How do I install the Ml Research Lab skill?

Run npx skillmds@latest add anastasiyaw/ml-research-lab 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 Ml Research Lab 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 Ml Research Lab?

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 Ml Research Lab free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ml Research Lab?

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

Ml Research Lab by anastasiyaw — Agent Skill

Name: Ml Research Lab
Author: AnastasiyaW

Ml Research Lab

Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. Use when working on ML experiments, training data, model benchmarks, RunPod/GPU runs, classifier quality, vLLM/GGUF serving, SHAP-style model explanations, or research-to-code iterations. Do not use for a simple code edit that has no ML dataset, metric, model, or experiment artifact.

AnastasiyaW Updated Sep 17, 2026

File contents

ML Research Lab

Use this skill as the compact router for ML work. It is derived from an audit of synthetic-sciences/openscience at commit 531467c, but does not require running OpenScience or loading its full 250+ skill set.

Operating Loop

Freeze the question as a measurable hypothesis.
Identify dataset provenance, labels, splits, leakage risks, and regeneration cost.
Pick the smallest baseline that can disprove the idea.
Define metrics before training. For release claims, require train/val/test split, no test-set model selection, and multi-seed proof when cost permits.
Run or wire experiment tracking before long jobs start.
Save artifacts: config, command, data manifest, metrics JSON/CSV, logs, model hash, and a short conclusion.
Compare against baseline, then keep/discard the change from evidence.

Domain Routing

Dataset or scrape cleanup: start from data quality, deduplication, leakage checks, train/eval splits, and regeneration notes.
Classical classifier or tabular baseline: use scikit-learn-style pipelines with preprocessing inside the pipeline and stratified splits for classification.
Model debugging or trust: add SHAP/explainability for feature importance, leakage, bias/proxy features, and misclassified samples.
LLM fine-tuning: prefer JSONL chat format, data validation, LoRA/QLoRA baseline, and tracked runs before scaling.
Single-GPU fast LoRA/QLoRA: consider Unsloth only after checking hardware, CUDA, model support, and export target.
Large or production inference: use vLLM for high-throughput GPU serving, GGUF or llama.cpp for local/Apple/CPU-friendly deployment, and TensorRT-LLM only when the NVIDIA production optimization cost is justified.
Research write-up: report method, dataset, exact metric formula, baseline source, limitations, and failure cases.

Verification Gates

Data gate: schema valid, duplicates/leakage checked, split manifest saved.
Metric gate: exact metric formula named; if benchmarked, original baseline source and benchmark code checked.
Runtime gate: command/log path and environment captured; GPU memory and errors checked for long runs.
Tracking gate: metrics are retrievable as JSON/CSV or a dashboard link plus local export.
Deployment gate: latency, throughput, memory, and OOM behavior measured before claiming production readiness.

Adoption Boundary

Do not import broad external skill collections wholesale. Use the inventory script scripts/openscience_skill_inventory.py to rank candidates, inspect the relevant source skill manually, then promote only compact workflows or deterministic scripts that improve our own tests.

---name: ml-research-labdescription: Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. Use when working on ML experiments, training data, model benchmarks, RunPod/GPU runs, classifier quality, vLLM/GGUF serving, SHAP-style model explanations, or research-to-code iterations. Do not use for a simple code edit that has no ML dataset, metric, model, or experiment artifact.---# ML Research LabUse this skill as the compact router for ML work. It is derived from an audit of`synthetic-sciences/openscience` at commit `531467c`, but does not require runningOpenScience or loading its full 250+ skill set.## Operating Loop1. Freeze the question as a measurable hypothesis.2. Identify dataset provenance, labels, splits, leakage risks, and regeneration cost.3. Pick the smallest baseline that can disprove the idea.4. Define metrics before training. For release claims, require train/val/test split, no test-set model selection, and multi-seed proof when cost permits.5. Run or wire experiment tracking before long jobs start.6. Save artifacts: config, command, data manifest, metrics JSON/CSV, logs, model hash, and a short conclusion.7. Compare against baseline, then keep/discard the change from evidence.## Domain Routing- Dataset or scrape cleanup: start from data quality, deduplication, leakage checks, train/eval splits, and regeneration notes.- Classical classifier or tabular baseline: use scikit-learn-style pipelines with preprocessing inside the pipeline and stratified splits for classification.- Model debugging or trust: add SHAP/explainability for feature importance, leakage, bias/proxy features, and misclassified samples.- LLM fine-tuning: prefer JSONL chat format, data validation, LoRA/QLoRA baseline, and tracked runs before scaling.- Single-GPU fast LoRA/QLoRA: consider Unsloth only after checking hardware, CUDA, model support, and export target.- Large or production inference: use vLLM for high-throughput GPU serving, GGUF or llama.cpp for local/Apple/CPU-friendly deployment, and TensorRT-LLM only when the NVIDIA production optimization cost is justified.- Research write-up: report method, dataset, exact metric formula, baseline source, limitations, and failure cases.## Verification Gates- Data gate: schema valid, duplicates/leakage checked, split manifest saved.- Metric gate: exact metric formula named; if benchmarked, original baseline source and benchmark code checked.- Runtime gate: command/log path and environment captured; GPU memory and errors checked for long runs.- Tracking gate: metrics are retrievable as JSON/CSV or a dashboard link plus local export.- Deployment gate: latency, throughput, memory, and OOM behavior measured before claiming production readiness.## Adoption BoundaryDo not import broad external skill collections wholesale. Use the inventory script`scripts/openscience_skill_inventory.py` to rank candidates, inspect the relevantsource skill manually, then promote only compact workflows or deterministic scriptsthat improve our own tests.

Frequently asked questions