Data Classification Assistant logo

Data Classification Assistant

Organization
Amazon-Quick
data-classification-assistant

Classifies data assets by sensitivity level (Public, Internal, Confidential, Restricted) and generates handling guidelines, retention policies, and access control recommendations. Validates classification against organizational policy and flags misclassifications. Use when asked to 'classify this data', 'what sensitivity level', 'data handling requirements', 'classification review', or 'label this dataset'.

Overview

PublisherAmazon-Quick
RepositoryAmazon-Quick-official-catalog
Skill namedata-classification-assistant
Stars
50
Forks
4
Bundled files
1
LicenseMIT-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.

  • 1 bundled files

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

  • Open source

    Published by Amazon-Quick on GitHub. Read the source before you install it.

Installation

Install the Data Classification Assistant 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/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/security-compliance/data-classification-assistant
  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/Amazon-Quick/Amazon-Quick-official-catalog.git /tmp/Amazon-Quick-official-catalog
mkdir -p .claude/skills
cp -r /tmp/Amazon-Quick-official-catalog/skills/security-compliance/data-classification-assistant .claude/skills/data-classification-assistant
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Classification Assistant 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 Data Classification Assistant 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 Data Classification Assistant 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.

Overview

Classifies data assets into one of four sensitivity levels based on organizational policy, regulatory context, and data content characteristics. Produces a structured classification decision record with handling guidelines, retention requirements, and access control recommendations. Validates existing labels and flags misclassifications with justification.

Workflow

<Definition - Sensitivity Levels> Four-tier classification scheme from least to most restrictive:

  • Public: Information explicitly approved for external distribution. No access restrictions required. Examples: published press releases, marketing materials on public websites, open-source documentation, public financial filings.
  • Internal: Information intended for general employee use but not for external release. Examples: internal announcements, org charts, general process documentation, non-sensitive meeting notes, internal tool guides.
  • Confidential: Information that could cause material harm to the organization or individuals if disclosed. Access limited to those with a documented business need. Examples: pre-release financial data, customer PII, employee performance records, proprietary algorithms, vendor contracts, strategic roadmaps.
  • Restricted: Information whose unauthorized disclosure would cause severe harm, regulatory violation, or legal liability. Strictest controls required. Examples: authentication credentials, encryption keys, regulated health records (PHI/HIPAA), payment card data (PCI-DSS), trade secrets, M&A materials, government classified information. </Definition - Sensitivity Levels>

<Definition - Handling Requirements> Controls and procedures required at each sensitivity level:

  • Public: No special handling. Standard backup and version control. No access restrictions. Retention per records schedule.
  • Internal: Store on corporate-managed systems only. No sharing to personal accounts or external parties without review. Standard access controls (corporate authentication). Retain per department schedule, default 3 years.
  • Confidential: Encryption at rest and in transit required. Access granted by data owner approval only. Audit logging of access events. Sharing requires DLP-approved channels. Retain per regulatory or contractual obligation, default 7 years. Annual access review required.
  • Restricted: Encryption at rest and in transit with organization-managed keys. Multi-factor authentication required for access. Real-time access monitoring and alerting. No copies outside approved vaults. Data loss prevention controls mandatory. Retain per specific regulatory mandate. Quarterly access review required. Incident response plan documented. </Definition - Handling Requirements>

<Workflow - Classification description="End-to-end data classification flow from intake through decision record delivery." tools=[file_read, file_read_pdf, file_write, open_in_session_tab] triggers=["classify this data", "what sensitivity level", "data handling requirements", "classification review", "label this dataset"]

  1. [Ask user] Gather the data description. If the user has not provided sufficient detail, ask about: data contents (field types, not actual values), source system, intended audience, geographic scope, regulatory context, and whether the data is derived from other classified assets. If a current_label was provided, note it for validation in step 5. If fails: If the description lacks the detail needed to classify, re-ask the user for the specific missing attributes before proceeding.

  2. [Agent] If policy_docs were provided, read them using file_read or file_read_pdf. Extract the relevant classification criteria, level definitions, and any domain-specific rules. Index key sections for citation in the decision record. If fails: If a policy document cannot be read, report the specific file and proceed using the built-in level definitions, noting that organizational policy was not available for validation.

  3. [Think] Evaluate the data description against the sensitivity level definitions. Consider each level from Public upward. Identify the lowest level whose handling requirements would adequately protect the asset. Apply Rule 2 (classify UP) if the assessment is ambiguous. Check for regulatory triggers (PII, PHI, PCI, financial data, trade secrets) that mandate a minimum level.

  4. [Think] Determine handling requirements, retention policy, and access control recommendations appropriate to the assigned level. Cross-reference with organizational policy if available. Note any requirements that exceed the standard level definition due to regulatory or contractual obligations.

  5. [Decide] If current_label was provided, compare it to the assessed level.

    • If they match: Note agreement in the decision record. No flag needed.
    • If current label is LOWER than assessed level: Flag as potential misclassification. Recommend immediate reclassification and escalation to the data owner.
    • If current label is HIGHER than assessed level: Note the discrepancy but do not recommend downgrade without user confirmation per Rule 6. Present the case for review.
  6. [Agent] Generate the classification decision record using the template below. Write it to a Markdown file using file_write, then present it to the user using open_in_session_tab. If fails: If the record cannot be written or opened, report the error, retry once, and present the record inline if the retry fails.

  7. [Ask user] Present the classification decision for review. Ask if the user wants to adjust scope, request clarification on any flags, or finalize the record. If fails: If the user does not respond with a clear decision, re-present the record and ask whether to adjust, clarify, or finalize.

  8. [Agent] If the user confirms, finalize the record. If adjustments were requested, return to the relevant step and regenerate. Save the final version. If fails: If the final version cannot be saved, report the write error and retry once, then present the record inline if the retry fails.

</Workflow - Classification>

<Template - Classification Decision Record>

markdown
# Data Classification Decision Record

## Asset Information
- **Asset name:** {{asset_name}}
- **Description:** {{data_description}}
- **Source system:** {{source_system}}
- **Data owner:** {{data_owner}}
- **Classification date:** {{date}}
- **Classified by:** AI-assisted (requires data owner approval)

## Classification Decision
- **Assigned level:** {{sensitivity_level}}
- **Confidence:** {{High | Medium - classify UP applied}}
- **Policy basis:** {{policy_section_or_principle}}

## Rationale
{{Narrative explanation of why this level was assigned, including regulatory triggers and any ambiguity considerations.}}

## Handling Requirements
- **Storage:** {{storage_requirements}}
- **Encryption:** {{encryption_requirements}}
- **Access control:** {{access_control_model}}
- **Sharing:** {{approved_sharing_channels}}
- **Audit:** {{audit_logging_requirements}}

## Retention Policy
- **Retention period:** {{retention_period}}
- **Retention basis:** {{regulatory_or_policy_basis}}
- **Disposal method:** {{disposal_method}}

## Access Control Recommendations
- **Approved roles:** {{list_of_roles_or_groups}}
- **Authentication:** {{authentication_requirements}}
- **Review cadence:** {{access_review_frequency}}

## Flags and Notes
{{Any misclassification flags, temporal sensitivity notes, jurisdiction considerations, or items requiring data owner review.}}

## Approval
- **Status:** Pending data owner review
- **Approved by:** _______________
- **Approval date:** _______________

</Template - Classification Decision 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 Data Classification Assistant AI skill do?

Classifies data assets by sensitivity level (Public, Internal, Confidential, Restricted) and generates handling guidelines, retention policies, and access control recommendations. Validates classification against organizational policy and flags misclassifications. Use when asked to 'classify this data', 'what sensitivity level', 'data handling requirements', 'classification review', or 'label this dataset'.

Why use Data Classification Assistant on TypingMind?

Because you install it once and use it with any model. Data Classification Assistant 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 Data Classification Assistant in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Amazon-Quick/Amazon-Quick-official-catalog/tree/main/skills/security-compliance/data-classification-assistant. 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 Data Classification Assistant?

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 Data Classification Assistant?

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

Is the Data Classification Assistant AI skill free?

Yes. It is published on GitHub by Amazon-Quick under the MIT-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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