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Agent skill
Designing Federated Learning Architecture
Architecture guide for GDPR-compliant federated learning systems.
Install Designing Federated Learning Architecture
$ npx skills add mukul975/Privacy-Data-Protection-Skills --skill designing-federated-learning-architecture -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills designing-federated-learning-architecture --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/designing-federated-learning-architecture .claude/skills/designing-federated-learning-architecture && 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 "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .claude/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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/designing-federated-learning-architectureType 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 designing-federated-learning-architecture -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills designing-federated-learning-architecture --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/designing-federated-learning-architecture .agents/skills/designing-federated-learning-architecture && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .agents/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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 designing-federated-learning-architecture -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills designing-federated-learning-architecture --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/designing-federated-learning-architecture .cursor/skills/designing-federated-learning-architecture && 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 "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .cursor/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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/designing-federated-learning-architecture--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 designing-federated-learning-architecture -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Privacy-Data-Protection-Skills designing-federated-learning-architecture --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/designing-federated-learning-architecture .gemini/skills/designing-federated-learning-architecture && 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 "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .gemini/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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 designing-federated-learning-architectureInstalls 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 designing-federated-learning-architecture -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/designing-federated-learning-architecture .github/skills/designing-federated-learning-architecture && 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 "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .github/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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 designing-federated-learning-architecture -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 designing-federated-learning-architecture --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/designing-federated-learning-architecture .opencode/skills/designing-federated-learning-architecture && 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 "designing-federated-learning-architecture" agent skill from https://github.com/mukul975/Privacy-Data-Protection-Skills/tree/main/skills/privacy/designing-federated-learning-architecture into .opencode/skills/designing-federated-learning-architecture/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "designing-federated-learning-architecture", 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
designing-federated-learning-architecture- GitHub stars
- 301
- Token cost
- ~2.5k tokens
- SKILL.md length
- 835 words
- Files
- 5 (incl. scripts, references, assets)
- Skills in repo
- 280
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Architecture guide for GDPR-compliant federated learning systems.
- Works in 3 steps: Pairwise masking (Bonawitz et al., 2017)… → Threshold secret sharing — Each… → Homomorphic encryption — Participants…
- Tasks that involve Privacy and GDPR
- SKILL.md covers Overview, FL Architectures, Aggregation Strategies and Privacy Guarantees, plus 3 more sections
- Runs Python scripts from its folder
What it does
Designing Federated Learning Architecture is an agent skill from mukul975/Privacy-Data-Protection-Skills. Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
Its SKILL.md is about 2.5k 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. 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
- “/designing-federated-learning-architecture”
Requirements
- Python 3
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Pairwise masking (Bonawitz et al., 2017) — Each pair of participants generates a shared random mask. Masks cancel out in the sum, but…
- Threshold secret sharing — Each participant secret-shares their update; the server reconstructs only the aggregate.
- Homomorphic encryption — Participants encrypt updates; server aggregates ciphertexts.
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
Designing Federated Learning Architecture loads about 2.5k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 835 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). 835 words, ~2,451 tokens.
Download SKILL.mdSave it as
.claude/skills/designing-federated-learning-architecture/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- designing-federated-learning-architecture
- description
- Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
- license
- Apache-2.0
- metadata.author
- mukul975
- metadata.version
- 1.0
- metadata.domain
- privacy
- metadata.subdomain
- privacy-by-design
- metadata.tags
- federated-learning, fedavg, secure-aggregation, distributed-ml, privacy-preserving-ml
Designing Federated Learning Architecture
Overview
Federated Learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Each participant trains a local model on their own data and shares only model updates (gradients or parameters) with a central aggregator. The aggregated model benefits from all participants' data without any single party accessing another's dataset.
FL directly supports GDPR Article 5(1)(c) data minimization (only model updates are shared, not personal data), Article 25(1) data protection by design (privacy is built into the architecture), and can reduce the need for cross-border data transfers under Chapter V (data stays in its jurisdiction of origin).
FL Architectures
Horizontal Federated Learning
Participants share the same feature space but have different data samples. Each participant has a complete record for their subjects but covers different subjects.
┌──────────────────────────────────────────────────────────────┐
│ Central Aggregator │
│ (aggregates model updates only) │
│ │
│ Global Model = Aggregate(Local_1, Local_2, ..., Local_K) │
└─────────────┬──────────────┬──────────────┬──────────────────┘
│ │ │
┌─────────▼────┐ ┌──────▼──────┐ ┌────▼──────────┐
│ Hospital A │ │ Hospital B │ │ Hospital C │
│ 5,000 pts │ │ 8,000 pts │ │ 3,000 pts │
│ Same features│ │ Same features│ │ Same features │
│ Train locally│ │ Train locally│ │ Train locally │
└──────────────┘ └─────────────┘ └───────────────┘Use case: Multiple hospitals training a diagnostic model; each hospital has complete patient records but different patients.
Vertical Federated Learning
Participants share the same data subjects but have different features. Features are distributed across participants.
┌──────────────────────────────────────────────────────────────┐
│ Coordination Server │
│ (aligns features without exposing raw data) │
└─────────────┬──────────────┬──────────────┬──────────────────┘
│ │ │
┌─────────▼────┐ ┌──────▼──────┐ ┌────▼──────────┐
│ Bank │ │ Insurer │ │ Retailer │
│ Financial │ │ Claims │ │ Purchase │
│ features │ │ features │ │ features │
│ Same users │ │ Same users │ │ Same users │
└──────────────┘ └─────────────┘ └───────────────┘Use case: Credit scoring where a bank has financial data, an insurer has claims history, and a retailer has purchase behavior — all for overlapping customers.
Aggregation Strategies
FedAvg (Federated Averaging)
The foundational FL algorithm (McMahan et al., 2017). Each participant trains for multiple local epochs, then the aggregator averages the model weights proportional to each participant's dataset size.
For each communication round t:
1. Server sends global model w_t to selected participants
2. Each participant k trains locally for E epochs on their data:
w_k = LocalSGD(w_t, data_k, E epochs, learning_rate η)
3. Server aggregates:
w_{t+1} = Σ (n_k / n) * w_k
where n_k = participant k's sample count, n = total samples| Parameter | Recommended Range | Impact |
|---|---|---|
| Local epochs (E) | 1-5 | More epochs = less communication, but higher divergence risk |
| Participation rate | 10-100% per round | Higher = better convergence, more communication |
| Learning rate (η) | 0.01-0.1 | Standard SGD tuning applies |
| Communication rounds | 50-500 | Depends on data heterogeneity and model complexity |
FedProx (Federated Proximal)
Extension of FedAvg for heterogeneous settings. Adds a proximal term to the local objective to limit how far local models drift from the global model. Suitable when participants have non-IID (non-independently and identically distributed) data.
Local objective for participant k:
minimize F_k(w) + (μ/2) * ||w - w_t||^2
where μ controls the strength of the proximal regularization| Scenario | FedAvg Performance | FedProx Performance | Recommendation |
|---|---|---|---|
| IID data across participants | Good | Good | FedAvg (simpler) |
| Mild non-IID (label skew) | Moderate | Good | FedProx (μ=0.01) |
| Severe non-IID (feature shift) | Poor | Moderate | FedProx (μ=0.1) + larger participation |
| Participants with varying compute | Unstable | Stable | FedProx (handles partial work) |
Privacy Guarantees
Threat Model
| Threat | Description | Mitigation |
|---|---|---|
| Gradient inversion | Adversary reconstructs training data from shared gradients | Secure aggregation + differential privacy |
| Membership inference | Adversary determines if a specific record was in training data | Differential privacy |
| Model inversion | Adversary recovers sensitive features from the trained model | Differential privacy + access control on model |
| Free-rider attack | Participant contributes noise instead of genuine updates | Contribution verification (FoolsGold, RFFL) |
| Poisoning attack | Malicious participant corrupts the global model | Byzantine-robust aggregation (Krum, trimmed mean) |
Secure Aggregation
Secure aggregation ensures the server sees only the sum of all participants' updates, not individual updates. Implemented using:
- Pairwise masking (Bonawitz et al., 2017) — Each pair of participants generates a shared random mask. Masks cancel out in the sum, but individual updates remain hidden.
- Threshold secret sharing — Each participant secret-shares their update; the server reconstructs only the aggregate.
- Homomorphic encryption — Participants encrypt updates; server aggregates ciphertexts.
Show full SKILL.md (335 more words)Show less
Differential Privacy in FL
Adding calibrated noise to model updates before sharing provides a formal privacy guarantee:
DP-FedAvg:
1. Each participant clips gradients: ||g_k|| ≤ C (sensitivity bounding)
2. Each participant adds Gaussian noise: g_k' = g_k + N(0, σ²·C²·I)
3. Server aggregates noised updates: g_agg = (1/K) Σ g_k'
4. Privacy accounting: track cumulative (ε, δ) via Rényi DP compositionPrivacy budget for Prism Data Systems AG FL deployment:
| Parameter | Value | Justification |
|---|---|---|
| Clipping norm (C) | 1.0 | Standard for gradient clipping in DP-SGD |
| Noise multiplier (σ) | 1.1 | Provides (ε=3, δ=1e-5) per training run with 500 rounds |
| Target epsilon (ε) | 3.0 | Moderate privacy for non-special-category data |
| Target delta (δ) | 1e-5 | Standard for n ≈ 316,000 total records |
| Composition method | Rényi DP | Tighter composition bounds than basic composition |
Communication Efficiency
| Technique | Compression Ratio | Accuracy Impact | Description |
|---|---|---|---|
| Gradient quantization | 8-32x | Minimal | Reduce gradient precision from 32-bit to 8-bit or lower |
| Top-k sparsification | 10-100x | Moderate | Transmit only the k largest gradient components |
| Federated dropout | 2-10x | Minimal | Each participant trains a random submodel |
| Gradient compression (SignSGD) | 32x | Moderate | Transmit only the sign of each gradient component |
| Model distillation | Variable | Moderate | Participants share soft predictions instead of gradients |
Implementation Workflow
Define the ML task — Specify the model architecture, training objective, and evaluation metrics.
Assess data distribution — Evaluate the degree of non-IID across participants. This determines the aggregation strategy (FedAvg for IID, FedProx for non-IID).
Select privacy mechanisms — Choose secure aggregation for gradient confidentiality and differential privacy for formal guarantees. Set epsilon budget.
Configure communication — Select gradient compression technique based on bandwidth constraints. Set the communication round budget.
Implement local training — Deploy the local training pipeline at each participant. Implement gradient clipping and noise injection for DP.
Deploy aggregation server — Deploy the central aggregator with secure aggregation protocol. Implement Byzantine-robust aggregation if untrusted participants exist.
Monitor and evaluate — Track model convergence, per-participant contribution quality, privacy budget consumption, and communication overhead.
Key Regulatory References
- GDPR Article 5(1)(c) — Data minimization (only model updates shared)
- GDPR Article 25(1) — Data protection by design
- GDPR Article 26 — Joint controller arrangements (FL consortium governance)
- GDPR Chapter V — Cross-border transfers (FL reduces transfer need)
- GDPR Article 35 — DPIA for FL involving special category data
- EDPB Guidelines 4/2019 on Article 25 Data Protection by Design and by Default
© 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/designing-federated-learning-architecture 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
Designing Federated Learning Architecture 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 |
|---|---|---|---|---|---|---|
| Designing Federated Learning Architecture this skillmukul975/Privacy-Data-Protection-Skills | 301 | — | ~2.5k | 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 Designing Federated Learning Architecture
What does Designing Federated Learning Architecture do?
Architecture guide for GDPR-compliant federated learning systems. Designing Federated Learning Architecture is an agent skill from mukul975/Privacy-Data-Protection-Skills. Architecture guide for GDPR-compliant federated learning systems.
When should I use Designing Federated Learning Architecture?
Designing Federated Learning Architecture fits situations like: tasks that involve Privacy and GDPR.
How do I install Designing Federated Learning Architecture in Claude Code?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill designing-federated-learning-architecture -a claude-code`. Or copy the skill folder (skills/privacy/designing-federated-learning-architecture in mukul975/Privacy-Data-Protection-Skills) into .claude/skills/designing-federated-learning-architecture in your project. Claude Code loads it when a task matches its description.
How do I install Designing Federated Learning Architecture in Codex?
Run `npx skills add mukul975/Privacy-Data-Protection-Skills --skill designing-federated-learning-architecture -a codex`. Or copy the skill folder (skills/privacy/designing-federated-learning-architecture in mukul975/Privacy-Data-Protection-Skills) into .agents/skills/designing-federated-learning-architecture in your project. Codex loads it when a task matches its description.
Can I use Designing Federated Learning Architecture 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 designing-federated-learning-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/designing-federated-learning-architecture, .gemini/skills/designing-federated-learning-architecture, .github/skills/designing-federated-learning-architecture and .opencode/skills/designing-federated-learning-architecture in your project.
What does Designing Federated Learning Architecture need to run?
Going by SKILL.md and its folder, Designing Federated Learning Architecture needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Designing Federated Learning Architecture 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 Designing Federated Learning Architecture 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 Designing Federated Learning Architecture use?
Designing Federated Learning Architecture 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 Designing Federated Learning Architecture use?
About 2.5k tokens (SKILL.md is roughly 9.8k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Designing Federated Learning Architecture?
Skills that share tags, products or a category with Designing Federated Learning Architecture: 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 Designing Federated Learning Architecture?
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.