Ace Rtl

ACE-RTL

Use this skill as the entry point for ACE-RTL work. It contains the agent protocol, CVDP runbooks, role guidance, optional external LLM bridge, and task-driven CVDP prompt guidance.

Core Rules

• Clone the official CVDP benchmark code from https://github.com/NVlabs/cvdp_benchmark.git into this workspace and follow that checkout's own setup instructions. Do not use a CVDP checkout, setup, environment, or data outside the current workspace unless the user explicitly authorizes it.
• Report final pass/fail only from the real CVDP evaluator.
• When Docker is unavailable or the daemon socket is inaccessible, use the checked-in native runner scripts/ace_cvdp_native_runner.py after setup-native.md. Do not hand-roll Docker-to-native path translation.
• Native CVDP runs must execute under the repo-local .cvdp-native Python environment when it exists. Missing Python packages, pytest collection/import failures, or missing harness env variables are INFRA_SETUP, not candidate RTL failures.
• Before starting any native CVDP datapoint workers or LLM calls, verify the selected rows' harness services have their required simulator tools on PATH. Rows that request Xcelium/Cadence services require a configured simulator command such as irun or xrun and usually imc; if either is missing, use the eda_tool_setup skill to configure site-provided EDA tools before classifying the run as setup-blocked.
• For native commercial/Cadence rows, copied harness command files must have container paths, configured Cadence command names, and a default Cadence timescale option rewritten before execution. The runner should inject -timescale 1ns/1ps into xrun/irun commands unless the harness already sets a timescale.
• Do not classify candidate RTL reset/register/output initialization failures as INFRA_SETUP just because a simulator or assertion line says "failed to initialize". Setup classification is for missing tools, missing Python modules, invalid harness environment variables, license/database/tool initialization failures, collection failures, or native path/setup breakage.
• The native runner must post-process generated SystemVerilog before CVDP evaluation, save bounded evaluator reports, and feed reports through the reusable FocusedDebugger reflector and FreshStartCoordinator coordinator components.
• Preserve every generated target path declared by the selected CVDP row. For multi-file targets, generator prompts must list every target file and require one complete // TARGET_FILE: <path> section per file.
• Native-runner generator prompts must be task-aware and data-driven from the selected CVDP row, target paths, prompt, and harness services. Do not hard-code a partial list of task IDs or inject one task shape's guidance into unrelated CVDP task types.
• Reusable RTL-agent components live under scripts/ace_rtl_agent/ and must be launched through scripts/ace_agent_runner.py.
• Do not use hidden solutions, golden outputs, private reference code, expected answer tables, or injected bug internals.
• Do not hardcode datapoint-specific answers or recognizable test vectors.
• Do not modify immutable harness, source, or helper files unless the task explicitly lists them as generated targets.
• During native-runner iteration, role agents and external models may receive public prompt/context, generated targets, current candidates, role history, and bounded evaluator reports emitted by the real CVDP run. Do not separately open, copy, or inject private scorer files, real test files, expected-output code, mutation definitions, or harness internals.
• Default CVDP scheduling is 4 datapoints in parallel, with 30 max iterations per process and 5 parallel independent processes per datapoint.
• Native evaluator subprocesses must be bounded and reaped after timeout or service exit.
• Every CVDP run must create a unique directory under outputs/, stream run logs to run.log, and write a Markdown result report with overall pass rate for the requested runtime filter, unique solved datapoint IDs, failed datapoint IDs, and useful run metadata.
• By default, do not call external LLM APIs. Use coding-agent reasoning and spawned role agents unless the user explicitly assigns a model to a role.
• If the user assigns a model to a role, use scripts/llm_call.py and scripts/extract_solution.py.

Role Agents

Spawn three role agents for non-trivial runs:

• generator: produces the next complete target RTL, testbench, assertion, or optimization artifact.
• reflector: use the reusable FocusedDebugger component to analyze the latest evaluator report and create focused fix guidance.
• coordinator: use the reusable FreshStartCoordinator component to maintain history and decide restart.

Read references/agent-workflow.md for the full loop, references/role-guidance.md for role prompts, and references/agent-components.md when selecting or modifying the local reusable implementation.

CVDP Setup

• Dataset setup: references/benchmarks/cvdp/setup-datasets.md
• Native no-Docker setup: references/benchmarks/cvdp/setup-native.md
• Docker setup: references/benchmarks/cvdp/setup-docker.md
• Concrete run checklist: references/benchmarks/cvdp/run-cvdp.md
• EDA tool setup skill: ../eda_tool_setup/SKILL.md

CVDP Task Guidance

Read references/benchmarks/cvdp/cid-index.md, inspect the JSONL row, target paths, prompt, and harness services, then choose guidance based on task behavior rather than hard-coded task IDs.

Optional LLM Calls

Read references/llm-routing.md only when the user explicitly assigns external models to roles. The default hosted provider is NVIDIA Inference API with model nvidia/nemotron-3-ultra-550b-a55b; users may replace that path with ACE_RTL_LLM_SCRIPT.