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Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring.
Install Pii In Unstructured
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill pii-in-unstructured -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pii-in-unstructured --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/pii-in-unstructured .claude/skills/pii-in-unstructured && 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 "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .claude/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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/pii-in-unstructuredType 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 pii-in-unstructured -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pii-in-unstructured --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/pii-in-unstructured .agents/skills/pii-in-unstructured && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .agents/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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 pii-in-unstructured -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pii-in-unstructured --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/pii-in-unstructured .cursor/skills/pii-in-unstructured && 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 "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .cursor/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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/pii-in-unstructured--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 pii-in-unstructured -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pii-in-unstructured --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/pii-in-unstructured .gemini/skills/pii-in-unstructured && 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 "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .gemini/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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 pii-in-unstructuredInstalls 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 pii-in-unstructured -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/pii-in-unstructured .github/skills/pii-in-unstructured && 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 "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .github/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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 pii-in-unstructured -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 pii-in-unstructured --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/pii-in-unstructured .opencode/skills/pii-in-unstructured && 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 "pii-in-unstructured" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pii-in-unstructured into .opencode/skills/pii-in-unstructured/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pii-in-unstructured", 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
pii-in-unstructured- GitHub stars
- 301
- Token cost
- ~2.4k tokens
- SKILL.md length
- 764 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring.
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Unstructured Data Sources at…, Detection Architecture and OCR Integration for Scanned…, plus 4 more sections
- Runs Python scripts from its folder
What it does
Pii In Unstructured is an agent skill from mukul975/Privacy-Data-Protection-Skills. Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring. Keywords: PII detection, unstructured data, NER, spaCy, Presidio, OCR, regex, email scanning, document scanning.
Its SKILL.md is about 2.4k 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 Documents & Office, covering Privacy and GDPR. 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
Example prompts
- “Use the pii-in-unstructured skill to detect PII in unstructured data including emails, documents, images, and logs using NER-based detection with…”
- “/pii-in-unstructured”
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.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Pii In Unstructured loads about 2.4k tokens when it runs, and up to ~4.5k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 764 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). 764 words, ~2,422 tokens.
Download SKILL.mdSave it as
.claude/skills/pii-in-unstructured/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- pii-in-unstructured
- description
- Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring. Keywords: PII detection, unstructured data, NER, spaCy, Presidio, OCR, regex, email scanning, document scanning.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- data-classification
- metadata.tags
- pii-detection, unstructured-data, ner, spacy, presidio, ocr, regex
PII Detection in Unstructured Data
Overview
Unstructured data — emails, documents, images, chat logs, call transcripts, and system logs — accounts for an estimated 80% of enterprise data and presents the greatest challenge for privacy compliance. Unlike structured databases where personal data resides in known columns, unstructured data contains PII embedded in free text, attached files, scanned images, and metadata. This skill covers detection approaches using Named Entity Recognition (NER), pattern matching, OCR, and hybrid pipelines, with focus on Microsoft Presidio and spaCy as implementation frameworks.
Unstructured Data Sources at Vanguard Financial Services
| Source | Volume | PII Risk | Detection Challenge |
|---|---|---|---|
| Email (Exchange Online) | 2.1M messages/month | HIGH — names, account numbers, financial data in body and attachments | Mixed text and attachments; forwarded chains contain accumulated PII |
| SharePoint documents | 4.2TB across 1,200 sites | HIGH — contracts, KYC docs, customer correspondence | Multiple formats (docx, pdf, xlsx); embedded images |
| Teams chat | 890K messages/month | MEDIUM — casual references to customers, internal discussions | Short messages, abbreviations, context-dependent PII |
| Application logs | 50GB/day | MEDIUM — IP addresses, user IDs, error messages with PII | High volume, mixed with non-PII technical data |
| Scanned documents | 45K pages/month | HIGH — passport scans, signed contracts, medical certificates | Requires OCR; variable image quality |
| Call transcripts | 8K transcripts/month | HIGH — customers state names, account numbers, personal details | Speech-to-text errors, colloquial language |
| PDF reports | 12K documents/month | MEDIUM — financial reports may contain customer lists | Embedded tables, charts with PII labels |
Detection Architecture
Microsoft Presidio Pipeline
Presidio is an open-source PII detection and anonymisation SDK developed by Microsoft, designed for integration with enterprise data pipelines.
Input Text/Document
│
▼
┌──────────────────┐
│ Pre-processing │ Format conversion, encoding normalisation,
│ (text extract) │ OCR for images/scanned PDFs
└──────┬───────────┘
│
▼
┌──────────────────┐
│ Presidio │ Multiple recognisers run in parallel:
│ Analyzer │ - NER model (spaCy/transformers)
│ │ - Pattern recognisers (regex)
│ │ - Custom recognisers (org-specific)
│ │ - Context-aware enhancers
└──────┬───────────┘
│
▼
┌──────────────────┐
│ Confidence │ Each detection assigned confidence score
│ Scoring & │ Threshold filtering applied
│ Filtering │ Context enhancement boosts/reduces scores
└──────┬───────────┘
│
▼
┌──────────────────┐
│ Results │ PII locations, types, confidence scores
│ (structured) │ Ready for classification, redaction, or alerting
└──────────────────┘Component Details
NER Model (spaCy/Transformers):
- spaCy
en_core_web_trfmodel (transformer-based) for English NER - Detects: PERSON, ORG, GPE, DATE, MONEY, CARDINAL entities
- Custom-trained NER for financial domain entities (IBAN, account numbers)
Pattern Recognisers (Regex):
- UK National Insurance Number:
[A-CEGHJ-PR-TW-Z]{2}\d{6}[A-D] - Email address:
[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} - UK phone number:
(?:0|\+44)\d{10,11} - IBAN:
[A-Z]{2}\d{2}[A-Z0-9]{4}\d{7}([A-Z0-9]?){0,16} - Credit card:
\b(?:\d{4}[-\s]?){3}\d{4}\b(with Luhn validation) - IP address (v4):
\b(?:\d{1,3}\.){3}\d{1,3}\b - Date of birth patterns:
\b\d{2}[/-]\d{2}[/-]\d{4}\b - Vanguard account:
VFS-\d{10} - ICD-10 code:
[A-Z]\d{2}\.\d{1,4}
Context-Aware Enhancement:
- Keyword proximity: if "National Insurance" appears within 50 characters of a NINO pattern, boost confidence by 15%
- Section headers: if detected within a section titled "Personal Details" or "Patient Information", boost confidence by 10%
- Negative context: if pattern appears within code blocks, SQL queries, or configuration files, reduce confidence by 25%
OCR Integration for Scanned Documents
Pipeline for Image-Based PII Detection
Scanned Document / Image
│
▼
┌──────────────────┐
│ Pre-processing │ Deskew, denoise, contrast enhancement,
│ (image) │ resolution upscaling (if < 300 DPI)
└──────┬───────────┘
│
▼
┌──────────────────┐
│ OCR Engine │ Tesseract OCR (open-source) or
│ │ Azure AI Document Intelligence (cloud)
│ │ Output: extracted text with bounding boxes
└──────┬───────────┘
│
▼
┌──────────────────┐
│ Presidio │ Standard NER + pattern detection
│ Analyzer │ on OCR-extracted text
└──────┬───────────┘
│
▼
┌──────────────────┐
│ Confidence │ Adjust for OCR quality:
│ Adjustment │ OCR confidence < 80% → reduce PII confidence by 20%
│ │ OCR confidence > 95% → no adjustment
└──────────────────┘Document Type Detection for Vanguard
| Document Type | OCR Strategy | Expected PII |
|---|---|---|
| Passport scan | Azure AI Document Intelligence (ID document model) | Full name, DOB, nationality, passport number, photo (biometric) |
| Utility bill | General OCR + address pattern recognition | Full name, address, account number |
| Medical certificate | General OCR + health NER model | Name, diagnosis, doctor name, dates |
| Signed contract | General OCR + contract template matching | Names, addresses, financial terms, signatures |
| Cheque image | Banking-specific OCR model | Name, account number, sort code, amount |
Confidence Scoring Framework
Show full SKILL.md (308 more words)Show less
Score Composition
| Component | Weight | Description |
|---|---|---|
| Pattern match confidence | 40% | Regex pattern specificity and validation (e.g., Luhn check for credit cards) |
| NER model confidence | 30% | Model probability score for entity classification |
| Context enhancement | 20% | Keyword proximity, section header, document type |
| Source quality | 10% | OCR quality score, document resolution, text extraction confidence |
Confidence Thresholds
| Confidence Level | Score Range | Action |
|---|---|---|
| HIGH | 85-100% | Auto-classify and auto-label; include in discovery report |
| MEDIUM | 70-84% | Queue for human review; include in discovery report as pending |
| LOW | 50-69% | Log for audit; do not auto-classify; available for bulk review |
| BELOW THRESHOLD | < 50% | Suppress; do not report unless specifically queried |
Implementation Patterns
Pattern 1: Email Scanning Pipeline
# Conceptual pipeline for Exchange Online email scanning
# 1. Microsoft Graph API retrieves email messages
# 2. Extract body text (HTML → plain text conversion)
# 3. Extract attachment text (document parsing)
# 4. Run Presidio analyzer on combined text
# 5. Map findings to email metadata (sender, recipients, date)
# 6. Apply classification labels via Microsoft PurviewPattern 2: Log File Scanning
For application logs, specific patterns dominate:
- IP addresses in access logs
- User IDs and session tokens in authentication logs
- Email addresses in error messages
- Stack traces containing file paths with usernames
Log scanning requires higher false-positive tolerance and volume-optimised processing.
Pattern 3: Chat Message Scanning
Teams/Slack messages present unique challenges:
- Short messages with minimal context
- Abbreviations and informal language
- Customer names mentioned without surrounding keywords
- Account numbers shared in fragments across messages
Strategy: scan message threads rather than individual messages to capture context.
Accuracy Benchmarks
Expected Performance by Data Source
| Source | Precision Target | Recall Target | Key Challenges |
|---|---|---|---|
| Email body text | > 92% | > 88% | Forwarded chains, signatures, disclaimers |
| SharePoint documents (Office formats) | > 90% | > 85% | Embedded tables, headers/footers |
| Scanned documents (OCR) | > 85% | > 80% | OCR errors, handwriting, poor image quality |
| Application logs | > 88% | > 82% | IP address over-detection, reference number ambiguity |
| Chat messages | > 80% | > 75% | Short context, informal language, abbreviations |
| Call transcripts | > 82% | > 78% | Speech-to-text errors, overlapping speech, accents |
Integration Points
- auto-data-discovery: Unstructured PII detection complements structured data discovery platforms
- data-labeling-system: Detected PII drives automatic sensitivity label application
- classification-policy: Detection results feed into classification tier assignment
- data-inventory-mapping: Unstructured PII findings expand the data inventory to include document repositories and email systems
© 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/pii-in-unstructured 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
Pii In Unstructured 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 |
|---|---|---|---|---|---|---|
| Pii In Unstructured this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Update Legal Pdfsremotion-dev/remotion | 63k | — | ~287 | Automated safety check: Pass | Custom licence | |
| Documenso Data Handlingjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~2.2k | Automated safety check: Pass | MIT | |
| Yc SaaS Drafterlawve-ai/awesome-legal-skills | 847 | — | ~2.4k | Automated safety check: Pass | MIT | |
| Data Exportgustavscirulis/snapgrid | 116 | 1 repos | ~2.9k | Automated safety check: Notes | Custom licence | |
| Transfer Impact Assessment Tia Oliver Schmidt Prietzlawve-ai/awesome-legal-skills | 847 | — | ~4k | Automated safety check: Pass | AGPL-3.0 |
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Related
Categories
Questions about Pii In Unstructured
What does Pii In Unstructured do?
Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring. Pii In Unstructured is an agent skill from mukul975/Privacy-Data-Protection-Skills. Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring.
When should I use Pii In Unstructured?
Pii In Unstructured fits situations like: tasks that involve Privacy and GDPR.
How do I install Pii In Unstructured in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill pii-in-unstructured -a claude-code`. Or copy the skill folder (skills/privacy/pii-in-unstructured in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/pii-in-unstructured in your project. Claude Code loads it when a task matches its description.
How do I install Pii In Unstructured in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill pii-in-unstructured -a codex`. Or copy the skill folder (skills/privacy/pii-in-unstructured in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/pii-in-unstructured in your project. Codex loads it when a task matches its description.
Can I use Pii In Unstructured 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 pii-in-unstructured -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pii-in-unstructured, .gemini/skills/pii-in-unstructured, .github/skills/pii-in-unstructured and .opencode/skills/pii-in-unstructured in your project.
What does Pii In Unstructured need to run?
Going by SKILL.md and its folder, Pii In Unstructured needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Pii In Unstructured 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 Pii In Unstructured 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 Pii In Unstructured use?
Pii In Unstructured 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 Pii In Unstructured use?
About 2.4k tokens (SKILL.md is roughly 9.7k 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 2.1k tokens, read only when the agent opens those files.
What are the alternatives to Pii In Unstructured?
Skills that share tags, products or a category with Pii In Unstructured: Update Legal Pdfs (remotion-dev/remotion, 63k stars), Documenso Data Handling (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Yc SaaS Drafter (lawve-ai/awesome-legal-skills, 847 stars) and Data Export (gustavscirulis/snapgrid, 116 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pii In Unstructured?
mukul975 (a GitHub user) maintains it in mukul975/Privacy-Data-Protection-Skills, which has 301 GitHub stars. The repository holds 280 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.