Tech Contract Negotiation Patrick Munro
lawve-ai/awesome-legal-skills
Systematic contract negotiation strategies for technology services agreements with German/EU law specificity.
Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins.
Install New Tech Pia
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill new-tech-pia -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills new-tech-pia --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/new-tech-pia .claude/skills/new-tech-pia && 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 "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .claude/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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/new-tech-piaType 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 new-tech-pia -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills new-tech-pia --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/new-tech-pia .agents/skills/new-tech-pia && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .agents/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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 new-tech-pia -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills new-tech-pia --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/new-tech-pia .cursor/skills/new-tech-pia && 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 "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .cursor/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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/new-tech-pia--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 new-tech-pia -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills new-tech-pia --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/new-tech-pia .gemini/skills/new-tech-pia && 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 "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .gemini/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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 new-tech-piaInstalls 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 new-tech-pia -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/new-tech-pia .github/skills/new-tech-pia && 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 "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .github/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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 new-tech-pia -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 new-tech-pia --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/new-tech-pia .opencode/skills/new-tech-pia && 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 "new-tech-pia" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/new-tech-pia into .opencode/skills/new-tech-pia/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "new-tech-pia", 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
new-tech-pia- GitHub stars
- 301
- Token cost
- ~3.3k tokens
- SKILL.md length
- 1,498 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins.
- Works in 5 steps: Technology Understanding (Week 1) → Stakeholder Impact Mapping (Week 2) → Proportionality Assessment (Week 3) → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Technology-Specific Privacy…, PIA Methodology for Emerging… and Privacy-by-Design Requirements…, plus 1 more section
- Runs Python scripts from its folder
What it does
New Tech Pia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.
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 Research & Science, covering Privacy and GDPR, Quantum computing and Vendor and procurement management. 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 Quantum computing
- Tasks that involve Vendor and procurement management
Example prompts
- “Use the new-tech-pia skill to guide privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and…”
- “/new-tech-pia”
Requirements
- Python 3
Workflow steps
5 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
New Tech Pia loads about 3.3k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 118 tokens; SKILL.md has 1,498 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). 1,498 words, ~3,300 tokens.
Download SKILL.mdSave it as
.claude/skills/new-tech-pia/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- new-tech-pia
- description
- Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- privacy-impact-assessment
- metadata.tags
- pia, emerging-technology, iot, blockchain, ar-vr, quantum-computing
Assessing New Technology Privacy Impact
Overview
Article 35(1) of the GDPR explicitly references new technologies as a factor increasing the likelihood of high risk to data subjects' rights and freedoms. The EDPB in WP248rev.01 identifies innovative use of technology as one of nine criteria triggering a DPIA. This skill provides a structured PIA methodology for emerging technologies where the privacy implications are not yet fully understood, including Internet of Things (IoT), blockchain and distributed ledger technologies, augmented and virtual reality (AR/VR), quantum computing, digital twins, brain-computer interfaces, and ambient computing.
Technology-Specific Privacy Risk Profiles
Internet of Things (IoT)
| Risk Area | Description | Privacy Impact |
|---|---|---|
| Pervasive data collection | IoT devices continuously collect environmental and behavioural data, often without visible indicators | Data subjects may be unaware of the scope of data collection; transparency obligations under Art. 13-14 are difficult to fulfil on devices without screens |
| Data minimisation challenges | Sensors often collect more data than needed for the immediate purpose to enable future analytics | Violation of Art. 5(1)(c) data minimisation; purpose creep through accumulated data |
| Device-to-device communication | IoT ecosystems share data between devices without user awareness | Unexpected recipients; difficulty identifying all processors and sub-processors |
| Insecure by default | Many IoT devices ship with default credentials, unencrypted communications, and no update mechanism | Art. 25 data protection by design and Art. 32 security of processing obligations not met |
| Location and behavioural tracking | Connected devices reveal location patterns, daily routines, and behavioural habits | Systematic monitoring (WP248 C3); profiling risk (WP248 C1) |
| Cross-device correlation | Data from multiple IoT devices can be combined to create comprehensive behavioural profiles | Matching or combining datasets (WP248 C6); disproportionate surveillance |
EDPB Guidelines 02/2023 on IoT and Wearable Devices: Emphasized that IoT devices must implement data protection by design, provide clear privacy notices (adapted to device constraints), and enable genuine consent mechanisms.
Blockchain and Distributed Ledger Technology
| Risk Area | Description | Privacy Impact |
|---|---|---|
| Immutability vs right to erasure | Blockchain's append-only nature conflicts with Art. 17 right to erasure | Technical inability to delete personal data recorded on-chain |
| Transparency of transactions | Public blockchains expose transaction data to all participants | Personal data potentially accessible to unlimited recipients |
| Pseudonymity not anonymity | Blockchain addresses are pseudonymous but can be linked to real identities through transaction analysis | Re-identification risk; Art. 4(5) pseudonymisation does not equal anonymisation |
| Controller identification | Decentralised governance makes it difficult to identify the data controller | Art. 26 joint controller arrangements may be needed; accountability unclear |
| Cross-border by design | Distributed ledger nodes are typically located across multiple jurisdictions | Chapter V international transfer obligations triggered |
| Smart contract automation | Smart contracts execute automatically without human intervention | Art. 22 automated decision-making implications when smart contracts affect individuals |
CNIL Blockchain Guidance (2018): Recommended storing personal data off-chain with only hashes on-chain; using commitment schemes; designating participants who decide to use blockchain as controllers.
Augmented and Virtual Reality (AR/VR)
| Risk Area | Description | Privacy Impact |
|---|---|---|
| Biometric data collection | Eye tracking, facial expressions, body movements, voice patterns | Art. 9 special category data (biometric data for identification); Art. 35(3)(b) trigger |
| Spatial mapping | AR/VR devices scan and map physical environments including private spaces | Collection of data about third parties present in the environment without their consent |
| Behavioural profiling | Gaze tracking reveals interests, attention patterns, and cognitive state | Highly personal data; evaluation and scoring (WP248 C1) |
| Immersive manipulation | VR environments can influence behaviour through environmental design | Art. 5(1)(a) fairness; potential for subliminal manipulation |
| Persistent identity | Avatar and behavioural biometrics create persistent identifiable profiles | Long-term tracking across virtual environments |
| Child safety | Minors using VR platforms face enhanced risks | Vulnerable data subjects (WP248 C7); Art. 8 child consent requirements |
Quantum Computing
| Risk Area | Description | Privacy Impact |
|---|---|---|
| Cryptographic vulnerability | Quantum computers may break current encryption standards (RSA, ECC) | Art. 32 security measures based on current encryption become insufficient |
| Retroactive decryption | Encrypted data harvested today can be decrypted when quantum computers mature (harvest now, decrypt later) | Data currently protected may become exposed; long-term confidentiality compromised |
| Enhanced data analytics | Quantum machine learning can process data at scales impossible for classical computers | New forms of profiling and inference; privacy-preserving techniques may be defeated |
| Post-quantum migration | Transitioning to quantum-resistant cryptography requires significant infrastructure changes | Interim vulnerability period during migration |
ENISA Post-Quantum Cryptography Report (2024): Recommended organisations begin quantum risk assessment and plan migration to NIST-standardised post-quantum algorithms (ML-KEM, ML-DSA, SLH-DSA).
Digital Twins
| Risk Area | Description | Privacy Impact |
|---|---|---|
| Comprehensive data aggregation | Digital twins aggregate data from multiple sources to create a virtual replica | Matching or combining datasets (WP248 C6); comprehensive profiling |
| Predictive modelling of individuals | Digital twins of patients or employees predict future states and behaviours | Evaluation and scoring (WP248 C1); Art. 22 implications for predictions affecting individuals |
| Continuous synchronisation | Real-time data feeds maintain the digital twin's accuracy | Systematic monitoring (WP248 C3); proportionality concerns |
| Blurred anonymisation boundary | Even without direct identifiers, a sufficiently detailed digital twin may be re-identifiable | Pseudonymisation vs anonymisation assessment required |
PIA Methodology for Emerging Technologies
Phase 1: Technology Understanding (Week 1)
- Document the technology's data processing characteristics:
- What data is collected (categories, volume, frequency)
- How data flows through the technology stack
- Where data is stored and processed (geographic locations)
- Who has access to data at each stage
- How long data is retained
- What security measures protect data at each stage
- Identify the technology's novel risk characteristics that distinguish it from established technologies.
- Map the technology against the EDPB WP248rev.01 nine criteria.
- Consult published regulatory guidance specific to the technology (EDPB, CNIL, ICO, ENISA).
Show full SKILL.md (605 more words)Show less
Phase 2: Stakeholder Impact Mapping (Week 2)
- Identify all affected data subjects:
- Direct users of the technology
- Third parties whose data may be incidentally collected
- Bystanders (particularly relevant for IoT, AR/VR, spatial computing)
- Identify vulnerable data subjects (children, employees, patients, elderly).
- Map potential impacts on each stakeholder group:
- Privacy impacts (loss of control, unexpected processing)
- Autonomy impacts (manipulation, reduced choice)
- Equality impacts (discriminatory outcomes, digital exclusion)
- Safety impacts (physical harm from decisions based on technology)
- Document data subject expectations versus actual processing.
Phase 3: Proportionality Assessment (Week 3)
For each data processing element of the technology:
| Assessment Question | Analysis Required |
|---|---|
| Is this processing necessary for the stated purpose? | Document why the technology cannot achieve its purpose without this data |
| Could the purpose be achieved with less data? | Evaluate data minimisation alternatives (aggregation, sampling, synthetic data) |
| Could the purpose be achieved with less identifying data? | Evaluate anonymisation, pseudonymisation, and differential privacy options |
| Could the purpose be achieved with a less invasive technology? | Compare the proposed technology against established alternatives |
| Are the benefits proportionate to the privacy intrusion? | Balancing test: public interest vs individual privacy impact |
| Have data subjects been consulted on the acceptability of the intrusion? | Art. 35(9) data subject views; user acceptance research |
Phase 4: Risk Assessment and Mitigation (Week 4-5)
- For each identified risk, assess likelihood and severity using the standard DPIA risk matrix.
- Identify technology-specific mitigation measures:
- Privacy-enhancing technologies (PETs): differential privacy, homomorphic encryption, secure multi-party computation, federated learning
- Data protection by design: privacy-preserving defaults, granular consent, purpose-bound processing
- Transparency mechanisms adapted to technology constraints (audio notices for screenless IoT, visual indicators for AR/VR)
- Assess residual risk after mitigation.
- If residual risk remains high, consider whether the technology should be deployed at all, not just how to mitigate risks.
Phase 5: Ongoing Monitoring Framework (Week 5-6)
- Define monitoring metrics specific to the technology:
- Data collection volumes (detect scope creep)
- Access patterns (detect unauthorised use)
- Accuracy and reliability of technology outputs
- User complaints and feedback
- Establish review triggers:
- Technology update or version change
- New use case or feature added
- Security vulnerability discovered
- Regulatory guidance issued for the technology
- Peer data breach involving similar technology
- Define a sunset plan: how will personal data be handled if the technology is decommissioned?
Privacy-by-Design Requirements for Emerging Technologies
| Principle | Implementation for Emerging Tech |
|---|---|
| Proactive not reactive | Conduct PIA before technology deployment, not after incidents |
| Privacy as default | Technology must ship with privacy-protective defaults; opt-in for additional data collection |
| Privacy embedded in design | Privacy requirements must be part of the technology specification, not bolt-on |
| Full functionality | Privacy protections should not degrade the technology's core functionality |
| End-to-end security | Data protection from collection through deletion, including inter-device communication |
| Visibility and transparency | Clear indicators when technology is collecting data; accessible privacy notices |
| Respect for user privacy | User-centric design; genuine choice and control over personal data |
Enforcement Precedents
- Spanish AEPD vs CaixaBank (2021): EUR 6 million fine for deploying new profiling technology (behavioural analytics for targeted financial products) without adequate DPIA for innovative technology use.
- ICO vs Clearview AI (2022): GBP 7.5 million fine for novel facial recognition technology scraping public images without lawful basis or DPIA.
- CNIL vs Amazon Europe (2020): EUR 35 million fine for deploying cookie tracking technology without valid consent on the Amazon.fr website.
- Italian Garante vs Foodinho/Glovo (2021): EUR 2.5 million fine for deploying algorithmic management technology (AI-driven shift allocation for riders) without DPIA.
- Belgian DPA vs IAB Europe (2022): EUR 250,000 fine for the Transparency and Consent Framework (TCF) used in programmatic advertising technology; found that TCF constituted processing of personal data without adequate controller designation.
© 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/new-tech-pia 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
New Tech Pia 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 |
|---|---|---|---|---|---|---|
| New Tech Pia this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Tech Contract Negotiation Patrick Munrolawve-ai/awesome-legal-skills | 847 | — | ~4.9k | Automated safety check: Pass | AGPL-3.0 | |
| Operational Designmagnus919/agent-skills | 115 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Regulatory Deal Card Generator Patrick Munrolawve-ai/awesome-legal-skills | 847 | — | ~2.1k | Automated safety check: Pass | AGPL-3.0 | |
| AI Governance Reviewer Carl Ditzlerlawve-ai/awesome-legal-skills | 847 | — | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Operational Designmagnus919/hermes-profiles | 289 | — | ~1.3k | Automated safety check: Pass | MIT |
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Related
Questions about New Tech Pia
What does New Tech Pia do?
Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. New Tech Pia is an agent skill from mukul975/Privacy-Data-Protection-Skills. Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins.
When should I use New Tech Pia?
New Tech Pia fits situations like: tasks that involve Privacy and GDPR; tasks that involve Quantum computing; tasks that involve Vendor and procurement management.
How do I install New Tech Pia in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill new-tech-pia -a claude-code`. Or copy the skill folder (skills/privacy/new-tech-pia in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/new-tech-pia in your project. Claude Code loads it when a task matches its description.
How do I install New Tech Pia in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill new-tech-pia -a codex`. Or copy the skill folder (skills/privacy/new-tech-pia in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/new-tech-pia in your project. Codex loads it when a task matches its description.
Can I use New Tech Pia 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 new-tech-pia -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/new-tech-pia, .gemini/skills/new-tech-pia, .github/skills/new-tech-pia and .opencode/skills/new-tech-pia in your project.
What does New Tech Pia need to run?
Going by SKILL.md and its folder, New Tech Pia needs Python for the scripts in its folder. Our summary lists: Python 3.
Does New Tech Pia 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 New Tech Pia 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 New Tech Pia use?
New Tech Pia 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 New Tech Pia 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 2.9k tokens, read only when the agent opens those files.
What are the alternatives to New Tech Pia?
Skills that share tags, products or a category with New Tech Pia: Tech Contract Negotiation Patrick Munro (lawve-ai/awesome-legal-skills, 847 stars), Operational Design (magnus919/agent-skills, 115 stars), Regulatory Deal Card Generator Patrick Munro (lawve-ai/awesome-legal-skills, 847 stars) and AI Governance Reviewer Carl Ditzler (lawve-ai/awesome-legal-skills, 847 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains New Tech Pia?
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.