# QA > Answer questions about the project by reading documentation sources in priority order. Use when the user asks "how does X work", "where is X", "what does X do", or any project-related question. Use when this capability is needed. - Skill: `tomevault-io/qa` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/qa` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/qa/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/qa --- # Project Q&A Answer the user's question about this project. Use the general lookup order first, then the topic-specific maps below for deeper dives. ## General lookup order Read these in order: 1. `.claude/docs/project_structure.md` — directory layout and component overview. 2. `README.md` — high-level project description, setup, and usage. 3. `DEVELOPING.md` — development workflow, testing, and contribution guide. 4. `docs/source/` — detailed Sphinx documentation pages. ## Topic: Data generation / collection - [slurm/data_collection/collect_data.py](slurm/data_collection/collect_data.py) — SLURM orchestrator launching parallel CARLA data collection jobs. - [slurm/data_collection/delete_failed_routes.py](slurm/data_collection/delete_failed_routes.py) — cleans up failed route directories. - [3rd_party/leaderboard_autopilot/leaderboard/leaderboard_evaluator_local.py](3rd_party/leaderboard_autopilot/leaderboard/leaderboard_evaluator_local.py) — runs CARLA scenarios per route. - [lead/__main__.py](lead/__main__.py) — adapts LEAD to the CARLA leaderboard agent protocol. - [lead/expert/expert.py](lead/expert/expert.py) — main expert agent driving logic. - [lead/expert/expert_base.py](lead/expert/expert_base.py) — shared base class for expert variants. - [lead/expert/expert_data.py](lead/expert/expert_data.py) — saves frames and labels during collection. - [lead/expert/config_expert.py](lead/expert/config_expert.py) — expert configuration dataclass. - [lead/expert/privileged_route_planner.py](lead/expert/privileged_route_planner.py) — privileged route/lane planning. ## Topic: Data preparation - [lead/data_loader/carla_dataset.py](lead/data_loader/carla_dataset.py) — CARLA Leaderboard 2.0 PyTorch dataset. - [lead/data_loader/carla_dataset_utils.py](lead/data_loader/carla_dataset_utils.py) — CARLA loading and preprocessing helpers. - [lead/data_loader/training_cache.py](lead/data_loader/training_cache.py) — compressed on-disk training cache. - [lead/data_loader/navsim_dataset.py](lead/data_loader/navsim_dataset.py) — NavSim PyTorch dataset. - [lead/data_loader/waymo_e2e_dataset.py](lead/data_loader/waymo_e2e_dataset.py) — Waymo Open Dataset end-to-end dataset. - [lead/data_buckets/abstract_bucket_collection.py](lead/data_buckets/abstract_bucket_collection.py) — ABC for bucket collections (serialization, loading). - [lead/data_buckets/bucket.py](lead/data_buckets/bucket.py) — single bucket holding paths and metadata. - [lead/data_buckets/route_filtering.py](lead/data_buckets/route_filtering.py) — route validity and deduplication. - [scripts/build_cache.py](scripts/build_cache.py) — entry point to build the training cache. - [scripts/build_buckets_pretrain.py](scripts/build_buckets_pretrain.py) — generate pretraining bucket collections. ## Topic: Training - [lead/training/train.py](lead/training/train.py) — main DDP-aware training entry point (Trainer class). - [lead/training/config_training.py](lead/training/config_training.py) — training hyperparameters dataclass. - [lead/training/training_utils.py](lead/training/training_utils.py) — seeding, checkpointing, data loader construction. - [lead/training/mixed_training_utils.py](lead/training/mixed_training_utils.py) — multi-dataset sample scheduling. - [lead/training/logger.py](lead/training/logger.py) — WandB metric and media logging. - [scripts/pretrain_ddp.sh](scripts/pretrain_ddp.sh) — DDP pretraining launcher. - [scripts/posttrain_ddp.sh](scripts/posttrain_ddp.sh) — DDP posttraining launcher. - [slurm/experiments/](slurm/experiments/) — per-experiment SLURM scripts (see `/prepare-slurm-scripts`). ## Topic: Model architecture (TransFuser v6) - [lead/tfv6/tfv6.py](lead/tfv6/tfv6.py) — top-level model orchestrating backbone and decoder heads, loss aggregation. - [lead/tfv6/transfuser_backbone.py](lead/tfv6/transfuser_backbone.py) — image + LiDAR fusion backbone. - [lead/tfv6/transfuser_utils.py](lead/tfv6/transfuser_utils.py) — backbone utility layers and functions. - [lead/tfv6/planning_decoder.py](lead/tfv6/planning_decoder.py) — waypoint, path and target speed planning head. - [lead/tfv6/bev_decoder.py](lead/tfv6/bev_decoder.py) — BEV semantic segmentation head. - [lead/tfv6/perspective_decoder.py](lead/tfv6/perspective_decoder.py) — perspective view output head. - [lead/tfv6/center_net_decoder.py](lead/tfv6/center_net_decoder.py) — CenterNet 2D bounding box detector. - [lead/tfv6/radar_detector.py](lead/tfv6/radar_detector.py) — 2D radar object detector. ## Topic: Evaluation (bench2drive, longest6, town13) - [slurm/evaluation/evaluate.py](slurm/evaluation/evaluate.py) — core orchestrator: submit, monitor, retry SLURM eval jobs. - [slurm/evaluation/evaluate_scripts_generator.py](slurm/evaluation/evaluate_scripts_generator.py) — generates per-route shell scripts. - [slurm/evaluation/evaluate_utils.py](slurm/evaluation/evaluate_utils.py) — JSON parsing, status checks. - [slurm/evaluation/merge_route_json.py](slurm/evaluation/merge_route_json.py) — merges per-route results into one summary. - [slurm/evaluation/evaluate_wandb_logger.py](slurm/evaluation/evaluate_wandb_logger.py) — logs aggregated eval results to WandB. - [lead/inference/closed_loop_inference.py](lead/inference/closed_loop_inference.py) — CARLA closed-loop evaluation with PID control. - [lead/inference/open_loop_inference.py](lead/inference/open_loop_inference.py) — open-loop evaluation (NavSim / Bench2Drive). - [lead/inference/sensor_agent.py](lead/inference/sensor_agent.py) — sensor-based agent wrapper for the CARLA leaderboard protocol. - [lead/inference/infraction_recorder.py](lead/inference/infraction_recorder.py) — records and serializes driving infractions. - [lead/inference/config_closed_loop.py](lead/inference/config_closed_loop.py) — closed-loop eval config. ## Rules 1. **Read in order.** Start with general docs, then dive into topic-specific files. 2. **Be specific.** Point the user to the exact file, section, or line. 3. **Don't guess.** If none of the sources answer the question, say so and suggest where else to look. --- > Source: [kesai-labs/lead](https://github.com/kesai-labs/lead) — distributed by [TomeVault](https://tomevault.io).