Eks Security
aws-samples/appmod-blueprints
A skill your agent uses whenever someone needs security or compliance guidance for Amazon EKS — phrased as "CIS Benchmark for EKS", "HIPAA / PCI-DSS / FedRAMP / SOC 2 / GDPR on EKS", "harden my EKS…
Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie.
Install Auto Data Discovery
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discovery --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/privacy/auto-data-discovery .claude/skills/auto-data-discovery && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .claude/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discoveryType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discovery --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/privacy/auto-data-discovery .agents/skills/auto-data-discovery && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .agents/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discovery --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/privacy/auto-data-discovery .cursor/skills/auto-data-discovery && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .cursor/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Privacy-Data-Protection-Skills.git --path skills/privacy/auto-data-discovery--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discovery --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/privacy/auto-data-discovery .gemini/skills/auto-data-discovery && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .gemini/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discoveryInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/privacy/auto-data-discovery .github/skills/auto-data-discovery && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .github/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills auto-data-discovery --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Privacy-Data-Protection-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/privacy/auto-data-discovery .opencode/skills/auto-data-discovery && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "auto-data-discovery" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/auto-data-discovery into .opencode/skills/auto-data-discovery/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-data-discovery", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
auto-data-discovery- GitHub stars
- 295
- Token cost
- ~3.3k tokens
- SKILL.md length
- 1,272 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 278
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie.
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Platform Comparison, Implementation Pattern —… and Implementation Pattern — BigID, plus 5 more sections
- Runs Python scripts from its folder; calls aws
- Tasks that involve Third-party API integration
What it does
Auto Data Discovery is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/standards.md` and `references/workflows.md`).
It sits in Legal & Compliance, covering Privacy and GDPR and Third-party API integration. It works with Amazon Web Services. The repository describes itself as: 282+ structured privacy & data protection skills for AI agents. GDPR, CCPA, EU AI Act, HIPAA, LGPD, PIPL, DPDP Act. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Privacy and GDPR
- Tasks that involve Third-party API integration
Example prompts
- “Use the auto-data-discovery skill to implement automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust…”
- “/auto-data-discovery”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 9b2ef9e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use aws, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Auto Data Discovery loads about 3.3k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 1,272 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from mukul975/Privacy-Data-Protection-Skills at commit 9b2ef9e, republished under its Apache-2.0 licence (© mukul975). 1,272 words, ~3,346 tokens.
Download SKILL.mdSave it as
.claude/skills/auto-data-discovery/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- auto-data-discovery
- description
- Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Covers scanning schedules, accuracy tuning, false positive management, and integration patterns. Keywords: data discovery, PII scanning, Purview, BigID, Macie, OneTrust, automated classification, data cataloging.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- data-classification
- metadata.tags
- data-discovery, pii-scanning, purview, bigid, macie, onetrust
Automated PII Discovery and Classification
Overview
Automated data discovery tools scan structured and unstructured data repositories to identify, classify, and catalogue personal data across the enterprise. Manual data inventories cannot keep pace with the volume, velocity, and variety of modern data processing. Automated discovery provides continuous visibility into where personal data resides, how it flows, and whether it is classified and protected according to policy. This skill covers implementation patterns for four leading platforms — Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie — with focus on scanning configuration, accuracy optimisation, and integration with privacy compliance workflows.
Platform Comparison
| Capability | Microsoft Purview | BigID | OneTrust DataDiscovery | AWS Macie |
|---|---|---|---|---|
| Structured data scanning | SQL Server, Azure SQL, Synapse, Cosmos DB, Oracle, PostgreSQL, MySQL, Teradata | 100+ connectors including all major RDBMS, NoSQL, data warehouses | 200+ connectors, pre-built integrations with SaaS applications | S3, DynamoDB, RDS (via Lambda) |
| Unstructured data scanning | SharePoint, OneDrive, Exchange, Azure Blob, Azure Files, AWS S3, GCP Storage | File shares, email, SharePoint, cloud storage, Slack, Teams, Confluence | File shares, email, cloud storage, collaboration platforms | S3 buckets (primary focus) |
| Classification method | 300+ built-in sensitive information types (SITs), trainable classifiers, exact data match (EDM), custom regex | ML-based NER, correlation analysis, pattern matching, custom classifiers | Pattern matching, NER, contextual analysis, custom rules | ML-based pattern matching, custom data identifiers, managed data identifiers |
| GDPR-specific classifiers | EU national ID formats, EU passport numbers, EU debit/credit card numbers, EU tax ID numbers per Member State | GDPR personal data taxonomy, Art. 9 special category detection, cross-regulation mapping | Pre-built GDPR data subject types, purpose mapping, lawful basis tagging | EU personal data identifiers (limited — primarily financial and identity patterns) |
| Accuracy tuning | Confidence levels (low/medium/high), custom keyword dictionaries, EDM for exact matching, document fingerprinting | ML model retraining, feedback loop, confidence thresholds, correlation rules | Confidence scoring, validation rules, exception management | Custom data identifiers with regex and keyword proximity, severity scoring |
| Deployment model | SaaS (Microsoft 365/Azure), hybrid with Purview governance | SaaS, on-premises, hybrid | SaaS, on-premises agent | AWS-native SaaS |
| Pricing model | Per information protection unit (Azure), per Microsoft 365 licence tier (E5 includes advanced) | Per data source connector, per TB scanned | Per data source module, per connector | Per S3 bucket evaluated, per GB scanned |
Implementation Pattern — Microsoft Purview
Architecture
Data Sources Microsoft Purview
┌─────────────┐ ┌───────────────────────────┐
│ Azure SQL │──────scanner──►│ Data Map (metadata store) │
│ SharePoint │──────scanner──►│ Data Catalog (search/tag) │
│ AWS S3 │──────scanner──►│ Data Estate Insights │
│ On-prem SQL │──self-hosted──►│ Information Protection │
│ Power BI │──────scanner──►│ Data Loss Prevention (DLP) │
└─────────────┘ └───────────────────────────┘Configuration Steps for Vanguard Financial Services
Step 1: Register Data Sources
- Register all Azure SQL databases, SharePoint sites, Azure Blob Storage accounts
- Install self-hosted integration runtime for on-premises SQL Server and file shares
- Register AWS S3 buckets via cross-cloud connector
- Register Power BI workspaces for report-level sensitivity scanning
Step 2: Configure Scanning Rules
- Enable built-in sensitive information types (SITs) for all EU jurisdictions:
- UK National Insurance Number (pattern:
[A-CEGHJ-PR-TW-Z]{2}\d{6}[A-D]) - EU Passport Numbers (per Member State format)
- International Banking Account Number (IBAN)
- EU Tax Identification Numbers
- EU Health Insurance Card Number
- UK National Insurance Number (pattern:
- Create custom SITs for Vanguard-specific identifiers:
- Customer account number format:
VFS-\d{10} - Employee ID format:
EMP-[A-Z]{2}\d{6} - Investment portfolio reference:
PF-\d{8}-[A-Z]{2}
- Customer account number format:
- Configure confidence levels: HIGH for direct identifiers, MEDIUM for indirect, LOW for contextual matches
- Enable exact data match (EDM) for customer name verification against CRM database
Step 3: Set Scanning Schedule
- Full scan: Monthly (first Saturday of each month, 02:00 UTC)
- Incremental scan: Weekly (every Saturday, 02:00 UTC)
- On-demand scan: Triggered by new data source registration or incident response
- Estimated scan duration: 8 hours for full scan across 15 TB of data
Step 4: Configure Sensitivity Labels
- Map Purview sensitivity labels to Vanguard classification tiers:
Public→ Purview label: PublicInternal→ Purview label: GeneralConfidential→ Purview label: ConfidentialRestricted→ Purview label: Highly Confidential (auto-applied to Art. 9/Art. 10 data)
- Enable auto-labelling policies: Apply labels based on detected SITs
- Configure label inheritance: Child items inherit parent container label unless higher
Step 5: DLP Policy Integration
- Create DLP policies triggered by sensitivity labels:
- Restricted data: Block external sharing, require encryption, notify DPO
- Confidential data: Warn on external sharing, audit log, require justification
- Internal data: Audit log only
- Configure endpoint DLP for Windows devices (prevent copy to USB, print, screenshot for Restricted content)
Accuracy Tuning for Purview
| Issue | Tuning Approach |
|---|---|
| False positive: UK phone numbers flagged as National Insurance numbers | Increase minimum confidence to HIGH for NINO SIT; add negative keyword list ("phone", "tel", "fax", "mobile") |
| False positive: Internal reference numbers flagged as account numbers | Create EDM schema for actual customer accounts; custom SIT with proximity to customer-related keywords |
| False negative: Health data in free-text email bodies | Enable trainable classifier for health content; train on sample of 50+ positive examples from occupational health correspondence |
| False negative: Genetic identifiers in research datasets | Create custom SIT for rs-number pattern (rs\d{4,12}), ICD-10 codes, and HUGO gene names |
Implementation Pattern — BigID
Architecture
BigID uses a distributed scanning architecture with correlation-based discovery:
Data Sources BigID Platform
┌─────────────┐ ┌──────────────────────────┐
│ Databases │──scan───►│ Discovery Engine │
│ File Shares │──scan───►│ Correlation Engine (ML) │
│ Cloud │──scan───►│ Classification Engine │
│ SaaS Apps │──API────►│ Catalog & Inventory │
│ Email │──scan───►│ Privacy Rights Automation │
└─────────────┘ └──────────────────────────┘Show full SKILL.md (509 more words)Show less
Key Differentiator: Correlation-Based Identity Resolution
BigID's ML-based correlation engine identifies personal data by correlating data elements across sources to build identity profiles. This approach detects personal data that pattern matching alone would miss — for example, a customer ID in one system linked to a name in another.
Configuration for Vanguard
- Data source connections: Configure connectors for Salesforce CRM, Workday HR, ADP Payroll, SharePoint, Azure SQL, AWS S3, on-premises Oracle Data Warehouse
- Correlation rules: Enable identity correlation across CRM customer ID, HR employee ID, and payroll employee number
- Classification policies: Map BigID classifiers to GDPR categories (personal data, special category, criminal data, pseudonymised, anonymised)
- Scanning schedule: Incremental scans daily at 01:00 UTC; full correlation rebuild weekly
Implementation Pattern — OneTrust DataDiscovery
Architecture
OneTrust integrates discovery with its broader privacy management platform:
Data Sources OneTrust Platform
┌─────────────┐ ┌──────────────────────────┐
│ Cloud/SaaS │──API────►│ DataDiscovery Module │
│ Databases │──agent──►│ Data Mapping (Art. 30) │
│ File Shares │──agent──►│ Assessment Automation │
│ Endpoints │──agent──►│ Consent Management │
└─────────────┘ │ DSAR Automation │
└──────────────────────────┘Key Differentiator: Privacy Program Integration
OneTrust's value proposition is tight integration between discovery results and privacy program management — discovered personal data feeds directly into Art. 30 records, DPIA assessments, and DSAR fulfilment workflows.
Configuration for Vanguard
- Data source inventory: Use OneTrust's data mapping questionnaires to establish baseline, then deploy automated scanning to validate and enrich
- Integration with RoPA: Map discovered data categories to Art. 30 processing activity records
- DSAR integration: Configure discovery results to support automated data subject access request fulfilment (locate all data for a given identity across systems)
- Vendor discovery: Scan for personal data shared with third-party processors; reconcile with vendor management records
Implementation Pattern — AWS Macie
Architecture
Macie is purpose-built for S3 data discovery within AWS:
AWS Environment
┌─────────────────────────────────────┐
│ S3 Buckets ──scan──► Macie │
│ │ │
│ EventBridge ◄──alerts──┘ │
│ Security Hub ◄──findings──┘ │
│ CloudWatch ◄──metrics──┘ │
└─────────────────────────────────────┘Configuration for Vanguard
- Bucket selection: Enable Macie for all S3 buckets in eu-west-1 (London) and eu-west-2 (Ireland) regions
- Custom data identifiers: Create identifiers for Vanguard-specific patterns (customer account numbers, employee IDs, portfolio references)
- Managed identifiers: Enable all EU-region managed data identifiers (UK NINO, IBAN, EU passport)
- Job scheduling: Scheduled classification jobs weekly; event-driven scan on new object upload for high-sensitivity buckets
- Findings integration: Route findings to AWS Security Hub for centralised monitoring; trigger Lambda function for automated label application
Scanning Schedule Best Practices
| Scan Type | Frequency | Duration Window | Trigger |
|---|---|---|---|
| Full discovery scan | Monthly | Weekend maintenance window (8-12 hours) | Scheduled |
| Incremental scan | Weekly | Off-peak hours (2-4 hours) | Scheduled |
| New source onboarding scan | On registration | Within 48 hours of source registration | Event-driven |
| Post-incident scan | As needed | Immediate (targeted scope) | Incident response |
| Pre-DPIA scan | Before DPIA commencement | 1-2 weeks before DPIA start | Project-triggered |
Accuracy Metrics and Tuning
Key Performance Indicators
| Metric | Target | Measurement Method |
|---|---|---|
| Precision (true positive rate) | > 90% | Sample 100 classified items monthly; verify classification accuracy |
| Recall (detection rate) | > 85% | Plant known PII test data in scan scope; measure detection rate |
| False positive rate | < 10% | Count items classified as personal data that are not |
| False negative rate | < 15% | Count personal data items missed by the scanner |
| Classification consistency | > 95% | Same data element classified consistently across repeat scans |
Continuous Improvement Cycle
Month 1: Baseline scan → establish initial accuracy metrics
Month 2: Review false positives/negatives → tune rules and thresholds
Month 3: Re-scan → measure improvement
Month 4: Expand scope (new data sources) → re-baseline
Month 5: Review edge cases → create custom classifiers
Month 6: Accuracy audit by DPO → formal accuracy report
[Repeat cycle]Integration Points
- data-inventory-mapping: Automated discovery feeds Art. 30 data inventory
- pii-in-unstructured: Specialized techniques for unstructured data augment platform capabilities
- data-labeling-system: Discovery results drive automated label application
- classification-policy: Discovery validates compliance with classification policy tiers
- data-lineage-tracking: Discovery combined with lineage provides complete data visibility
© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references, assets) in skills/privacy/auto-data-discovery of mukul975/Privacy-Data-Protection-Skills.
- SKILL.md
- assets/template.md
- references/standards.md
- references/workflows.md
- scripts/process.py
Open the folder on GitHubat commit 9b2ef9e
Compare with similar skills
Auto Data Discovery next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Auto Data Discovery this skillmukul975/Privacy-Data-Protection-Skills | 295 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Eks Securityaws-samples/appmod-blueprints | 113 | — | ~4.7k | Automated safety check: Pass | MIT-0 | |
| Eu Data Act Oliver Schmidt Prietzlawve-ai/awesome-legal-skills | 836 | — | ~3.9k | Automated safety check: Pass | AGPL-3.0 | |
| Healthcare Phi Complianceaffaan-m/ECC | 275k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Storage S3 Resiliency Expertiseaws/tools-for-devops-agent | 100 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Amazon Location Serviceawslabs/agent-plugins | 915 | — | ~4k | Automated safety check: Pass | Apache-2.0 |
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Related
Works with
Questions about Auto Data Discovery
What does Auto Data Discovery do?
Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie. Auto Data Discovery is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements automated PII discovery and classification using tools like Microsoft Purview, BigID, OneTrust DataDiscovery, and AWS Macie.
When should I use Auto Data Discovery?
Auto Data Discovery fits situations like: tasks that involve Privacy and GDPR; tasks that involve Third-party API integration.
How do I install Auto Data Discovery in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a claude-code`. Or copy the skill folder (skills/privacy/auto-data-discovery in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/auto-data-discovery in your project. Claude Code loads it when a task matches its description.
How do I install Auto Data Discovery in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a codex`. Or copy the skill folder (skills/privacy/auto-data-discovery in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/auto-data-discovery in your project. Codex loads it when a task matches its description.
Can I use Auto Data Discovery in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill auto-data-discovery -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto-data-discovery, .gemini/skills/auto-data-discovery, .github/skills/auto-data-discovery and .opencode/skills/auto-data-discovery in your project.
What does Auto Data Discovery need to run?
Going by SKILL.md and its folder, Auto Data Discovery needs Python for the scripts in its folder and the command-line tools its instructions call (aws). Our summary lists: Python 3.
Does Auto Data Discovery access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Auto Data Discovery safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Auto Data Discovery use?
Auto Data Discovery is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Auto Data Discovery use?
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.
What are the alternatives to Auto Data Discovery?
Skills that share tags, products or a category with Auto Data Discovery: Eks Security (aws-samples/appmod-blueprints, 113 stars), Eu Data Act Oliver Schmidt Prietz (lawve-ai/awesome-legal-skills, 836 stars), Healthcare Phi Compliance (affaan-m/ECC, 275k stars) and Storage S3 Resiliency Expertise (aws/tools-for-devops-agent, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Auto Data Discovery?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 295 GitHub stars. The repository holds 278 skills in this directory. The repository was last updated on March 16, 2026.
Source: mukul975/Privacy-Data-Protection-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.