How do I install the Venus Finetune Workflow skill?

Run npx skillmds@latest add ai4protein/venus-finetune-workflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Venus Finetune Workflow safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Venus Finetune Workflow?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Venus Finetune Workflow free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published Venus Finetune Workflow?

ai4protein (@ai4protein) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Venus Finetune Workflow by ai4protein

Name: Venus Finetune Workflow
Author: ai4protein

Venus Finetune Workflow

Fine-tune and run custom protein models on VenusFactory (CSV/HF → config → train → predict). Use when the user brings labeled sequences, wants adapter training (ProtT5/ESM2/Ankh/QLoRA notes), or batch inference with a trained config. Do NOT use for zero-shot mutation without labels (zero_shot_mutation_workflow) or built-in function heads already covered by predict_protein_function.

ai4protein Updated Sep 17, 2026

File contents

Venus Fine-Tune Workflow

Overview

Orchestrates training hub tools. Expect CSV columns such as aa_seq / sequence + label. GPU recommended; training can be long — set clear success criteria (metrics file / checkpoint exists).

Project Tools (VenusFactory2)

Tool	Args	Purpose
generate_training_config	`csv_file` or `dataset_path`; optional valid/test CSV; `user_requirements`; `output_name`	Build training JSON
train_protein_model	`config_path`	Run training; auto-registers under `ckpt/user_trained/<model_id>/`
register_trained_model	`config_path`; optional `model_id` / `output_dir` / `model_path`	Explicit re-register (usually unnecessary after train)
list_trained_models	—	List cross-session registered models
protein_model_predict	`config_path` or `model_id` + `sequence` or `csv_file`	Inference
agent_generated_code	`task_description`, `input_files`, `output_dir`	Splits, metrics plots, CSV cleanup (Expert only; not MCP)

Mode: local only. Online mode disables these tools (and VenusMine / FoldSeek discovery). Science Agent reaches train tools via MCP; Expert via the LangGraph tool hub.

Workflow

Validate/split data with agent_generated_code if needed (70/15/15) — Expert path.
generate_training_config with explicit user_requirements (model, epochs, LR, QLoRA…).
train_protein_model on the returned config path (dependency:step_N:file_path). On success, use registered_model_id / registered_config_path from the result.
protein_model_predict with the registered model_id (or config path) for hold-out or new sequences — works across sessions.
Figure: loss/accuracy curves from training metrics (dpi≥300).

When NOT to use

Unlabeled mutagenesis ranking → zero_shot_mutation_workflow
Off-the-shelf solubility/temp heads → predict_protein_function (protein_property_prediction)

Common mistakes

Omitting both csv_file and dataset_path
Predicting without the same config_path used for training
Writing artifacts outside the session output_dir

---name: venus-finetune-workflowdescription: Fine-tune and run custom protein models on VenusFactory (CSV/HF → config → train → predict). Use when the user brings labeled sequences, wants adapter training (ProtT5/ESM2/Ankh/QLoRA notes), or batch inference with a trained config. Do NOT use for zero-shot mutation without labels (zero_shot_mutation_workflow) or built-in function heads already covered by predict_protein_function.license: Apache-2.0---# Venus Fine-Tune Workflow## OverviewOrchestrates training hub tools. Expect CSV columns such as `aa_seq` / `sequence` + `label`. GPU recommended; training can be long — set clear success criteria (metrics file / checkpoint exists).## Project Tools (VenusFactory2)| Tool | Args | Purpose ||------|------|---------|| **generate_training_config** | `csv_file` **or** `dataset_path`; optional valid/test CSV; `user_requirements`; `output_name` | Build training JSON || **train_protein_model** | `config_path` | Run training; **auto-registers** under `ckpt/user_trained/<model_id>/` || **register_trained_model** | `config_path`; optional `model_id` / `output_dir` / `model_path` | Explicit re-register (usually unnecessary after train) || **list_trained_models** | — | List cross-session registered models || **protein_model_predict** | `config_path` **or** `model_id` + `sequence` **or** `csv_file` | Inference || **agent_generated_code** | `task_description`, `input_files`, `output_dir` | Splits, metrics plots, CSV cleanup (Expert only; not MCP) |**Mode:** local only. Online mode disables these tools (and VenusMine / FoldSeek discovery). Science Agent reaches train tools via MCP; Expert via the LangGraph tool hub.## Workflow1. Validate/split data with `agent_generated_code` if needed (70/15/15) — Expert path.2. `generate_training_config` with explicit `user_requirements` (model, epochs, LR, QLoRA…).3. `train_protein_model` on the returned config path (`dependency:step_N:file_path`). On success, use `registered_model_id` / `registered_config_path` from the result.4. `protein_model_predict` with the registered `model_id` (or config path) for hold-out or new sequences — works across sessions.5. Figure: loss/accuracy curves from training metrics (dpi≥300).## When NOT to use- Unlabeled mutagenesis ranking → `zero_shot_mutation_workflow`- Off-the-shelf solubility/temp heads → `predict_protein_function` (`protein_property_prediction`)## Common mistakes- Omitting both `csv_file` and `dataset_path`- Predicting without the **same** `config_path` used for training- Writing artifacts outside the session `output_dir`

Frequently asked questions