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FPGA 10/13 · Saleae Claude — Measurement as Proof
Verifies FPGA hardware behavior by comparing real-world Saleae logic captures against simulation predictions.
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See it in action
You say
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.
Your agent does
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.
What you get
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 install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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