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Anta Validation

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automateyournetwork
anta-validation

Validate Arista EOS network state against ANTA's pre-built 208-test catalogue, with structured pass/fail verdicts. Use for "is this switch healthy", "did my change break anything", "verify BGP/interfaces/hardware are correct", "run a health check on this device". Read-only. A test for a feature the device does not run reports not_applicable — never a failure — and no health percentage is ever emitted. For non-Arista devices, or an assertion ANTA's catalogue doesn't cover, use `pyats-dynamic-test` to author a custom aetest script instead.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill nameanta-validation
Stars
680
Forks
189
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by automateyournetwork on GitHub. Read the source before you install it.

Installation

Install the Anta Validation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/anta-validation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/anta-validation .claude/skills/anta-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anta Validation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Anta Validation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Anta Validation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ANTA validation — the assertion layer

Every other NetClaw source reads state. This one asserts on it and returns a verdict you can act on.

Server: anta-mcp (NetClaw-authored over ANTA 1.9.0, Apache-2.0, own virtualenv) · 4 tools · 1,272 tokens · 208 tests in the catalogue

Which plane answers — read this before reaching for another server

Three servers touch Arista. They answer different questions, and picking the wrong one gives a confidently wrong answer.

PlaneServerAnswers
Validationthis skilldoes the state match what it should be — pass/fail
Managementarista-cvp-mcpwhat does CloudVision say — inventory, tags, compliance as CVP sees it
Device CLIpyats-*, multivendor-cliwhat is the raw state — show-command output

Use this skill to assert, not to fetch. If the question is "what is the interface MTU", that is the CLI plane. If the question is "is the MTU what it should be", that is this one.

The verdicts — five, and they never merge

VerdictMeansNever counts as
passtested, expectation held—
failtested, expectation did not hold—
not_applicablethe feature is not configured — nothing was testedfail
skippedANTA declined to run itpass
errordevice unreachable or the run brokefail

not_applicable is the one that matters

ANTA natively reports a test for an unconfigured feature as a failure. Measured on a lab switch:

VerifyBGPPeerCount → failure
  "'show bgp summary vrf all' failed on veos1: BGP inactive"

That device has no BGP at all. Reporting it as a failure claims a BGP fault where there is no BGP. The server reclassifies it to not_applicable and keeps the original message.

When you report results: say "BGP: not applicable — this device does not run BGP", never "BGP test failed".

There is no health percentage, and you should not compute one

passed / total is meaningless when not_applicable and skipped sit in the denominator. Forty tests of which thirty are not applicable is not "25% healthy" — it is ten real answers and thirty non-answers. The server refuses to emit a percentage. Report the five counts.

Workflow

1. Find the tests (contacts no device):

anta_list_tests: category="routing.bgp"        # or keyword="ntp", or both

2. Learn what a test needs (contacts no device):

anta_describe_test: test="VerifyEOSVersion"    # returns its input schema

Do this whenever a test takes inputs. A test run without required inputs is reported as skipped with the requirements listed — it does not guess a default and silently test the wrong thing.

3. Run them:

anta_run_tests:
  host: "172.20.20.4"
  tests: ["VerifyEOSVersion", "VerifyUptime", "VerifyNTP"]
  inputs: {"VerifyEOSVersion": {"versions": ["4.36.1F"]}, "VerifyUptime": {"minimum": 3600}}

Or by category: category: "hardware".

Reading a result honestly

  • An unreachable device returns error with zero results. It is not a broken device — nothing was tested. Say "could not reach the device", never "the device failed its tests".
  • An empty selection returns no_tests_selected. No test matched. That is not a healthy device.
  • A fail names observed and expected. Quote both — "NTP expected synchronised, actual unsynchronised" is actionable; "NTP test failed" is not.

Credentials and scope

ANTA_USERNAME / ANTA_PASSWORD come from the environment and are never tool arguments and never appear in output. ANTA_VERIFY_TLS defaults to true and verifies both certificate trust and hostname. For private PKI set ANTA_CA_BUNDLE to a trusted PEM bundle. An explicit false lab override is disclosed as tls_verified=false; it does not disable SSH host-key checks. See docs/INTEGRATION-TLS-MIGRATION.md for migration.

Boundaries

  • EOS only. ANTA is Arista's framework. This is not multivendor validation, and it must not be described as such.
  • Read-only. ANTA tests; it does not configure. There is no remediation path here — if a test fails, fixing it goes through the normal change process with its CR gating.
  • On demand. This is not continuous monitoring. For "what was it doing over time", use zabbix-metrics-history.

Frequently asked questions

What does the Anta Validation AI skill do?

Validate Arista EOS network state against ANTA's pre-built 208-test catalogue, with structured pass/fail verdicts. Use for "is this switch healthy", "did my change break anything", "verify BGP/interfaces/hardware are correct", "run a health check on this device". Read-only. A test for a feature the device does not run reports not_applicable — never a failure — and no health percentage is ever emitted. For non-Arista devices, or an assertion ANTA's catalogue doesn't cover, use `pyats-dynamic-test` to author a custom aetest script instead.

Why use Anta Validation on TypingMind?

Because you install it once and use it with any model. Anta Validation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Anta Validation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/anta-validation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Anta Validation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Anta Validation?

As many as you like. As long as a model supports skills, you can use Anta Validation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Anta Validation AI skill free?

Yes. It is published on GitHub by automateyournetwork under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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