Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add onfire7777/universal-ai-skills-library --skill ai-federated-learninggit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-federated-learning)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-federated-learning"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-federated-learning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/ai-federated-learning"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/ai-federated-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.02386 |
| Opus 5 | $0.00028 | $0.01193 |
| Sonnet 5 | $0.00011 | $0.00477 |
| Haiku 4.5 | $0.00006 | $0.00239 |
Grade A, and why
ai-federated-learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to ai-federated-learning — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 203 lines · 57 tokens per session scan A 47ae0e6e5c76
ai-federated-learning is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 2,386 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to ai-federated-learning, differing in 19 lines, and is treated as a copy.
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