Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    FPGA 01/13 · Python Claude — Spec and Reference

    1

    Generate bit-accurate Python executable specifications for FPGA hardware design and verification.

    $10

    30-day refund guarantee

    Secure checkout via Stripe

    0 installsSecurity scanned

    See it in action

    You say

    Design a bit-accurate specification for a 32-bit integer square root using Newton's method. I need relative accuracy of 2^-16. Document the functional parameters and the loop structure.

    Your agent does

    I have generated SPEC_sqrt32_v1.md and ref_sqrt32.py. The reference uses a fixed iteration count based on the 32-bit width to ensure determinism. Functional parameters (input_width, accuracy) are defined without defaults, while performance parameters (pipeline_stages) include defaults.

    What you get

    Generate bit-accurate Python models for hardware algorithm verification.Define mandatory functional parameters versus optional performance defaults.Derive FPGA word widths based on specific numerical accuracy requirements.Document explicit initial values for registers to prevent simulation mismatches.

    About this skill

    The problem

    Hardware bugs often stem from vague specifications where numerical accuracy and bit-level behavior are left to "gut feeling." Without a deterministic reference, developers resort to eyeballing waveforms, allowing silent rounding errors and initialization bugs to reach the physical FPGA.

    What it does

    • Generates bit-accurate Python reference models that serve as the single source of truth for hardware logic.
    • Separates functional parameters from performance parameters to ensure architectural changes don't break numerical correctness.
    • Derives specific word widths directly from required application accuracy to eliminate wasted logic.
    • Produces intermediate state outputs for cycle-by-cycle verification against future RTL.
    • Enforces strict determinism by avoiding non-deterministic loops, unset seeds, or timing-dependent logic.

    Frameworks & tools

    Python 3.x, FPGA Design Suites (Xilinx Vivado, Intel Quartus), Verilog/SystemVerilog, RTL Simulation tools.

    Why this beats prompting it yourself

    Generic LLMs often mix hardware structure with algorithmic logic, leading to non-synthesizable references or mismatched bit-widths. This skill enforces a 13-stage industrial design methodology that separates "what" is computed from "how" fast it runs, preventing common FPGA traps like uninitialized registers and heuristic termination.

    Use cases

    • Creating bit-accurate references for SAR bit-serial algorithms or integer square roots.
    • Defining fixed-point arithmetic pipelines where rounding and overflow must be exact.
    • Building testable "small instances" of large operators for exhaustive verification.
    • Documenting register initial values to ensure simulation matches physical hardware behavior.

    Known limitations

    This skill focuses solely on the executable specification and Python reference. It does not generate Verilog code or testbenches directly, as those are handled by subsequent stations in the chain.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill passes our trust checks below.

    Security scanned

    Passed our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    30-day refund

    Not a fit? Get your money back

    Trust & safety

    Security scanned

    Verified clean today

    • One-time purchase, yours forever

    Listedtoday

    Frequently Asked Questions