HIPAA Safe Harbor Coverage Audit
maziyarpanahi/openmed
Checks OpenMed de-identified clinical text against the 18 HIPAA Safe Harbor identifier categories and reports gaps and residual re-identification risk.
Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations.
Install Pia Health Data
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill pia-health-data -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pia-health-data --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/pia-health-data .claude/skills/pia-health-data && 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 "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .claude/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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/pia-health-dataType 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 pia-health-data -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pia-health-data --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/pia-health-data .agents/skills/pia-health-data && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .agents/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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 pia-health-data -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pia-health-data --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/pia-health-data .cursor/skills/pia-health-data && 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 "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .cursor/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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/pia-health-data--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 pia-health-data -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills pia-health-data --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/pia-health-data .gemini/skills/pia-health-data && 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 "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .gemini/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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 pia-health-dataInstalls 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 pia-health-data -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/pia-health-data .github/skills/pia-health-data && 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 "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .github/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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 pia-health-data -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 pia-health-data --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/pia-health-data .opencode/skills/pia-health-data && 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 "pia-health-data" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/pia-health-data into .opencode/skills/pia-health-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pia-health-data", 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
pia-health-data- GitHub stars
- 301
- Token cost
- ~2.3k tokens
- SKILL.md length
- 1,068 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations.
- Works in 9 steps: Electronic Health Records (EHR) → Clinical Research → Health Wearables and mHealth Apps → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Regulatory Framework, Health Data Processing Scenarios and DPIA Methodology for Health Data, plus 1 more section
- Runs Python scripts from its folder
What it does
Pia Health Data is an agent skill from mukul975/Privacy-Data-Protection-Skills. Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations. Covers special category data safeguards, clinical research data, patient portals, health wearables, genetic data, and cross-border health data transfers. Keywords: health data PIA, DPIA, Article 9, HIPAA, special category data, clinical research, patient privacy, genetic data.
Its SKILL.md is about 2.3k 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, Health and fitness tracking and Clinical and healthcare research. 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 Health and fitness tracking
- Tasks that involve Clinical and healthcare research
Example prompts
- “Use the pia-health-data skill to conduct Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific…”
- “/pia-health-data”
Requirements
- Python 3
Workflow steps
9 steps, taken from the step headings in SKILL.md.
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
Pia Health Data loads about 2.3k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 108 tokens; SKILL.md has 1,068 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). 1,068 words, ~2,270 tokens.
Download SKILL.mdSave it as
.claude/skills/pia-health-data/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- pia-health-data
- description
- Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations. Covers special category data safeguards, clinical research data, patient portals, health wearables, genetic data, and cross-border health data transfers. Keywords: health data PIA, DPIA, Article 9, HIPAA, special category data, clinical research, patient privacy, genetic data.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- privacy-impact-assessment
- metadata.tags
- pia, health-data, article-9, hipaa, special-category, clinical-research
Privacy Impact Assessment for Health Data
Overview
Health data processing triggers mandatory DPIA requirements under GDPR Article 35(3)(b) (processing on a large scale of special categories of data referred to in Article 9(1)). The EDPB in WP248rev.01 identifies health data processing as meeting multiple DPIA-triggering criteria: special category data (C5), vulnerable data subjects (C7), and often innovative use or applying new technological or organisational solutions (C8). This skill provides a structured PIA methodology specific to health data processing across clinical, research, wearable, and digital health contexts.
Regulatory Framework
GDPR Article 9 — Special Category Data
Health data falls within the special categories of personal data under Article 9(1). Processing is prohibited unless one of the Article 9(2) exceptions applies:
| Exception | Application to Health Data |
|---|---|
| Art. 9(2)(a) Explicit consent | Patient consent for clinical care beyond treatment necessity; health app consent |
| Art. 9(2)(b) Employment obligations | Occupational health assessments, fitness-to-work evaluations |
| Art. 9(2)(c) Vital interests | Emergency medical treatment when data subject cannot consent |
| Art. 9(2)(h) Health care provision | Medical diagnosis, treatment, health system management by health professionals under secrecy obligations |
| Art. 9(2)(i) Public health | Epidemiological surveillance, disease registries, pharmacovigilance |
| Art. 9(2)(j) Scientific research | Clinical trials, health research with appropriate safeguards under Art. 89(1) |
HIPAA Privacy Rule (US)
The HIPAA Privacy Rule (45 CFR Part 160, 164) governs the use and disclosure of Protected Health Information (PHI) by covered entities (health plans, health care clearinghouses, health care providers) and business associates. Key privacy requirements include:
- Minimum necessary standard for PHI use and disclosure
- Individual access rights to PHI within 30 days
- Accounting of disclosures of PHI
- De-identification standards: Expert Determination (45 CFR 164.514(b)(1)) and Safe Harbor (45 CFR 164.514(b)(2))
- Business Associate Agreements (BAAs) for third-party processors
Sector-Specific Regulations
| Regulation | Scope |
|---|---|
| EU Clinical Trials Regulation (536/2014) | Personal data in clinical trial conduct and reporting |
| UK Data Protection Act 2018 Schedule 1 | Health data processing conditions for UK-based organisations |
| 42 CFR Part 2 (US) | Substance use disorder treatment records — stricter than HIPAA |
| HITECH Act (US) | Breach notification requirements for health data; strengthened HIPAA enforcement |
| eHealth Network guidelines | Cross-border exchange of health data within the EU |
Health Data Processing Scenarios
1. Electronic Health Records (EHR)
Data elements: Medical history, diagnoses, medications, lab results, imaging, clinician notes, patient demographics. Key risks: Unauthorised access by non-treating staff, insufficient access controls, data retention beyond clinical necessity, secondary use for research without consent or legal basis. Mitigation: Role-based access control, audit logging, break-glass procedures with post-access review, encryption at rest and in transit, purpose-bound access policies.
2. Clinical Research
Data elements: Study participant identifiers, health measurements, biospecimens, genomic data, adverse event reports. Key risks: Re-identification from research datasets, consent scope creep (using data beyond original study purpose), international transfers to non-adequate jurisdictions. Mitigation: Pseudonymisation with key separation, Data Access Committees, informed consent with granular options, data sharing agreements with re-identification prohibitions.
3. Health Wearables and mHealth Apps
Data elements: Heart rate, blood pressure, sleep patterns, activity levels, glucose levels, medication adherence, location data. Key risks: Continuous monitoring creating comprehensive health profiles, data sharing with third-party advertisers, insufficient user control over data, insecure data transmission. Mitigation: Privacy by design (on-device processing where possible), granular consent for data sharing, transparency about all data recipients, secure API design, data minimisation.
4. Genetic and Genomic Data
Data elements: DNA sequence data, genetic test results, family health history, polygenic risk scores. Key risks: Uniquely identifying and irrevocable (cannot be changed), impacts on biological relatives who did not consent, insurance and employment discrimination, law enforcement access. Mitigation: Purpose limitation (prohibit use for insurance underwriting or employment decisions where legally required), access restrictions, separate storage from clinical records, genetic counselling before data collection.
DPIA Methodology for Health Data
Show full SKILL.md (459 more words)Show less
Phase 1: Processing Inventory (Week 1)
- Identify all health data processing activities and map data flows.
- Classify health data by sensitivity tier: routine clinical data, sensitive diagnoses (mental health, HIV, substance use), genetic/genomic data.
- Identify all data processors and sub-processors handling health data.
- Verify Business Associate Agreements (HIPAA) or Data Processing Agreements (GDPR) are in place.
- Document the lawful basis and Article 9(2) exception for each processing activity.
Phase 2: Necessity and Proportionality (Week 2)
- For each processing activity, assess whether the purpose could be achieved with less data.
- Evaluate anonymisation or pseudonymisation alternatives.
- Assess data retention periods against clinical, legal, and research requirements.
- Review access controls: principle of least privilege, need-to-know basis.
- Evaluate whether aggregate or statistical data could serve the purpose instead of individual-level health data.
Phase 3: Risk Assessment (Week 3)
- Assess risks to data subjects from each processing activity.
- Apply the severity factors: sensitivity of health condition (stigma, discrimination risk), vulnerability of data subjects (patients, children, elderly), volume of records, identifiability.
- Assess likelihood factors: threat landscape (insider threats, ransomware targeting health sector), control maturity, regulatory scrutiny.
- Score risks on the standard likelihood x severity matrix.
Phase 4: Mitigation and Controls (Week 4)
- For each identified risk, define technical and organisational measures.
- Implement health-sector-specific controls: clinical audit trails, break-glass access, pseudonymisation key management, encrypted backup.
- Assess residual risk after mitigation.
- If residual risk remains high, consider prior consultation with the supervisory authority (Art. 36) or abandoning the processing activity.
Phase 5: Documentation and Review (Week 5)
- Document the DPIA in the required format (Art. 35(7)): systematic description of processing, necessity and proportionality assessment, risk assessment, measures to address risks.
- Obtain sign-off from DPO, Caldicott Guardian (UK NHS), Chief Medical Information Officer, or equivalent.
- Schedule periodic review (at least annually for health data processing, or on trigger events).
- File with supervisory authority if required (prior consultation).
Enforcement Precedents
- Finnish DPA vs Pihlajalinna (2021): EUR 608,000 fine for inadequate access controls to patient records in a private healthcare company; employees accessed patient records without clinical justification.
- Dutch DPA vs OLVG Hospital (2021): EUR 440,000 fine for insufficient access controls to patient medical records; inadequate two-factor authentication and insufficient log review.
- Portuguese DPA vs Hospital do Barreiro (2018): EUR 400,000 fine for excessive access to patient data; 985 users had profiles allowing access to clinical information, but only 296 were physicians.
- Swedish DPA vs Capio St Goran Hospital (2020): EUR 30,000 fine for insufficient access controls and logging in EHR system.
- HHS OCR vs Anthem Inc (2018): USD 16 million HIPAA settlement for data breach affecting 78.8 million individuals due to insufficient access controls and risk analysis.
© 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/pia-health-data 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
Pia Health Data 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 |
|---|---|---|---|---|---|---|
| Pia Health Data this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Clinical Reportsdavila7/claude-code-templates | 33k | 11 repos | ~9.9k | Automated safety check: Notes | MIT | |
| Audit Reportharness/harness-skills | 115 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Anne WojcickiK-Dense-AI/mimeographs | 129 | — | ~1.5k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Pia Health Data
What does Pia Health Data do?
Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations. Pia Health Data is an agent skill from mukul975/Privacy-Data-Protection-Skills. Conducts Privacy Impact Assessment for health data processing under GDPR Article 9, HIPAA, and sector-specific health privacy regulations.
When should I use Pia Health Data?
Pia Health Data fits situations like: tasks that involve Privacy and GDPR; tasks that involve Health and fitness tracking; tasks that involve Clinical and healthcare research.
How do I install Pia Health Data in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill pia-health-data -a claude-code`. Or copy the skill folder (skills/privacy/pia-health-data in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/pia-health-data in your project. Claude Code loads it when a task matches its description.
How do I install Pia Health Data in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill pia-health-data -a codex`. Or copy the skill folder (skills/privacy/pia-health-data in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/pia-health-data in your project. Codex loads it when a task matches its description.
Can I use Pia Health Data 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 pia-health-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pia-health-data, .gemini/skills/pia-health-data, .github/skills/pia-health-data and .opencode/skills/pia-health-data in your project.
What does Pia Health Data need to run?
Going by SKILL.md and its folder, Pia Health Data needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Pia Health Data 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 Pia Health Data 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 Pia Health Data use?
Pia Health Data 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 Pia Health Data use?
About 2.3k tokens (SKILL.md is roughly 9.1k 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 1.7k tokens, read only when the agent opens those files.
What are the alternatives to Pia Health Data?
Skills that share tags, products or a category with Pia Health Data: HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Hipaa Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars), Clinical Reports (davila7/claude-code-templates, 33k stars) and Audit Report (harness/harness-skills, 115 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pia Health Data?
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.