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AC arista-device-health

Arista EOS device health check and triage procedure. Use when troubleshooting Arista 7000, 7500, or 720X series switches — assessing CPU, memory, interfaces, environment, and agent/daemon health. Covers MLAG state validation and VXLAN/EVPN health as data center extension steps. EOS is Linux-native — standard Linux diagnostics (bash top, df, dmesg) are valid troubleshooting tools alongside EOS show commands. Includes agent health monitoring via show agent for EOS-specific daemon failure detection.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 4 276 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Arista EOS device health check and triage procedure.

As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers

ProcedureAI 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

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

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 64/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4276 tokens
    • 100Steps. 56 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 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
    • +2Single-language instructions
    • +3Description length 501: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 56 items
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.