Action Verification logo

Action Verification

Organization
gensecaihq
action-verification

Verify on-host effect of executed containment actions with the wazuh_check_* tools, record verified/failed/unknown, and recommend rollback or retry; use after the platform reports an action executed.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill nameaction-verification
Stars
61
Forks
15
Bundled files
Instructions only
LicenseMIT
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 gensecaihq on GitHub. Read the source before you install it.

Installation

Install the Action Verification 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/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/action-verification
  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/gensecaihq/Wazuh-Autopilot.git /tmp/Wazuh-Autopilot
mkdir -p .claude/skills
cp -r /tmp/Wazuh-Autopilot/backend/app/skills/action-verification .claude/skills/action-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Action Verification 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 Action Verification 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 Action Verification 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.

Action Verification

A successful Wazuh API call means the active-response command was dispatched, not that it worked on the host. The agent may be disconnected, the AR script missing, or the attacker may have re-established access. Verify every executed action.

Procedure

  1. list_actions(case_id) — find actions with status executed (or verified: null).
  2. For each, call the matching check tool with the same target parameters. Know what each check actually looks at. None of them read live host state, so treat a positive as strong evidence, not proof:
Action typeCheck toolWhat it inspects (Wazuh MCP Server v4.3.0)Verified when
block_ip / firewall_drop / host_denywazuh_check_blocked_ip(ip_address, agent_id)active-response alerts (rule.groups: active_response) with data.srcip = IP in the last 24h, e.g. rule 651 "Host Blocked by firewall-drop Active Response" or rule 653 (host-deny)blocked: true (matching_alerts > 0)
isolate_hostwazuh_check_agent_isolation(agent_id)a recent active-response alert mentioning isolation for the agent. Connection status is not a signal: an isolated host keeps its manager linkisolation_confirmed: true
kill_processwazuh_check_process(agent_id, process_id)syscollector process inventory for that PID. Periodic, not live: compare inventory_scan_time to the execution timerunning: false with a scan time after execution
disable_userwazuh_check_user_status(agent_id, username)active-response alerts mentioning the user in the last 24h, classified disable/enable. A heuristic, not order-awarelikely_disabled: true
quarantine_filewazuh_check_file_quarantine(agent_id, file_path)the latest FIM "deleted" alert (rule 553) for the exact path. Needs the path under FIM monitoring and the Indexer configuredquarantined: true
restart_wazuhcheck_agent_health(agent_id)agent status and last keepaliveagent active with a keepalive after execution

Wazuh writes the AR alerts only after the agent runs the script, so an AR alert (rules 651, 653, or rule 657 "Active response: - ") is better evidence than the API's dispatch acceptance. null or "inconclusive" results are common when the Indexer isn't configured on the MCP server. Report them as unknown, not failed. Load wazuh-active-response for how AR scripts and timeouts behave. 3. If the check is inconclusive, check_agent_health — a disconnected agent can't apply or report AR; say "unknown", not "failed". 4. Look for effect evidence too: get_wazuh_alerts after execution time. Did the blocked IP keep generating alerts (for example more rule 5710 from the same data.srcip)? Did the killed process reappear in get_agent_processes? A block that was undone shows up as rule 652 "Host Unblocked by firewall-drop Active Response", usually from an AR timeout. 5. Wait-and-recheck guidance: AR is asynchronous; if the first check is negative within 2 minutes of execution, report pending and recommend a re-check rather than failure.

Outcomes

OutcomeCriteriaRecommendation
verifiedcheck positive and no continued attacker activitycase can move toward contained
partialcheck positive but attacker activity continues (other IPs, other hosts)hand off to response-planner for additional containment
failedcheck negative after re-check, agent healthyretry once; if still failing, escalate to a human (AR script missing?)
unknownagent disconnected / check tool erroredescalate; don't mark contained

Dry-run and demo runs

If the action was executed in dry-run mode, the check tools reflect the real host, so a negative result is expected. Report not applicable (dry run) instead of failed.

Harm check

If the action caused business impact (service down, legitimate users blocked — look for new alerts or human comments), recommend rollback with the rollback tool named in containment-playbooks. Rollback itself is a human-approved action.

Output

  • add_finding titled Verification: <action type> <target> with check tool, raw result summary, timestamps, outcome, and recommendation. standard_refs: NIST-800-61r3, NIST-CSF-2:RS.MI.
  • update_case(status="contained") only when all containment actions for the case are verified.

Frequently asked questions

What does the Action Verification AI skill do?

Verify on-host effect of executed containment actions with the wazuh_check_* tools, record verified/failed/unknown, and recommend rollback or retry; use after the platform reports an action executed.

Why use Action Verification on TypingMind?

Because you install it once and use it with any model. Action Verification 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 Action Verification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/action-verification. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Action Verification?

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 Action Verification?

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

Is the Action Verification AI skill free?

Yes. It is published on GitHub by gensecaihq under the MIT 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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