C15t
c15t/c15t
Work with c15t consent management docs, APIs, and integrations for Next.js, React, and JavaScript.
Implements federated learning architecture patterns for GDPR compliance.
Install AI Federated Learning
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-federated-learning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-federated-learning --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/ai-federated-learning .claude/skills/ai-federated-learning && 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 "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .claude/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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/ai-federated-learningType 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 ai-federated-learning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-federated-learning --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/ai-federated-learning .agents/skills/ai-federated-learning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .agents/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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 ai-federated-learning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-federated-learning --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/ai-federated-learning .cursor/skills/ai-federated-learning && 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 "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .cursor/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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/ai-federated-learning--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 ai-federated-learning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills ai-federated-learning --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/ai-federated-learning .gemini/skills/ai-federated-learning && 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 "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .gemini/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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 ai-federated-learningInstalls 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 ai-federated-learning -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/ai-federated-learning .github/skills/ai-federated-learning && 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 "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .github/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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 ai-federated-learning -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 ai-federated-learning --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/ai-federated-learning .opencode/skills/ai-federated-learning && 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 "ai-federated-learning" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/ai-federated-learning into .opencode/skills/ai-federated-learning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-federated-learning", 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
ai-federated-learning- GitHub stars
- 301
- Token cost
- ~3.1k tokens
- SKILL.md length
- 1,337 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Implements federated learning architecture patterns for GDPR compliance.
- Works in 6 steps: Gradient leakage: Model updates can leak… → Model memorization: The aggregated model… → Right to erasure: Removing a… → …
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, Federated Learning…, Secure Aggregation Protocols and Differential Privacy Integration, plus 4 more sections
- Runs Python scripts from its folder
What it does
AI Federated Learning is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.
Its SKILL.md is about 3.1k 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, Software architecture and Deep learning. 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 Software architecture
- Tasks that involve Deep learning
Example prompts
- “Use the ai-federated-learning skill to implement federated learning architecture patterns for GDPR compliance”
- “/ai-federated-learning”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Gradient leakage: Model updates can leak training data even without transmitting raw data — secure aggregation and DP mitigate but do not…
- Model memorization: The aggregated model may still memorize individual records — privacy auditing required post-training
- Right to erasure: Removing a participant's contribution from the trained model is challenging — machine unlearning or retraining required
- Controller/processor determination: Complex multi-party FL architectures require clear role delineation
- Lawful basis: Each participant needs independent lawful basis; the aggregator needs basis for processing updates
- Transparency: Data subjects must be informed about federated learning participation
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
AI Federated Learning loads about 3.1k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 1,337 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). 1,337 words, ~3,058 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-federated-learning/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- ai-federated-learning
- description
- Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- ai-privacy-governance
- metadata.tags
- federated-learning, secure-aggregation, differential-privacy, distributed-ml, privacy-preserving, gdpr
Federated Learning for GDPR Compliance
Overview
Federated learning (FL) is a distributed machine learning approach that trains models across multiple data holders without centralising personal data. Instead of collecting training data into a central repository, federated learning sends the model to the data, computes local updates on each participant's device or server, and aggregates only model updates (gradients or weights) at a central coordinator. This architecture directly addresses GDPR data minimisation (Art. 5(1)(c)) and data protection by design (Art. 25) principles by eliminating the need to transfer and centralise personal data for AI training. However, federated learning is not a privacy silver bullet — it introduces its own privacy risks that must be managed through complementary techniques.
Federated Learning Architecture Patterns
Pattern 1: Cross-Device Federated Learning
Use case: Training on data from millions of user devices (smartphones, tablets, IoT).
| Component | Description |
|---|---|
| Participants | End-user devices (smartphones, tablets, wearables) |
| Scale | Thousands to millions of participants |
| Data | Small per-device, large aggregate (e.g., keyboard predictions, health metrics) |
| Coordination | Central server selects participants per round, distributes model, aggregates updates |
| Communication | Compressed gradient updates over mobile networks |
| Privacy risk | Individual gradient updates may leak information about device data |
GDPR Analysis:
- Data minimisation: personal data never leaves the device — strong compliance
- Controller role: platform operator is controller; device owners are not processors
- Lawful basis: consent or legitimate interest for on-device processing
- International transfers: no personal data transfer if aggregation is privacy-preserving
- Right to erasure: device can be excluded from future rounds; model unlearning may be needed
Pattern 2: Cross-Silo Federated Learning
Use case: Training across organisational boundaries (hospitals, banks, subsidiaries).
| Component | Description |
|---|---|
| Participants | Organisational data silos (hospitals, branches, partner companies) |
| Scale | 2 to 100 participants |
| Data | Large per-silo, structured (e.g., medical records, financial transactions) |
| Coordination | Trusted aggregator or peer-to-peer protocol |
| Communication | Model updates over secure channels between organisations |
| Privacy risk | Gradient updates may reveal institutional data patterns |
GDPR Analysis:
- Joint controller determination: participants and aggregator may be joint controllers (Art. 26) — requires joint controller agreement
- Data processing agreements: if aggregator is a processor, Art. 28 DPA required
- Data minimisation: personal data stays within each silo — strong compliance
- International transfers: if silos are in different jurisdictions, gradient transfer legality depends on privacy guarantees
- Lawful basis: each silo needs independent lawful basis for local training; aggregation needs separate basis
Pattern 3: Vertical Federated Learning
Use case: Different organisations hold different features for the same individuals.
| Component | Description |
|---|---|
| Participants | Organisations with complementary data (bank + retailer sharing customer features) |
| Scale | 2 to 10 participants |
| Data | Different features for overlapping individuals |
| Coordination | Secure multi-party computation for feature combination |
| Communication | Encrypted intermediate representations |
| Privacy risk | Feature linkage may reveal individual attributes across parties |
GDPR Analysis:
- Joint controller: participants are likely joint controllers — each processes personal data for a common purpose
- Purpose limitation: combined training purpose must be compatible with each party's original collection purpose
- Data subject notification: data subjects must be informed about cross-organisation federated training
- Consent: may be required for combining data across organisations
Secure Aggregation Protocols
Protocol 1: Masking-Based Secure Aggregation
Participants mask their local updates with pairwise random masks that cancel out upon aggregation. The aggregator receives the sum without seeing individual updates.
| Property | Value |
|---|---|
| Privacy guarantee | Individual updates not visible to aggregator or other participants |
| Computational cost | O(n^2) pairwise key agreement, O(n) masking per round |
| Communication cost | 2x baseline (masks + masked updates) |
| Dropout tolerance | Handles participant dropout if sufficient participants remain |
| Collusion resistance | Secure against aggregator + up to t-1 participant collusion |
Protocol 2: Homomorphic Encryption Aggregation
Participants encrypt their updates with a homomorphic encryption scheme. The aggregator computes the sum on encrypted data without decryption.
| Property | Value |
|---|---|
| Privacy guarantee | Computationally secure — updates encrypted throughout |
| Computational cost | 100-1000x overhead for encryption/decryption operations |
| Communication cost | 2-10x baseline (ciphertext expansion) |
| Dropout tolerance | Excellent — encrypted updates can be summed independently |
| Collusion resistance | Secure against aggregator (does not hold decryption key) |
Protocol 3: Trusted Execution Environment (TEE)
Aggregation occurs within a hardware-protected enclave (Intel SGX, ARM TrustZone). Participants send updates to the TEE, which performs aggregation in isolated memory.
| Property | Value |
|---|---|
| Privacy guarantee | Hardware-based isolation — aggregator cannot inspect updates |
| Computational cost | Near-native (small overhead for enclave transitions) |
| Communication cost | Baseline (no encryption expansion for enclave-to-enclave) |
| Dropout tolerance | Excellent |
| Collusion resistance | Depends on hardware trust model; vulnerable to side-channel attacks |
Differential Privacy Integration
Local Differential Privacy (LDP)
Each participant adds noise to their gradient update before sending to the aggregator:
- Mechanism: Gaussian noise calibrated to sensitivity and privacy budget (epsilon, delta)
- Guarantee: Individual update is differentially private — aggregator cannot infer specific data points
- Trade-off: Higher noise reduces model accuracy; need more participants to compensate
- Recommended epsilon: 1-10 per round, with privacy accounting across rounds
Central Differential Privacy (CDP)
The aggregator adds noise to the aggregated update before applying to the global model:
- Mechanism: Gaussian noise added to the sum of clipped gradients
- Guarantee: Global model is differentially private with respect to any single participant's data
- Trade-off: Less noise needed than LDP for same accuracy (noise averages out); requires trusted aggregator
- Recommended epsilon: 1-8 total privacy budget
Show full SKILL.md (507 more words)Show less
Privacy Budget Management
| Parameter | Description | Guidance |
|---|---|---|
| Epsilon (ε) | Privacy loss parameter — lower is more private | ε ≤ 1: strong privacy; ε ≤ 8: moderate; ε > 10: weak |
| Delta (δ) | Probability of privacy failure | δ < 1/N where N is dataset size |
| Rounds (T) | Number of federated training rounds | Privacy degrades with rounds — use composition theorems |
| Clip norm (C) | Maximum gradient norm per participant | Balance between privacy (lower C) and convergence (higher C) |
| Noise multiplier (σ) | Ratio of noise to sensitivity | Determined by ε, δ, C, and composition method |
GDPR Compliance Assessment for Federated Learning
Data Protection by Design (Art. 25) Alignment
| GDPR Principle | FL Implementation | Compliance Status |
|---|---|---|
| Data minimisation (Art. 5(1)(c)) | Personal data stays local — only model updates transmitted | Strong compliance |
| Purpose limitation (Art. 5(1)(b)) | Local processing for specified training purpose | Requires per-participant purpose documentation |
| Storage limitation (Art. 5(1)(e)) | No central training data repository — data retained locally per participant's policy | Compliance depends on participant retention |
| Integrity and confidentiality (Art. 5(1)(f)) | Secure aggregation protects update confidentiality | Strong with SA + DP |
| Accuracy (Art. 5(1)(d)) | Model accuracy may differ from centralised training | Monitor and document accuracy trade-offs |
Remaining GDPR Challenges
- Gradient leakage: Model updates can leak training data even without transmitting raw data — secure aggregation and DP mitigate but do not eliminate
- Model memorization: The aggregated model may still memorize individual records — privacy auditing required post-training
- Right to erasure: Removing a participant's contribution from the trained model is challenging — machine unlearning or retraining required
- Controller/processor determination: Complex multi-party FL architectures require clear role delineation
- Lawful basis: Each participant needs independent lawful basis; the aggregator needs basis for processing updates
- Transparency: Data subjects must be informed about federated learning participation
Implementation Considerations
Communication Efficiency
| Technique | Description | Privacy Impact |
|---|---|---|
| Gradient compression | Quantise or sparsify gradients before transmission | May interact with DP noise — careful calibration needed |
| Federated averaging (FedAvg) | Multiple local SGD steps before communication | Reduces communication rounds; may increase per-round privacy cost |
| Gradient selection | Send only top-k gradient components | Leaks which components are most significant — privacy concern |
Participant Selection
| Strategy | Description | Privacy Consideration |
|---|---|---|
| Random selection | Uniformly random participant sampling per round | Fair representation; privacy amplification through subsampling |
| Availability-based | Select participants with sufficient resources | May bias toward certain participant profiles |
| Contribution-based | Select participants whose data improves model most | Reveals information about data distribution — privacy risk |
Enforcement Relevance
While no enforcement action has specifically addressed federated learning, the technology is directly relevant to:
- CNIL AI recommendations (2024): Identified federated learning as a recommended privacy-enhancing technology for AI training
- EDPB Guidelines 04/2025: Mentioned distributed training as a data minimisation measure for AI systems
- Garante v. OpenAI (2023): Centralised data collection was a key compliance issue — federated alternatives could have mitigated
- AI Act Art. 10: Data governance for training data — FL enables data governance while maintaining data locality
Integration Points
- ai-dpia: FL architecture reduces data centralisation risk in DPIA assessment
- ai-training-lawfulness: FL may simplify lawful basis by keeping data local
- ai-model-privacy-audit: Federated models require distributed privacy auditing
- ai-privacy-inference: FL complements confidential computing for inference privacy
- ai-data-retention: Data locality in FL simplifies retention compliance
© 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/ai-federated-learning 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
AI Federated Learning 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 |
|---|---|---|---|---|---|---|
| AI Federated Learning this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| C15tc15t/c15t | 1.9k | 1 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Korean Privacy Termskimlawtech/korean-privacy-terms | 587 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | |
| Gdpr ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~3.9k | Automated safety check: Pass | MIT | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~2.3k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about AI Federated Learning
What does AI Federated Learning do?
Implements federated learning architecture patterns for GDPR compliance. AI Federated Learning is an agent skill from mukul975/Privacy-Data-Protection-Skills. Implements federated learning architecture patterns for GDPR compliance.
When should I use AI Federated Learning?
AI Federated Learning fits situations like: tasks that involve Privacy and GDPR; tasks that involve Software architecture; tasks that involve Deep learning.
How do I install AI Federated Learning in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-federated-learning -a claude-code`. Or copy the skill folder (skills/privacy/ai-federated-learning in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/ai-federated-learning in your project. Claude Code loads it when a task matches its description.
How do I install AI Federated Learning in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill ai-federated-learning -a codex`. Or copy the skill folder (skills/privacy/ai-federated-learning in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/ai-federated-learning in your project. Codex loads it when a task matches its description.
Can I use AI Federated Learning 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 ai-federated-learning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-federated-learning, .gemini/skills/ai-federated-learning, .github/skills/ai-federated-learning and .opencode/skills/ai-federated-learning in your project.
What does AI Federated Learning need to run?
Going by SKILL.md and its folder, AI Federated Learning needs Python for the scripts in its folder. Our summary lists: Python 3.
Does AI Federated Learning 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 AI Federated Learning 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 AI Federated Learning use?
AI Federated Learning 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 AI Federated Learning use?
About 3.1k tokens (SKILL.md is roughly 12k 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 2k tokens, read only when the agent opens those files.
What are the alternatives to AI Federated Learning?
Skills that share tags, products or a category with AI Federated Learning: C15t (c15t/c15t, 1.9k stars), HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), Korean Privacy Terms (kimlawtech/korean-privacy-terms, 587 stars) and Gdpr Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Federated Learning?
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.