Command Channel Defense Assessment logo

Command Channel Defense Assessment

Organization
zero-lab-ai
command-channel-defense-assessment

Assesses supplied defensive evidence for prevention, visibility, detection, containment, and recovery coverage against opaque command-channel scenarios. Use when reviewing command-and-control exposure or control gaps without designing infrastructure, live tests, or operational guidance.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namecommand-channel-defense-assessment
Stars
232
Forks
14
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Command Channel Defense Assessment 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/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/command-channel-defense-assessment
  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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/network-protocol-security/skills/command-channel-defense-assessment .claude/skills/command-channel-defense-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Command Channel Defense Assessment 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 Command Channel Defense Assessment 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 Command Channel Defense Assessment 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.

Command channel defense assessment

Measure defensive assurance against supplied opaque scenarios. This is an evidence review, not a design, simulation, validation plan, or authorization.

Never represent topology, components, services, channels, endpoints, transports, infrastructure, implementation recommendations, or rule, query, or configuration bodies. Never supply commands, live identifiers, credentials, payloads, evasion, deployment, or testing guidance.

When to Use

  • Reviewing command-and-control exposure or control gaps without designing infrastructure, live tests, or operational guidance.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Record one assessment cutoff and engagement window. Bind the governing policy and opaque scope reference.
  2. Record environment and control fingerprints without names, addresses, products, services, or implementation details.
  3. Classify the environment. Production review is passive and sanitized only. Active or imported validation evidence must bind an isolated-lab or dedicated-test-tenant with an active-validation receipt.
  4. Keep three independent tracks per coverage cell: configuration is unknown, absent, or present; health is unknown, degraded, or healthy; validation is not-tested, inconclusive, supported, or gap.
  5. Create exactly one cell for each scenario and applicable objective: prevent, observe, detect, contain, and recover.
  6. Keep policy, configuration, health, observation, detection, containment, recovery, and validation evidence separate.
  7. Bind every cited item to the exact scenario, bundle digest, environment and control fingerprints, scope, policy, validation receipt, assessment window, and custody record.
  8. Derive the cell state from the three tracks. Configuration or health evidence without validation is evidence-only, never validation-supported.
  9. Record observed validation gaps as immutable open facts. A later fix creates a new ledger; it does not rewrite the record that observed the gap.
  10. Use not-applicable only with a closed policy reason and current policy evidence. Missing coverage is not non-applicability.
  11. Record defensive handoffs only: evidence refresh, control review, or gap remediation. A handoff is neither approval nor completion.
  12. Resolve contradictions without changing scenario identity or adding operational detail.

Derivation rules

  • unassessed: all three tracks are unknown or not tested.
  • evidence-only: configuration or health is known, but validation is not.
  • validation-supported: current exact validation supports the objective.
  • gap-observed: current exact validation demonstrates a gap and an immutable gap record exists.
  • inconclusive: current exact validation is inconclusive.
  • stale: prior validation is expired, disposed, or invalidated by drift.
  • not-applicable: current exact policy evidence supports an allowed reason.
  • A scenario is validated only when every applicable objective is currently validation-supported. One stale, gap, inconclusive, or unassessed cell limits the aggregate.

Report

Return opaque scenario IDs, per-objective derived states, aggregate counts, open gap IDs, defensive handoffs, and blocking reason codes. Do not quote restricted values from rejected input.

Boundary

  • Receive scenario IDs and taxonomy receipts from an upstream assessment.
  • Send authorized active validation to a separate isolated-lab workflow.
  • Send gaps to detection, containment, or recovery owners without prescribing implementations.
  • Treat PASS as consistency only, never authenticity, effectiveness, approval, completion, or authority.

Rationalizations to reject

  • “Configured means covered.” Presence does not prove health or outcome.
  • “Healthy telemetry proves detection.” Visibility and detection are separate claims with separate evidence.
  • “No alert proves a gap.” Silence without exact observation and validation evidence is inconclusive.
  • “Production is the most realistic test.” This skill permits passive, sanitized production review only.
  • “An imported result applies here.” It must bind the exact scenario, non-production fingerprint, control fingerprint, scope, policy, and window.
  • “The control name is unchanged.” Fingerprint drift invalidates prior claims.
  • “We fixed the gap, so close it here.” Preserve the observed fact and open a later assessment.
  • “The handoff approves the work.” A handoff transfers defensive work; it never grants authority or attests completion.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Command Channel Defense Assessment AI skill do?

Assesses supplied defensive evidence for prevention, visibility, detection, containment, and recovery coverage against opaque command-channel scenarios. Use when reviewing command-and-control exposure or control gaps without designing infrastructure, live tests, or operational guidance.

Why use Command Channel Defense Assessment on TypingMind?

Because you install it once and use it with any model. Command Channel Defense Assessment 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 Command Channel Defense Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/command-channel-defense-assessment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Command Channel Defense Assessment?

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 Command Channel Defense Assessment?

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

Is the Command Channel Defense Assessment AI skill free?

Yes. It is published on GitHub by zero-lab-ai 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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