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rtl-p5s-func-verify

Run cocotb multi-seed regressions comparing RTL outputs to reference models.

43|9|Updated Feb 25, 2026
Search Tags:#verification#regression#traceability#coverage#rtl#multi-seed#cocotb
One-click install
npx skills add https://github.com/babyworm/rtl-agent-team --skill rtl-p5s-func-verify
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rtl-p5s-func-verify
Source: https://github.com/babyworm/rtl-agent-team/tree/main/skills/rtl-p5s-func-verify
Command: npx skills add https://github.com/babyworm/rtl-agent-team --skill rtl-p5s-func-verify

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires make, verilator_coverage, genhtml, lcov, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Eliminates manual, ad-hoc functional verification by orchestrating cocotb multi-seed regressions that compare RTL simulation outputs against C/Python reference models and produce consolidated pass/fail and coverage results.

Core Features & Use Cases

  • Multi-seed Regression Orchestration: Launches and manages multiple seeded cocotb runs in parallel or sequential mode to surface intermittent failures.
  • Coverage Merge & Analysis: Merges per-seed coverage artifacts into a single report and annotated source view.
  • Requirement Traceability: Generates a Requirement Traceability Matrix linking requirements/acceptance criteria to test cases and verdicts.
  • Use Case: Run module-level regressions for an RTL module with existing SystemVerilog sources and a Python/C reference model to verify bit-exact behavior across randomized seeds.

Quick Start

Invoke the skill with a module name and optional seed list, for example: /rtl-agent-team:rtl-p5s-func-verify top --seeds="1 42 123".

Frequently Asked Questions about rtl-p5s-func-verify

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run cocotb multi-seed regressions to verify RTL against a reference model?▼

You can run cocotb multi-seed regressions by invoking the skill with a module name and optional seed list. It executes parallelized seeded testing, comparing RTL outputs to C or Python reference models and producing per-seed pass/fail results.

What is a Requirement Traceability Matrix in RTL functional verification?▼

A Requirement Traceability Matrix links requirements and acceptance criteria to specific test cases and their verdicts. This skill generates the matrix automatically during cocotb regression runs to ensure coverage of functional specifications.

How do I merge per-seed coverage reports from cocotb simulations?▼

Merging per-seed coverage artifacts is handled automatically using verilator_coverage and lcov. The skill consolidates individual simulation coverage data into a single report with annotated source views via genhtml.

Can I use this cocotb regression workflow with AWS Batch for large-scale module verification?▼

Yes, this skill supports optional AWS Batch execution for running module-level cocotb regressions. It allows you to scale parallelized seeded testing across simulators while collecting merged coverage and pass/fail results.

What dependencies do I need to generate annotated coverage reports from cocotb regressions?▼

You need make, verilator_coverage, genhtml, and lcov installed in your environment. These tools merge per-seed coverage artifacts and generate annotated source views from your RTL simulation runs.

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