SKILLEMALL.ai

BB monitoring-aiops

Use this skill whenever the user needs to operate a network / infrastructure monitoring NOC on SolarWinds Orion (SWIS REST + SWQL), Paessler PRTG (web API), or Zabbix 6.x/7.x (JSON-RPC) — a one-shot NOC overview, canned SWQL answers (nodes down, flapping interfaces, muted, high-CPU nodes, full volumes, unmanaged/scheduled), a validated read-only SWQL passthrough, deduped/rolled-up active alerts, SolarWinds node/interface/volume/application health and top-N, PRTG sensors/devices/groups/history/alarms, Zabbix problems/hosts/host-groups/triggers/events/item-history/maintenances, and guarded writes (acknowledge, mute/unmute, schedule maintenance, unmanage/remanage, remove node, pause/resume sensor, create/delete Zabbix maintenance window). Always use this skill for "SolarWinds", "Orion", "SWQL", "THWACK question", "PRTG", "Paessler", "Zabbix", "Zabbix problem", "Zabbix trigger", "Zabbix maintenance", "NOC overview", "which nodes are down", "flapping interfaces", "interface flap storm", "alert storm", "acknowledge this alert", "worst CPU nodes", "top-N by latency/packet loss", "which volumes are full", "muted alerts report", "unmanaged nodes", "schedule a maintenance window", "unmanage / remanage a node", "pause a PRTG sensor" when the context is monitoring. Do NOT use when the target is something other than a SolarWinds/PRTG/Zabbix monitoring platform (a hypervisor, storage appliance, backup product, Kubernetes cluster, network device config, or OT/industrial equipment) — route those to the appropriate other AIops-tools skill. Governed monitoring operations with a built-in governance harness (audit, policy, token budget, undo, risk-tiers). PRTG's free Freeware edition and an open-source Zabbix appliance are the easiest live checks; SolarWinds is trial-only past 30 days.

ClawHub Claude Code author: wei zhou v0.10.0 MIT-0 6 files body ≈ 2 708 tokens Open the sourceclawhub.ai analyzed 25 h ago

x/7.x (JSON-RPC) — a one-shot NOC overview, canned SWQL answers (nodes down, flapping interfaces, muted, high-CPU nodes, full volumes, unmanaged/scheduled), a…

As a process B 71/100 · Nearly there — weak spots: result and completion, inputs and preconditions

IntegrationKubernetesAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
64
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
55
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.

Broad scope 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Shorten the description to 1024 characters.
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
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1797 chars, limit 1024
  • note description-budget description takes 1797 of the ~15000-char shared budget for all skills
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "installer"

Process rating: all ten parameters 71/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 55Failures and branches. 1 branches
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 37 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2708 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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

  • +3Description length 1796: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 21 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 37 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
The skill is transparent about operating monitoring systems, but it can make high-impact production changes with stored credentials while lacking enforced approval/read-only controls and using an insecure TLS default.
LLM: suspicious (high) · 12 Sept 2026