复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
SkillAtlasSkill 详情
tao-finetune-cosmos-reason
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
其他
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：2 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-cosmos-reason" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-cosmos-reason" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-cosmos-reason" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-cosmos-reason" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/tao-finetune-cosmos-reason" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: tao-finetune-cosmos-reason
description: Cosmos3-Nano video QA supervised fine-tuning with FSDP parallelism. Use when training or evaluating video
question-answering models, fine-tuning Cosmos3-Nano or compatible Cosmos Reason models with SFT/LoRA, or working with
Cosmos-RL. Trigger phrases include "fine-tune Cosmos", "Cosmos3 Nano Reasoner", "Cosmos-RL SFT",
"video QA fine-tune", "Cosmos3-Nano training".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
author: NVIDIA Corporation
version: "0.1.0"
allowed-tools: Read Bash
tags:
- video
- qa
- cosmos
- sft
- reasoning
- vlmCosmos-RL
Supervised fine-tuning (SFT) of Cosmos Reason video QA models. The packaged
default base model is hf_model://nvidia/Cosmos3-Nano. Pretrained weights
are sourced from HuggingFace, not NGC. Gated HuggingFace models require
HF_TOKEN. Some Cosmos-RL images cannot load the native Cosmos3 Omni checkpoint
format directly; for those images, convert Cosmos3-Nano to a Qwen3-VL HF
safetensors directory before train/evaluate and use that converted directory as
the PTM path.
Uses FSDP-based parallelism with dp_shard_size for GPU count and dp_replicate_size for node count (not the standard num_gpus/num_nodes).
Requests for "Cosmos Reason 3", "Cosmos3 Nano Reasoner", or
nvidia/Cosmos3-Nano are handled by this skill. There is no separate Cosmos3
model directory in the skill bank; route those requests here. Override the base
HuggingFace model only when the user explicitly asks for a different model.
Deep detail lives in references; load the smallest one that matches the task:
references/cosmos-reason-launch.md— launch intake, preflight, per-action dataset requirements, spec construction, typical overrides.references/cosmos-reason-evaluate.md— evaluate (flat TOML, task types, LoRA eval, selective download, results) and datasets.references/cosmos-reason-automl.md— AutoML/HPO policy and search-space guidance.references/cosmos-reason-parameters.md— important parameters, hardware, error patterns, DEFT/gap analysis, parent-model inference mappings.
Dataclass Schemas
Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skill_info.yaml via automl_enabled. Runnable AutoML still requires schemas/train.schema.json and references/spec_template_train.yaml to exist and parse. Use the packaged train schema for automl_default_parameters, automl_disabled_parameters, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ~/tao-core at runtime; maintainers regenerate schemas/templates before packaging the skill bank.
Train Action Policy
This model is AutoML-enabled at the model layer. Before handling any train-stage request, read references/skill_info.yaml and resolve the run override from either an explicit automl_policy value or the user's workflow request. Use automl_policy: on by default and only expose on / off in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as automl_policy: off for this run only. When automl_policy: on, automl_enabled: true, and both schemas/train.schema.json and references/spec_template_train.yaml are packaged, route the train action through tao-skill-bank:tao-run-automl by default with this model's skill_dir. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and automl_policy. Use direct model training only when automl_policy: off or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
Non-train actions such as evaluate, inference, and quantize stay in this model skill. The per-run automl_policy override does not change model metadata.
Credentials
- HF_TOKEN (required for gated models): HuggingFace access token. For the
packaged default, the user must accept the model agreement at
https://huggingface.co/nvidia/Cosmos3-Nano and provide a token with read
access. If the user explicitly overrides the base model, they must accept
that target model's agreement too. Passed to the container as a
docker_env_var.
Cosmos3 Checkpoint Conversion
When a selected image cannot load the native Cosmos3 checkpoint format
(model_type="cosmos3_omni" or Cosmos3ForConditionalGeneration), do not patch
QwenVL, Transformers, or vLLM first. Use the upstream Cosmos Framework VLM
conversion path to produce a Qwen3-VL HF safetensors directory, then point
Cosmos-RL specs at that converted directory.
The model skill packages a helper:
python skills/models/tao-finetune-cosmos-reason/scripts/prepare_cosmos3_vlm_checkpoint.py \
--checkpoint-path /abs/path/Cosmos3-Nano \
--output-path /abs/path/Cosmos3-Nano-VLM \
--secrets-env ~/.tao/secrets.env \
--validate-with-image <cosmos-rl-image>
After conversion, use the converted directory consistently as the PTM:
train: policy.model_name_or_path=/abs/path/Cosmos3-Nano-VLM
evaluate: model.model_name=/abs/path/Cosmos3-Nano-VLM
evaluate: model.base_model_path=/abs/path/Cosmos3-Nano-VLM
For local Docker, mount the converted directory read-only into the Cosmos-RL container and set the spec to the container path. If a converted copy already exists and validates, reuse it for PTM baseline evaluation, AutoML recommendations, and final best-checkpoint evaluation rather than converting again.
Training Requirements
- Dataset type: vlm
- Formats: llava, daft
- Accepted dataset intents: training, evaluation, testing
- Monitoring metric: val/avg_loss, val/reward_avg, val/loss
- Dataset URI examples:
s3://bucket/cosmos/train,s3://bucket/cosmos/eval,/lustre/fsw/tao_datasets/cosmos_rl/train,/lustre/fsw/tao_datasets/cosmos_rl/eval - Input modes: accept either dataset roots or direct spec-key paths. Root mode maps
<root>/annotations.jsonplus<root>as the media path. Direct spec mode is valid when annotations and media live in different locations, for examplecustom.train_dataset.annotation_path=/lustre/.../train.jsonandcustom.train_dataset.media_path=/lustre/.../videos.tar.gz. - Media handling: do not ask the user to choose
videos.tar.gzvsimages.tar.gzunless they are using direct spec mode or the model/action requires a single media archive. In root mode, pass the dataset root as the media path. - Annotation validation: before launching train/AutoML/evaluate, verify the annotation JSON is readable and the referenced media path or archive is visible from the selected platform. Do not block, patch, or mutate annotations solely because optional fields are absent.
- Per-record video FPS: the packaged train template uses
custom.vision.nframes, so per-recordvideo_fpsis not required by default. If the user switches tocustom.vision.fps, selects a dataset profile that requires per-record timing, or uses an image/version that requiresvideo_fps, make it a preflight requirement with--json-required-field train_annotation=video_fpsand--json-required-field val_annotation=video_fpsbefore any download or job launch.
Spec construction
cosmos-rl is mode: config. Always start from the packaged
references/spec_template_<action>.yaml for the requested action — load it
as your base spec via yaml.safe_load(...) and apply user overrides on top.
Don't rebuild from scratch.
import yaml
from pathlib import Path
skill = Path.home() / "tao-sdk/tao-skills-external/skills/models/tao-finetune-cosmos-reason"
action = "train" # train, evaluate, inference, or quantize
specs = yaml.safe_load((skill / f"references/spec_template_{action}.yaml").read_text())
# Now apply your overrides on top of `specs`.
The reference TOML (and the spec the model actually consumes) is nested
dicts, not flat dotted keys. Dotted notation in override examples denotes
paths into the nested spec — walk the path and assign at the leaf. See
skills/platform/tao-run-platform/SKILL.md's "spec is nested dicts" callout.
Data source overrides are mandatory for every action.
The packaged template keeps custom.vision.nframes=8 for bounded 1-GPU memory;
switch to fps only after checking token budget and GPU memory, and delete
custom.vision.nframes from the spec when you do.
See references/cosmos-reason-launch.md for launch intake, the full
check_tao_launch_preflight.py slurm/local-Docker examples, the
video_fps preflight example, S3 staging, the GPU resource/architecture gate,
the per-action dataset requirements table, the /workspace mount caveat,
the quantize compatibility shim, and the full typical-overrides list.
Critical Overrides (Train)
These are the keys whose template defaults are wrong or where omission flips the run into a different mode:
| Parameter | Template Default | Required Value | Why |
|---|---|---|---|
policy.model_name_or_path | hf_model://nvidia/Cosmos3-Nano | Direct Docker: nvidia/Cosmos3-Nano, hf_model://nvidia/Cosmos3-Nano, or a local HF snapshot path. SDK/managed platform predownload: hf_model://nvidia/Cosmos3-Nano. | Keep the train and evaluate base model aligned. |
policy.model_max_length | 40960 | Keep at 40960 or higher | Smaller than ~40k causes vision_embeds shape mismatch on video inputs |
train.train_batch_per_replica | 32 | Any multiple of train.train_policy.mini_batch | Mismatch raises an immediate AssertionError |
train.train_policy.type | "sft" | Keep as "sft" for SFT workflows | If dropped during agent regeneration, cosmos-rl flips to RL mode → rollout replica allocated → multi-node attempted → hostname errors when num_nodes=1 |
Evaluate
The evaluator reads a flat TOML config (dataset, model, task,
evaluation, vision, generation, metrics, results, num_gpus,
results_dir); the actions.evaluate block in references/skill_info.yaml
declares inputs and outputs. See references/cosmos-reason-evaluate.md for the
flat-TOML config detail, task types ("" General Evaluator vs
"its_directionality"), LoRA evaluation via spec_overrides, selective download,
results/metrics, and the datasets section.
AutoML / HPO Notes
The packaged default base model is hf_model://nvidia/Cosmos3-Nano; apply it
consistently to train (policy.model_name_or_path) and post-training evaluation
(model.base_model_path) unless the user provides a different model. See
references/cosmos-reason-automl.md for accuracy-vs-val/avg_loss objective
selection, the eval_fn per-recommendation evaluate flow, the knob mapping
(learning rate, batch size, epochs, weight decay, warmup ratio), example
custom_param_ranges, train_sample_count batch-size capping,
ordered_int requirements, and the pre-launch recommendation summary.
Parameters, Hardware, Errors, DEFT, Inference
For parallelism, set policy.parallelism.dp_shard_size = GPUs per node and
policy.parallelism.dp_replicate_size = node count (1 for single node).
Cosmos-RL handles distributed init internally via FSDP and does not rely on
platform-level MASTER_ADDR/WORLD_SIZE; submit with
gpu_count=<gpus_per_node> and num_nodes=<N> and the spec keys drive
sharding. Cosmos-RL models are 8B parameters; recommended 8x A100 or H100
(80GB each).
See references/cosmos-reason-parameters.md for important parameters (training
loop, model/policy, parallelism incl. multi-node FSDP, optimization, vision
encoders, checkpointing incl. the best symlink/epoch_* resolution,
validation, logging), hardware sizing, the full error-pattern catalog (CUDA OOM,
LoRA-eval OOM, NaN loss, vision_embeds mismatch, quantize token mismatch,
batch-size divisibility and per-rank limits, stale cache, scheduler-None,
gated-repo HF_TOKEN, GPU resource/architecture gate, status-logging warnings),
DEFT support and scripts/analyze_gaps.py gap analysis, and the parent-model
inference mapping table.
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