Fortianalyzer Ops logo

Fortianalyzer Ops

Community
automateyournetwork
fortianalyzer-ops

FortiAnalyzer log operations — policy-filtered traffic log query within a bounded time window, offset pagination, per-policy activity checks, and logging-device inventory. Use when asking whether traffic actually matched a firewall rule, investigating what hit a policy, or determining whether a rule is genuinely unused.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill namefortianalyzer-ops
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 Fortianalyzer Ops 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/fortianalyzer-ops
  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/fortianalyzer-ops .claude/skills/fortianalyzer-ops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fortianalyzer Ops 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 Fortianalyzer Ops 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 Fortianalyzer Ops 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.

FortiAnalyzer Operations — the analyzer plane

MCP Server

  • Server: fortinet-mcp (NetClaw-authored, spec 080 / roadmap R3)
  • Command: $FORTINET_MCP_CMD
  • Transport: stdio · JSON-RPC over /jsonrpc (the same dialect FortiManager speaks)
  • Requires: FORTIANALYZER_HOST, FORTIANALYZER_API_TOKEN (FortiAnalyzer 7.2.2+ for token auth)
  • Mode: read-only

The one rule that matters most here

"No logs matched" is NOT "this rule is unused."

This skill exists to answer "is this rule dead?" — and that question is dangerously easy to answer wrongly. An empty result can mean:

  • nothing matched in the window you queried (retention is finite; history is not)
  • the device never forwarded logs to this analyzer at all
  • logging is disabled on the rule itself

Reporting any of those as "unused" would license someone to delete a live firewall rule. So an empty result returns the explicit outcome no_logs_in_window, never ok and never an error, with a message saying what it does and does not prove.

This is the same error class as spec 078's "no advisories ≠ not vulnerable" and spec 079's "no probes found ≠ outage", and it gets the same treatment: a separate, named outcome that cannot be silently collapsed.

Where this plane sits

QuestionPlaneSkill
"Has anything actually matched this rule?"analyzerthis skill
"What policy is intended?"managerfortimanager-ops
"What is the box running right now?"devicefortigate-ops

The manager knows a rule exists. Only the analyzer knows whether anyone ever matched it. A configured rule is not a used rule.

Tools (4, all read-only)

ToolWhat it answers
faz_query_logsTraffic logs matching a filter within a bounded window
faz_fetch_moreNext page, re-run at an offset
faz_policy_activityDid anything match policy N in this window?
faz_list_devicesWhich devices forward logs here — check this first

Time windows are mandatory and always stated

If you supply no window, the tools apply the last 24 hours and say so in scope.window_start / scope.window_end and in notes. An unbounded log query against a busy analyzer is slow, expensive, and produces a result nobody can interpret because they do not know what period it covers.

Every response echoes the window actually queried, not the one requested.

Pagination

faz_fetch_more re-runs the search at a new offset. It does not reuse FortiAnalyzer's search task id (tid), because those are single-use and expire — treating one as a durable cursor produces silent truncation, where you believe you have all the results and you have some of them.

Workflow: is this firewall rule dead?

  1. faz_list_devices — confirm the device that owns the rule actually forwards logs here. If it does not, stop: an empty result would mean nothing.
  2. faz_policy_activity with the policy id and an explicit, generous window.
  3. Read the outcome:
    • ok with sessions_matched > 0 → the rule is live. Do not remove it.
    • no_logs_in_window → nothing matched in that window. Not proof of disuse.
  4. Before concluding a rule is unused, verify: log forwarding on, retention covers the period, logging enabled on the rule itself.
  5. Only then treat it as a removal candidate — and route the actual change through fortimanager-ops, which enforces the two write gates.

Workflow: what hit this policy?

  1. faz_query_logs with filter_expr (e.g. policyid=12) and a bounded window.
  2. Page with faz_fetch_more using the returned next_offset while has_more.
  3. Correlate sources and destinations against fmg_resolve_object to check whether the traffic matches what the rule intended to permit.

Every response carries its plane and scope

jsonc
{ "plane": "analyzer",
  "scope": {"adom": "root", "window_start": "...", "window_end": "..."},
  "outcome": "no_logs_in_window", "message": "... This is NOT evidence the rule is unused." }

The window is part of the scope, not an optional detail. A log result without its window is uninterpretable, so a response that cannot state one is an error.

Integration with other skills

SkillHow they compose
fortimanager-opsFind the rule (intent), then come here for whether it was used
fortigate-opsDevice state; use together to separate "not configured" from "not used"
fwrule-analyzerShadowed rules found there + zero activity here = strong removal case
servicenow-change-workflowRule removal is a production change — CR required
gait-session-trackingEvery query here is GAIT-audited automatically

Important rules

  • Never report an empty window as "unused".
  • Always state the window you queried, including the default.
  • Check faz_list_devices before trusting silence.
  • Read-only — findings here justify a change; they never make one.

Frequently asked questions

What does the Fortianalyzer Ops AI skill do?

FortiAnalyzer log operations — policy-filtered traffic log query within a bounded time window, offset pagination, per-policy activity checks, and logging-device inventory. Use when asking whether traffic actually matched a firewall rule, investigating what hit a policy, or determining whether a rule is genuinely unused.

Why use Fortianalyzer Ops on TypingMind?

Because you install it once and use it with any model. Fortianalyzer Ops 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 Fortianalyzer Ops in TypingMind?

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

Which AI models can use Fortianalyzer Ops?

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 Fortianalyzer Ops?

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

Is the Fortianalyzer Ops 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇