SKILLEMALL.ai
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BB network-aiops

Use this skill whenever the user needs to operate a network device — read device facts, interfaces (+ counters/IP), BGP/LLDP neighbors (summary and detail), ARP/MAC tables, VLANs, routes, hardware environment (fans/temp/power/CPU/mem), optics, NTP, users, SNMP info, VRFs, and an aggregated device-health summary; run read-only RCA diagnostics on interface health and BGP neighbors; back up a switch/router config, diff a candidate config (dry-run), and merge/replace/rollback config — across Cisco IOS/IOS-XE, Nexus NX-OS, IOS-XR, Arista EOS, and Juniper Junos via NAPALM. An optional NetBox block adds source-of-truth lookups. Always use this skill for "back up switch config", "show bgp neighbors", "diff network config", "push config to router", "show interfaces on the switch", or tasks mentioning "cisco", "arista", "juniper", "nexus", "ios-xr", or "napalm". Do NOT use when the target is not a NAPALM-supported network device (Kubernetes clusters, hypervisor VMs, and cloud consoles are out of scope — route those elsewhere). Common multi-vendor device operations with a built-in governance harness (audit, policy, token budget, undo, risk-tiers).

ClawHub Claude Code author: wei zhou v0.12.1 MIT-0 6 files body ≈ 5 084 tokens Open the sourceclawhub.ai↗ analyzed 33 h ago

Use this skill whenever the user needs to operate a network device — read device facts, interfaces (+ counters/IP), BGP/LLDP neighbors (summary and detail)…

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureKubernetesGitHubAI and agentstype 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%
95
Quality 40%
57
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
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.

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.
  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 · 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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1155 chars, limit 1024
  • warning body-long SKILL.md body ≈ 5084 tokens (recommended < 5000); move details to references/
  • 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 68/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 17 mutating operations with no state check
  • 70Execution cost. Instruction body is 5084 tokens
  • 85Steps. 29 steps, 1 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 top-level sections: this looks like several domains in one skill
  • 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 1154: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (4 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: 57.

External checks

ClawHub: suspicious
This is coherent network-automation tooling, but it needs Review because it can change network devices and expose network secrets without built-in authorization controls.
LLM: suspicious (high) · 12 Sept 2026