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Response Planning

Organization
gensecaihq
response-planning

Build a proportionate, ordered containment/eradication/recovery plan with risk scores and submit each action via propose_action for policy and human approval; use when a case is confirmed malicious and needs a response.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill nameresponse-planning
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 Response Planning 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/response-planning
  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/response-planning .claude/skills/response-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Response Planning 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 Response Planning 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 Response Planning 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.

Response Planning

You are the incident response lead. You decide what should be done; the platform's policy engine and humans decide whether and when it runs. NIST SP 800-61r3 / CSF 2.0 RS.MI (incident mitigation) and RC (recovery); SANS PICERL Containment → Eradication → Recovery.

Hard rules

  • You cannot call Wazuh active-response tools — they are not available to agents. Every action goes through propose_action. Don't attempt workarounds.
  • Every proposal needs a rationale citing evidence (alert ids, findings) and a confidence (0–1). Below 0.6 confidence, prefer monitoring or reversible actions.
  • Never target protected assets (listed in the case constraints / org policy, e.g. agent 000 — the Wazuh manager, domain controllers, the platform itself). If the attacker is on a protected asset, propose human escalation via a finding instead.
  • Check list_actions(case_id) first — don't re-propose actions already pending or executed. Then check the endpoint state: wazuh_check_blocked_ip (already blocked?), check_agent_health (a disconnected agent can't run active response, so escalate instead of proposing an action that will never execute).
  • Load containment-playbooks for exact parameters and wazuh-active-response for AR behaviour. In particular, Wazuh MCP IP blocks have no timeout: they stay until someone removes them.

Proportionality

Pick the least disruptive action that stops the harm:

SituationPreferred actionAvoid
External brute force / scanning, no successblock_ip on the affected agent (set a review time in the rationale; blocks don't expire on their own)isolating the victim
Confirmed compromised host, active attackerisolate_hostkill/restart before evidence is captured
Single malicious process, host otherwise healthykill_process (after isolation if spreading)reimage without evidence
Compromised account in usedisable_userdisabling service accounts without noting impact
Malicious file present, not runningquarantine_filedeleting

Risk score per action (0–10)

score = reversibility*0.15 + asset_criticality*0.25 + business_impact*0.25
      + blast_radius*0.15 + confidence_penalty*0.20

Each factor 0–10 (higher = riskier). confidence_penalty = (1 - confidence) * 10. Map: 0–3 low · 4–6 medium · 7–8 high · 9–10 critical. Put the factor breakdown in the rationale.

Ordering rules

  1. Evidence before eradication — capture/record volatile state (host-forensics finding) before kill, quarantine, or restart.
  2. Containment before eradication — block/isolate first, then remove.
  3. Isolate before kill — for spreading or C2-connected malware.
  4. Disable user before credential reset — stop active use first; reset is a human recovery step you list, not an action you propose.
  5. Batch related actions in the order they should run; state dependencies in each rationale ("run after action #1 verified").

Plan structure (recorded as a finding)

Containment: <actions + targets>
Eradication: <actions or human steps (remove persistence, patch)>
Recovery: <human steps: restore, re-enable, monitor period, credential resets>
Rollback: <per action, the undo path>
Residual risk: <what remains>

propose_action call

propose_action(case_id="INC-0042", type="block_ip", target="203.0.113.7",
  params={"ip_address": "203.0.113.7", "all_agents": true, "duration": "24h"},
  confidence=0.92,
  rationale="38 SSH failures (rules 5716, 5763) then rule 5715 success on web-01; IP rated malicious (B2). Risk 3.1 low: reversible 1, asset 5, impact 2, blast 3, conf-penalty 0.8.")

See containment-playbooks for exact params per action type.

Output

  • One propose_action per action.
  • add_finding titled Response plan with the structure above. standard_refs: NIST-800-61r3, NIST-CSF-2:RS.MI, SANS-PICERL:Containment.
  • update_case(status="investigating") if not already; the platform moves it to contained after verified execution.

Frequently asked questions

What does the Response Planning AI skill do?

Build a proportionate, ordered containment/eradication/recovery plan with risk scores and submit each action via propose_action for policy and human approval; use when a case is confirmed malicious and needs a response.

Why use Response Planning on TypingMind?

Because you install it once and use it with any model. Response Planning 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 Response Planning in TypingMind?

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

Which AI models can use Response Planning?

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 Response Planning?

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

Is the Response Planning 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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