# Ace Rtl > Consolidated ACE-RTL skill for running an iterative generator-reflector-coordinator RTL agent on CVDP. Use for CVDP official repo setup with native or Docker environments, no-cheating benchmark runs, role-based external LLM routing, unique outputs logging, task-driven CVDP prompt guidance, and real pass-rate reporting. - Skill: `nvlabs/ace-rtl` (Agent Skill, multi-file: 88 files) - Install (CLI): `npx skillmds@latest add nvlabs/ace-rtl` - Raw SKILL.md: https://api.skillmd.com/api/skills/nvlabs/ace-rtl/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: nvlabs (https://skillmd.com/u/nvlabs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nvlabs/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: ` 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`.