard to scan
  • +3Output format is not stated: the model decides each time
  • -5Long text without headings
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • +3Description length 140: enough signal without eating the budget
  • +3Step-by-step instructions: 12 items
  • +4Has examples (23 code blocks)
  • +1License stated
  • +2Bilingual instructions (RU + EN)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.

External checks

ClawHub: suspicious
The skill is mostly a monitoring and incident-response playbook, but it needs review because broad prompts can lead to production-impacting commands and sensitive operational reporting without clear confirmation boundaries.
LLM: suspicious (high) · VirusTotal: · 29 May 2026