---

name: skywork-r1v description: "Route Skywork-R1V3 local inference, R1V4 API batch testing, and benchmark reproduction workflows to the right bundled sub-skill." disable-model-invocation: true metadata: disco-role: operating license: MIT

Skywork-R1V

Use this repo skill when a user asks about the Skywork multimodal reasoning repository, especially Skywork-R1V3 local inference, Skywork R1V4 API batch testing, or evaluation reproduction with VLMEvalKit, EMMA-mini, and MMK12.

This root skill is a router. It keeps the fast path short and sends detailed commands, parameters, and failure handling to the sub-skill that owns the workflow.

First choices

• Need local Transformers or vLLM inference for Skywork-R1V3? Go to sub-skills/local-inference/.
• Need R1V4 batch request payloads, response parsing, or result summaries? Go to sub-skills/r1v4-api-testing/.
• Need Skywork evaluation reproduction, benchmark launch commands, or score post-processing? Go to sub-skills/evaluation-reproduction/.

What this skill covers

• Skywork-R1V3 local inference command construction and image-patch reasoning.
• Skywork R1V4 API batch testing and tagged response parsing.
• VLMEvalKit, EMMA-mini, and MMK12 evaluation reproduction.
• Safe helper scripts for command building, schema checks, parsing, and output inspection.

What this skill does not do

• It does not load the 38B model, launch vLLM, or run the heavy evaluation suites by itself.
• It does not make live API calls, download checkpoints, or request credentials unless the chosen sub-skill says so.
• It does not replace the need for a prepared CUDA/GPU runtime when the user is actually running local inference.

Minimal setup and sanity check

There is no single top-level Python package to install for this repository. For the bundled helper scripts, use a normal Python environment and install only the small helper stack when needed:

python -m pip install requests tqdm flask pillow pyyaml pandas openpyxl
python scripts/validate_skill_runtime.py --root .

Install CUDA, vLLM, Transformers, model weights, datasets, or judge/API clients only when the chosen sub-skill explicitly requires them for a real run.

Quick routing signals

Local inference

Use local-inference when the request mentions:

• Skywork/Skywork-R1V3-38B
• inference_with_transformers.py
• inference_with_vllm.py
• split_model, tensor_parallel_size, flash-attn, trust_remote_code
• multi-image prompts, image patch counts, or CUDA OOM while serving the model

Read:

• sub-skills/local-inference/references/workflows.md
• sub-skills/local-inference/references/api-and-parameters.md
• sub-skills/local-inference/references/troubleshooting.md

Run:

• sub-skills/local-inference/scripts/build_inference_command.py
• sub-skills/local-inference/scripts/check_image_grid.py

R1V4 API testing

Use r1v4-api-testing when the request mentions:

• r1v4-lite or r1v4-vl-planner-lite
• batch_nonstream.py, batch_stream.py, or planner variants
• test_cases.jsonl, <think>, <tool_call>, <observation>, <answer> tags
• result JSONL summaries, MIME types, or a safe batch payload preview

Read:

• sub-skills/r1v4-api-testing/references/api-batch-workflows.md
• sub-skills/r1v4-api-testing/references/data-formats.md
• sub-skills/r1v4-api-testing/references/result-analysis.md
• sub-skills/r1v4-api-testing/references/troubleshooting.md

Run:

• sub-skills/r1v4-api-testing/scripts/validate_cases.py
• sub-skills/r1v4-api-testing/scripts/build_api_payload.py
• sub-skills/r1v4-api-testing/scripts/parse_r1v4_response.py
• sub-skills/r1v4-api-testing/scripts/summarize_results.py

Evaluation reproduction

Use evaluation-reproduction when the request mentions:

• eval/README.md
• VLMEvalKit launch or benchmark scripts
• EMMA-mini generation and scoring
• MMK12 generation or judge scoring
• MMMU, LogicVista, PhyX, r1v3-alpha, USE_COT, or a served OpenAI-compatible model

Read:

• sub-skills/evaluation-reproduction/references/vlmevalkit.md
• sub-skills/evaluation-reproduction/references/emma-mmk12.md
• sub-skills/evaluation-reproduction/references/data-and-results.md
• sub-skills/evaluation-reproduction/references/troubleshooting.md

Run:

• sub-skills/evaluation-reproduction/scripts/build_eval_commands.py
• sub-skills/evaluation-reproduction/scripts/score_boxed_answers.py
• sub-skills/evaluation-reproduction/scripts/check_eval_outputs.py

Helper scripts at the root

• scripts/validate_skill_runtime.py checks that the generated tree still has the expected router layout and does not contain obvious path leaks.

Maintenance note

When you need to check whether this skill still matches the current repository state, read references/repo-provenance.md first. That file records the source commit and evidence paths used to build the skill.