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    FPGA 11/13 · Documentation Claude — Evidence

    1

    Generates auditable FPGA validation dossiers with evidence levels, error budgets, and performance metrics.

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    See it in action

    You say

    Generate a validation dossier for the fixed-point FFT core. Include the error budget for 16-bit precision, the kernel speedup versus the ARM A9 baseline, and the resource usage table.

    Your agent does

    Validation Dossier: FFT Core

    Evidence Level: [Own Measurement] Accuracy: 1.2 ulp (faithful). Error Chain: Truncation (0.5) + Rounding (0.4) + Overflow Margin (0.3) < 1.5 bound. Kernel Speedup: 14.2x (Data: int16, Pattern: Linear). Resources: 4200 LUTs / 15k available.

    What you get

    Convert raw FPGA measurements into an auditable evidence-based dossier.Chain individual error sources to prove overall system accuracy bounds.Separate kernel performance from application-level gains to avoid misleading claims.Log rejected design paths to save future engineering synthesis cycles.

    About this skill

    The problem

    Engineering claims often rot into vague marketing because they lack boundary conditions. Without a traceable evidence chain, FPGA performance numbers, speedups, and accuracy claims become impossible for third parties to reproduce or verify.

    What it does

    • Assigns every statement an evidence level: cited source, own measurement, hypothesis, or quarantined analogy.
    • Deconstructs accuracy claims into a traceable error budget chain using triangle inequality bounds.
    • Generates mandatory resource usage and timing tables with used versus available metrics.
    • Enforces strict separation between kernel speedup and end-to-end application speedup.
    • Documents rejected optimizations and dead ends to prevent redundant future engineering effort.

    Frameworks & tools

    FPGA development environments, RTL simulation, gate-level netlists, hardware measurement tools (Saleae), and bitstream mapping tools.

    Why this beats prompting it yourself

    Writing validation dossiers manually often leads to mixing hypotheses with facts. This skill enforces a sign-off catalog of independent checks and ensures boundary conditions stay attached to every metric, preventing the "it works on my machine" fallacy in hardware documentation.

    Use cases

    • Creating an auditable validation dossier for FPGA IP delivery.
    • Breaking down complex precision requirements into a checkable error budget.
    • Documenting high-performance computing metrics with explicit counting methods.
    • Performing a formal sign-off for hardware release cycles.

    Known limitations

    Requires raw data inputs from synthesis reports and measurement series. Does not perform the actual physical measurements or simulations.

    How to install

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

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