Password Audit Plan logo

Password Audit Plan

Organization
zero-lab-ai
password-audit-plan

Plans authorized offline password-strength audits across supplied hash datasets while enforcing compute budgets, progressive pass order, cohort coverage, aggregate-only reporting, and restricted handling. Use when preparing or documenting password cracking without online authentication, spraying, stuffing, replay, or credential reuse testing.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namepassword-audit-plan
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 Password Audit Plan 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/password-audit-plan
  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/password-audit-plan .claude/skills/password-audit-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Password Audit Plan 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 Password Audit Plan 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 Password Audit Plan 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.

Password audit plan

Offline password auditing is a measured experiment over supplied material. Algorithm, hardware, candidate space, runtime, and cohort denominators determine what the result means. Preserve unknowns until measurement exists.

This skill plans and records offline analysis. It does not collect hashes, expose raw material, generate organization-derived candidates, authenticate, spray, stuff, replay, test reuse, contact accounts, or access target systems.

When to Use

  • Preparing or documenting password cracking without online authentication, spraying, stuffing, replay, or credential reuse testing.
  • 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 authorization, offline-only boundary, compute and storage ceilings, prohibited sources and actions, stop conditions, workspace, retention, owner, and disposition evidence.
  2. Inventory each dataset using opaque IDs. Record source class, record and unique digest counts, acquisition-time precision, algorithm claim, validation state, cost parameters, integrity, and limitations. Never place raw hashes in the plan.
  3. Preserve record counts and digest counts separately. Deduplicate work while keeping the mapping needed to expand aggregate recoveries back to record and cohort counts.
  4. Define cohorts with explicit denominators and overlap rules. Disabled, privileged, service, administrator, and standard populations remain separate where supplied.
  5. Plan format validation and a measured benchmark before recovery passes. Do not invent throughput, runtime, keyspace completion, or recoveries.
  6. Order bounded passes from generic baseline through generic rules, bounded masks, and optional adaptive work. Each pass names dependencies, candidate-source class, dataset, GPU budget, expected measurement, and stop conditions.
  7. Let the review reject budget over-allocation, recovery before validation and benchmark, prohibited candidate sources, online actions, unproved results, and inconsistent cumulative counts.
  8. Record pass results as counts and rates only. Attribute each unique recovery to its first successful pass; expand record recovery and cohort recovery from the preserved mapping without exposing plaintext.
  9. Analyze aggregate length, character-class, duplicate, pattern-label, cohort, policy shape, and marginal-yield metrics. A policy document is a requirement, not proof a recovered password complied when created.
  10. Track raw datasets, recovered material, potfiles, restore state, generated candidates, logs, and exports through disposition. Report only approved aggregate artifacts and opaque IDs.
  11. Cover format, deduplication, cohorts, benchmark, baseline, rules, masks, adaptive work, handling, and reporting for every dataset. Blocked work remains visible.

Boundary with adjacent skills

  • Use credential-exposure-audit to inventory possible credential exposure without cracking or decrypting.
  • Use password-audit-plan only after offline material and authority are supplied.
  • Use findings-report after aggregate audit results are proven.
  • Online authentication, spraying, stuffing, reuse checks, and account contact require a separate capability and authorization; they are outside this skill.

Rationalizations to reject

  • "The manifest says NTLM, so mode selection is settled." Validate structure before spending compute or interpreting results.
  • "Deduplication reduced 1,000 accounts to 860 hashes." It reduced work items, not the reporting population. Expand results back to records and cohorts.
  • "The benchmark should be about this fast." Hardware labels and published speeds are not measurements of this dataset and configuration.
  • "Eight hours means every pass gets an equal share." Allocate from measured cost, expected evidence value, and explicit priorities; record the rationale.
  • "Recovered means valid." Offline equality says nothing about current account state, usability, privilege, or successful authentication.
  • "Examples make the pattern finding clearer." Plaintext examples and reusable candidate strings do not belong in the assessment record or report.
  • "The potfile is just tool state." It contains recovered evidence and inherits the same storage, access, retention, and deletion rules.
  • "One more pass cannot hurt." The compute ceiling and stop conditions are part of authorization, even when the hardware is idle.

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 Password Audit Plan AI skill do?

Plans authorized offline password-strength audits across supplied hash datasets while enforcing compute budgets, progressive pass order, cohort coverage, aggregate-only reporting, and restricted handling. Use when preparing or documenting password cracking without online authentication, spraying, stuffing, replay, or credential reuse testing.

Why use Password Audit Plan on TypingMind?

Because you install it once and use it with any model. Password Audit Plan 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 Password Audit Plan 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/password-audit-plan. 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 Password Audit Plan?

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 Password Audit Plan?

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

Is the Password Audit Plan 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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