fhe-application-designlisted

Design and build Fully Homomorphic Encryption (FHE) applications using OpenFHE. Guides developers from problem statement through privacy model, feasibility assessment, scheme selection, circuit design, SIMD data layout, parameter selection, and code generation. Use this skill whenever the user mentions FHE, homomorphic encryption, encrypted computation, computing on encrypted data, OpenFHE, CKKS, BFV, BGV, privacy-preserving computation, or wants to design any application that processes data without decrypting it. Also use when the user asks about FHE feasibility, whether a workload can run under encryption, or how to structure a client-server protocol for encrypted data. Use this skill even if the user doesn't explicitly say "FHE" — if they describe a scenario where computation must happen on data that the computing party cannot see, this skill applies.
NiobiumInc/niobium-skills · ★ 11 · Web & Frontend · score 75

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# FHE Application Design ("FHEanna") > Informally known as **FHEanna** (pronounced "fee-AH-na"). Patent pending. > The install/trigger name remains `fhe-application-design`. This skill guides developers through designing and building FHE applications using OpenFHE. It targets developers who are new to FHE but know how to code. The approach is top-down: start with the privacy problem, establish feasibility, then progressively work down through scheme selection, circuit design, data layout, parameter selection, and implementation. ## How to Use This Skill Follow the stages below in order. Each stage has a brief description here in the SKILL.md, with pointers to reference files that contain deeper guidance. Read the relevant reference file when you need the detail for a given stage. The stages mirror a real FHE design process. Do not skip stages — each one produces inputs that the next stage depends on. **Three versions of the application.** Keep these distinct throughout — the whole methodology is the disciplined path from the first to the third: 1. **The reference** — the full plaintext computation, the ground truth. The *user* provides this (for ML workloads it is a firm requirement; see Stage 3). Every later version is measured against it. 2. **The twin** — a plaintext model of the *FHE-shaped* computation: the same algorithm but with every operation replaced by an FHE-friendly one (non-linearities → polynomial approximations), FHE-friendly data types, and