Firmware Hardware Assessment logo

Firmware Hardware Assessment

Organization
zero-lab-ai
firmware-hardware-assessment

Tracks embedded-device and firmware assessment coverage across exact units, acquisition methods, debug interfaces, stored secrets, update trust, radios, physical safety, and delegated external systems. Use when planning, conducting, reviewing, or handing over an authorized hardware or IoT penetration test.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namefirmware-hardware-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 Firmware Hardware 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/application-endpoint-security/skills/firmware-hardware-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/application-endpoint-security/skills/firmware-hardware-assessment .claude/skills/firmware-hardware-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Firmware Hardware 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 Firmware Hardware 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 Firmware Hardware 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.

Firmware and hardware assessment

A visible debug pad is not usable debug access. Matching flash reads prove a repeatable acquisition, not authentic firmware. A signature block or downgrade string does not prove enforcement. Keep each conclusion tied to the exact unit, artifact, action, and evidence.

When to Use

  • Planning, conducting, reviewing, or handing over an authorized hardware or IoT penetration test.
  • 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 model, serial, hardware revision, reported firmware, and whether each device is sacrificial and authorized for damage.
  2. State allowed action classes, in-scope external systems, and constraints. Treat voltage, power state, fixtures, and irreplaceable calibration as safety boundaries, not informal notes.
  3. Bind photographs, measurements, logs, captures, and analysis to a device. Keep secret values out of summaries and report attachments.
  4. Record each acquired artifact with its SHA-256, source, acquisition, and evidence. Preserve repeated reads as separate artifacts.
  5. Derive acquisition consistency from artifact hashes. Matching reads establish repeatability only from the first recorded read onward.
  6. Compare vendor and installed images by partitions and normalized content. Record per-device configuration and calibration before interpreting unequal whole-image hashes.
  7. Inventory one device surface per unit, then components, debug interfaces, storage, firmware images, update mechanisms, local and network services, radios, and external systems.
  8. Cover device identity, acquisition integrity, debug access, firmware analysis, credential material, update trust, communications, integration, supply chain, and physical safety. Preserve planned, blocked, delegated, and inconclusive work.
  9. Use finding only for an executed, evidenced case. Limit claim scope to the tested unit unless evidence from additional devices supports a broader claim.
  10. Delegate companion applications, vendor cloud, brokers, update services, and production systems. Discovery does not authorize contact.

Interpretation rules

  • A password prompt is a positive control for that interface, not proof of password strength or restricted post-authentication privilege.
  • A locked root account says nothing about other support identities.
  • A recovered private key proves extractability from that artifact. Fleet-wide reuse needs evidence from more than one unit.
  • An apparent signature block is an indicator until verification code, trust anchor, signed regions, and failure behavior are established.
  • An update error string does not prove rollback prevention.
  • Interface pads prove physical presence. Pinout, lifecycle state, fuses, authentication, and effective access remain separate questions.
  • Firmware version labels do not prove byte equivalence.

Rationalizations to reject

  • "The hashes differ, so the installed firmware was tampered with." Device configuration, calibration, logs, and writable state can explain the delta.
  • "Two reads match, so the firmware is authentic." They prove acquisition repeatability, not origin or trust.
  • "JTAG pads exist, so JTAG is enabled." Usability depends on pinout, electrical state, lifecycle controls, and authorization to interrogate it.
  • "The package is signed, so updates are secure." Enforcement and coverage of every security-sensitive field still need evidence.
  • "The credential is in one unit, so the whole fleet shares it." A single-device observation cannot establish provisioning behavior.
  • "A sacrificial fixture makes destructive work authorized." Written action scope and explicit damage authorization are both required.

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 Firmware Hardware Assessment AI skill do?

Tracks embedded-device and firmware assessment coverage across exact units, acquisition methods, debug interfaces, stored secrets, update trust, radios, physical safety, and delegated external systems. Use when planning, conducting, reviewing, or handing over an authorized hardware or IoT penetration test.

Why use Firmware Hardware Assessment on TypingMind?

Because you install it once and use it with any model. Firmware Hardware 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 Firmware Hardware Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/application-endpoint-security/skills/firmware-hardware-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 Firmware Hardware 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 Firmware Hardware Assessment?

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

Is the Firmware Hardware 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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