Persistence Surface Assessment logo

Persistence Surface Assessment

Organization
zero-lab-ai
persistence-surface-assessment

Measures local Windows and Linux endpoint persistence exposure from supplied evidence by deriving consumer, authority, lifecycle, prevention, context, telemetry, and coverage state. Use when auditing durable-execution risk without creating, activating, testing, ranking, or removing a persistence mechanism.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namepersistence-surface-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 Persistence Surface 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/persistence-surface-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/persistence-surface-assessment .claude/skills/persistence-surface-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Persistence Surface 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 Persistence Surface 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 Persistence Surface 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.

Persistence surface assessment

Persistence exposure is not proof that persistence exists, executed, survived, was approved, or is malicious. Measure whether current supplied evidence closes the prerequisites for durable local execution while keeping every assessed surface opaque.

This skill is read-only and local-endpoint only. It does not enumerate a live host, reveal concrete locations, create or rank mechanisms, execute triggers, change configuration, test survival, clean artifacts, evade telemetry, inspect directory or cloud control planes, handle credentials, or assess kernel, bootkit, firmware, or hidden-object integrity.

When to Use

  • Auditing durable-execution risk without creating, activating, testing, ranking, or removing a persistence mechanism.
  • 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 exact written authority, scoped Windows or Linux assets, the supplied-evidence boundary, assessment time, collection windows, and prohibited active actions.
  2. Normalize evidence with one asset, source group, opaque locator, digest status, observed time or bounded window, validity, provenance, limitations, and custody. Do not invent precision, names, ownership, hashes, or independence.
  3. Cover every required platform surface. Each asset-category cell is assessed, blocked, or not-applicable; one empty surface never proves asset-wide absence.
  4. Represent each existing consumer only by an opaque surface reference and closed category. Keep consumer availability, effective change authority, lifecycle survival, preventive control, execution-context relation, and telemetry state as separate evidence-backed facts.
  5. Require current same-asset evidence for every non-unknown fact. Evidence from one source group remains one vantage even when several parsers report it. Stale, unavailable, or disposed material cannot prove current exposure.
  6. Keep execution separate from exposure. Execution needs an exact configuration-to-artifact-to- event binding in a compatible window; an existing registration is not compromise or success.
  7. Mark higher-context exposure for a privilege-escalation handoff without claiming elevation. If host-view integrity is suspect, derive inconclusive and require rootkit triage.
  8. Create only closed defensive remediation objectives and owned specialist handoffs. Rank fix urgency, never mechanism usefulness. A fix is complete only from post-change evidence.
  9. Derive all surface states, coverage, priorities, and summaries. Preserve blocked, unknown, conditional, conflicting, and not-applicable work rather than converting silence to clean.

Boundary with adjacent skills

  • privilege-escalation-assessment owns vertical transitions and achieved identity.
  • rootkit-triage owns hidden artifacts, compromised inspection layers, and boot or kernel integrity.
  • credential-exposure-audit owns secret material, validity, privilege, and handling.
  • memory-forensics-triage owns supplied volatile images; memory representation is not durability.
  • directory-persistence-audit owns directory-wide identities, policy, delegation, and control paths.
  • Cloud attack-path workflows own IAM, workload identity, provider schedulers, and control planes.
  • Detection workflows own detector creation and authorized technique-to-alert validation.
  • findings-report owns final claim-bearing stakeholder prose.

Rationalizations to reject

  • "It is writable, so persistence is confirmed." Consumer, survival, and prevention still bind.
  • "The ticket or signature makes it approved." Neither binds the current exact configuration.
  • "The scheduler returned success." A result code does not prove the intended artifact executed.
  • "The process name matches." Names do not bind configuration, artifact, event, or context.
  • "One empty location means the host is clean." Preserve the full platform denominator.
  • "Read authority permits a harmless test." Triggers, restarts, logons, reboots, and edits are active.
  • "Show the exact location so remediation is easier." Keep this artifact opaque and hand off safely.
  • "Rank the most reliable option." Rank remediation urgency, never persistence utility.
  • "Disposed evidence still proves the state." Preserve its disposition receipt, not a current claim.
  • "Unknown means low risk." Unknown prerequisites yield conditional or inconclusive exposure.

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 Persistence Surface Assessment AI skill do?

Measures local Windows and Linux endpoint persistence exposure from supplied evidence by deriving consumer, authority, lifecycle, prevention, context, telemetry, and coverage state. Use when auditing durable-execution risk without creating, activating, testing, ranking, or removing a persistence mechanism.

Why use Persistence Surface Assessment on TypingMind?

Because you install it once and use it with any model. Persistence Surface 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 Persistence Surface 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/persistence-surface-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 Persistence Surface 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 Persistence Surface Assessment?

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

Is the Persistence Surface 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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