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    FPGA 10/13 · Saleae Claude — Measurement as Proof

    1

    Verifies FPGA hardware behavior by comparing real-world Saleae logic captures against simulation predictions.

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    FPGA 10/13 · Saleae Claude — Measurement as Proof

    FPGA 10/13 · Saleae Claude — Measurement as Proof

    Example session with this skill installed

    Plan a Saleae capture for my SPI accelerator. The driver uses CPOL=0, CPHA=1 at 10MHz. I need to compare the physical MISO/MOSI pins against my sim_trace.vcd and seal the resulting .sal file.

    • Read your context and instructions
    • Compiled the fpga 10 13

    I have set the capture plan: 100MS/s sample rate (10x margin), SPI decoder to Mode 1 (CPOL=0, CPHA=1). Comparison target is sim_trace.vcd. After recording, run scripts/sal_seal.py capture_spi_2023-10-27.sal to generate the SHA-256 custody line for HANDOVER_SPI.md.

    run to generate the SHA-256 custody line for HANDOVER_SPI.md

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Relying on simulation or internal debug cores creates a feedback loop where systematic errors remain hidden. Without independent external measurement, performance claims are self-reported predictions rather than verified hardware reality.

    What it does

    • Plans hardware captures by calculating required sample rate margins and trigger points against simulation predictions.
    • Configures protocol decoders for UART, SPI, and I2C based on actual driver implementation rather than generic datasheets.
    • Executes bit-for-bit comparisons between physical signal captures and RTL simulation traces to identify regressions.
    • Calculates separate timing metrics for raw kernel execution versus end-to-end system paths to prevent inflated speedup claims.
    • Generates cryptographically sealed capture archives with SHA-256 hashes for chain-of-custody verification.

    Frameworks & tools

    Saleae Logic hardware and software, SPI, I2C, UART protocols, RTL simulation traces, and Python-based sealing scripts.

    Why this beats prompting it yourself

    Generic prompts often miss subtle protocol misconfigurations like SPI clock phase mismatches or insufficient oversampling. This skill enforces a strict measurement-before-prediction workflow and automates the custody hashing required for rigorous hardware audits.

    Use cases

    • Verifying FPGA accelerator throughput against simulated benchmarks.
    • Debugging communication mismatches between driver software and RTL implementations.
    • Auditing hardware performance for official technical reports and client delivery.
    • Disclosing logic overhead caused by internal embedded logic analyzers.

    Known limitations

    Requires physical Saleae logic analyzer hardware and broken-out signal pins or headers for external measurement.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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