Binary Fault Validation Readiness logo

Binary Fault Validation Readiness

Organization
zero-lab-ai
binary-fault-validation-readiness

Governs an exact current binary-fault-triage assessment result and recorded validation-readiness handoff for separate isolated-lab review. Use when checking defensive authority, isolation, budget, stop, cleanup, custody, and independent-review readiness without analyzing faults, creating tests or inputs, determining exploitability, or authorizing execution or development.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namebinary-fault-validation-readiness
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 Binary Fault Validation Readiness 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/binary-forensics/skills/binary-fault-validation-readiness
  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/binary-forensics/skills/binary-fault-validation-readiness .claude/skills/binary-fault-validation-readiness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Binary Fault Validation Readiness 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 Binary Fault Validation Readiness 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 Binary Fault Validation Readiness 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.

Binary fault validation readiness

When to Use

  • Checking defensive authority, isolation, budget, stop, cleanup, custody, and independent-review readiness without analyzing faults, creating tests or inputs, determining exploitability, or authorizing execution or development.
  • 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.

Purpose

Validate a supplied closed readiness ledger and produce, at most, a non-authorizing handoff for separate isolated validation review. A complete ledger establishes internal consistency only; it does not approve, perform, or describe validation.

Boundary

Never inspect or analyze a binary fault. Never execute, mutate, fuzz, debug, disassemble, reproduce, or validate anything. Never create a test, input, exploit, payload, proof of concept, command, pseudocode, debugger syntax, address, offset, register, instruction, gadget, memory layout, primitive, bypass, path, or target. Never determine exploitability, severity, success, or validation outcome. Never authorize execution, exploit development, or use of a production environment.

Workflow

  1. Accept only a supplied exact binary-fault-triage/v2 assessment result whose review status is passed and whose upstream handoff is recorded validation-readiness.
  2. Stop on operational or restricted content. Do not quote rejected values.
  3. Verify the canonical imported receipt over the complete opaque binary, build, architecture, provenance, fault, input, environment, observation, artifact, review, policy, and upstream handoff bindings.
  4. Record only supplied passive sanitized receipts. Never contact an artifact, source, lab, target, or downstream operator.
  5. Instantiate all nine essential readiness checks. None is not-applicable.
  6. Keep authority actions limited to readiness-review and handoff-recording; neither grants execution authority.
  7. Require every satisfied check to carry one canonical role-specific subject proof. Require an isolated non-production lab, exact numeric ceilings, exact stop-pretest proof, ready cleanup, retained custody, and genuinely independent review lineage.
  8. Derive gate precedence as prohibited, triage-refresh-required, approval-required, blocked, then handoff-ready.
  9. Treat handoff-ready as readiness for separate review only. It is not execution permission, validation success, or a technical conclusion.
  10. Keep handoffs exact-bound and chronological. Null ownership derives withheld; a recorded occurrence requires an opaque owner and later receipt.
  11. Derive withdrawal after authority expiry or revocation, bound drift, custody disposal, budget exhaustion, a fired stop, or cleanup loss. Require a later exact trigger-bound receipt for withdrawn.

Report

State the ledger identifier, contradictions, exact gate and handoff counts, blocker gate IDs, and withdrawal handoff IDs. Add: “Internal readiness consistency only; no fault analysis, validation, execution authority, exploitability determination, or development authorization occurred.”

Rationalizations to reject

  • “The triage review passed, so run a quick confirmation.” The upstream receipt starts governance only; this skill never validates.
  • “Finding-ready means exploitable.” Triage explicitly does not establish exploitability, and this ledger cannot add that claim.
  • “The input digest is available, so reconstruct the input.” Digests bind identity only; restricted artifacts stay opaque.
  • “The lab is isolated, so authority is implied.” Isolation and authority are independent mandatory checks.
  • “A handoff-ready gate authorizes the operator.” It permits only separate review; downstream execution authority is outside this skill.
  • “A stopped or expired package can be resumed.” No retry or resume state exists; refresh the evidence and establish a new readiness record.

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 Binary Fault Validation Readiness AI skill do?

Governs an exact current binary-fault-triage assessment result and recorded validation-readiness handoff for separate isolated-lab review. Use when checking defensive authority, isolation, budget, stop, cleanup, custody, and independent-review readiness without analyzing faults, creating tests or inputs, determining exploitability, or authorizing execution or development.

Why use Binary Fault Validation Readiness on TypingMind?

Because you install it once and use it with any model. Binary Fault Validation Readiness 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 Binary Fault Validation Readiness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/binary-forensics/skills/binary-fault-validation-readiness. 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 Binary Fault Validation Readiness?

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 Binary Fault Validation Readiness?

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

Is the Binary Fault Validation Readiness 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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