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Agent skill

Rd Agent Guide

by wentorai in wentorai/research-plugins

Microsoft AI-driven R&D agent for automated data and model development

MITAuto-check passedData & Analytics

Install Rd Agent Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill rd-agent-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins rd-agent-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/automation/rd-agent-guide .claude/skills/rd-agent-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rd-agent-guide
GitHub stars
298
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
436 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Microsoft AI-driven R&D agent for automated data and model development

  • Works in 4 steps: Proposal: The agent analyzes the current… → Implementation: Hypotheses are… → Evaluation: The implemented changes are… → …
  • Tasks that involve Machine learning
  • SKILL.md covers Overview, Installation and Setup, Core Concepts and Advanced Features, plus 2 more sections
  • Calls pip, git and docker; reaches github.com; needs OPENAI_API_KEY and AZURE_OPENAI_API_KEY

What it does

Rd Agent Guide is an agent skill from wentorai/research-plugins. Microsoft AI-driven R&D agent for automated data and model development

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Machine learning

Example prompts

  • “/rd-agent-guide”

Requirements

  • Python 3
  • Docker
  • A credential in OPENAI_API_KEY
  • A credential in AZURE_OPENAI_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Proposal: The agent analyzes the current state and proposes new hypotheses or improvements
  2. Implementation: Hypotheses are translated into executable code (feature engineering, model changes, etc.)
  3. Evaluation: The implemented changes are executed in a sandbox and results are measured against defined metrics
  4. Feedback: Results are analyzed and used to inform the next round of proposals

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • git
    • docker

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • microsoft.com
    • microsoft.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY
    • AZURE_OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Rd Agent Guide loads about 1.9k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 436 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 436 words, ~1,939 tokens.

Download SKILL.mdSave it as .claude/skills/rd-agent-guide/SKILL.md (or your agent's skills folder).
name
rd-agent-guide
description
Microsoft AI-driven R&D agent for automated data and model development

RD-Agent Guide

Overview

RD-Agent is an open-source AI-powered research and development automation framework developed by Microsoft Research, with over 12,000 stars on GitHub. It automates key steps in the R&D lifecycle -- including hypothesis generation, experiment design, code implementation, and result analysis -- enabling researchers and data scientists to accelerate their development cycles significantly.

The framework implements a closed-loop R&D automation pipeline where an AI agent iteratively proposes hypotheses, implements experiments, evaluates results, and refines its approach based on feedback. This mirrors the scientific method but operates at machine speed, allowing researchers to explore a much larger space of ideas and configurations than would be feasible manually.

RD-Agent is particularly valuable for researchers working in quantitative finance, data science, and machine learning, where the development process involves iterating on feature engineering, model architectures, and hyperparameter configurations. The framework has demonstrated the ability to autonomously develop competitive machine learning models and trading strategies, achieving results comparable to experienced human practitioners.

Installation and Setup

bash
# Clone the repository
git clone https://github.com/microsoft/RD-Agent.git
cd RD-Agent

# Install dependencies
pip install -e .

# Or install from PyPI
pip install rdagent
Environment Configuration
bash
# LLM configuration (required)
export OPENAI_API_KEY=$OPENAI_API_KEY
export CHAT_MODEL=gpt-4o

# Or use Azure OpenAI
export AZURE_OPENAI_API_KEY=$AZURE_OPENAI_API_KEY
export AZURE_OPENAI_ENDPOINT=$AZURE_OPENAI_ENDPOINT
export AZURE_OPENAI_DEPLOYMENT=$AZURE_OPENAI_DEPLOYMENT

# Docker is required for sandboxed code execution
# Ensure Docker is installed and running
docker --version

RD-Agent uses Docker containers to execute generated code safely, ensuring that automatically generated experiments cannot affect the host system. This sandboxed execution is critical for an autonomous agent that writes and runs arbitrary code.

Core Concepts

The R&D Loop

RD-Agent implements a continuous improvement loop with four phases:

  1. Proposal: The agent analyzes the current state and proposes new hypotheses or improvements
  2. Implementation: Hypotheses are translated into executable code (feature engineering, model changes, etc.)
  3. Evaluation: The implemented changes are executed in a sandbox and results are measured against defined metrics
  4. Feedback: Results are analyzed and used to inform the next round of proposals
python
from rdagent.core.runner import RDRunner
from rdagent.scenarios.data_science import DataScienceScenario

# Define the research scenario
scenario = DataScienceScenario(
    task="tabular_classification",
    dataset_path="path/to/dataset.csv",
    target_column="label",
    metric="auc",
)

# Create and run the R&D agent
runner = RDRunner(
    scenario=scenario,
    max_iterations=50,
    llm_model="gpt-4o",
)

# Start the autonomous R&D loop
results = runner.run()

# Review the best solution found
print(f"Best metric: {results.best_score}")
print(f"Iterations: {results.total_iterations}")
print(f"Solutions explored: {results.num_solutions}")
Show full SKILL.md (168 more words)Show less
Scenario Types

RD-Agent supports multiple R&D scenarios out of the box:

Data Science / Kaggle Competitions

Automatically engineer features, select models, and tune hyperparameters for tabular data tasks:

python
from rdagent.scenarios.data_science import DataScienceScenario

scenario = DataScienceScenario(
    task="tabular_regression",
    dataset_path="data/housing.csv",
    target_column="price",
    metric="rmse",
    time_budget_hours=4,
)
Quantitative Finance

Develop and backtest trading factors and strategies:

python
from rdagent.scenarios.qlib import QlibScenario

scenario = QlibScenario(
    market="csi300",
    task="alpha_factor_mining",
    backtest_start="2020-01-01",
    backtest_end="2024-12-31",
    metric="information_coefficient",
)
Model Development

Iterate on model architectures and training procedures:

python
from rdagent.scenarios.model_dev import ModelDevScenario

scenario = ModelDevScenario(
    task="image_classification",
    base_model="resnet50",
    dataset="cifar100",
    optimization_target="accuracy",
)

Advanced Features

Experiment Tracking and Analysis

RD-Agent maintains detailed logs of all experiments, enabling post-hoc analysis of the R&D process:

python
# Access experiment history
for experiment in results.history:
    print(f"Iteration {experiment.iteration}:")
    print(f"  Hypothesis: {experiment.hypothesis}")
    print(f"  Changes: {experiment.code_changes}")
    print(f"  Metric: {experiment.score}")
    print(f"  Analysis: {experiment.feedback}")
Custom Evaluation Functions

Define custom evaluation metrics for domain-specific research:

python
from rdagent.core.evaluation import EvaluationFunction

class CustomMetric(EvaluationFunction):
    def evaluate(self, predictions, ground_truth, **kwargs):
        # Your custom metric computation
        score = compute_domain_specific_metric(predictions, ground_truth)
        return {
            "primary_metric": score,
            "secondary_metrics": {
                "precision": compute_precision(predictions, ground_truth),
                "recall": compute_recall(predictions, ground_truth),
            }
        }

scenario = DataScienceScenario(
    evaluation_function=CustomMetric(),
    # ... other config
)
Human-in-the-Loop Mode

Guide the agent with human feedback at key decision points:

python
runner = RDRunner(
    scenario=scenario,
    human_in_the_loop=True,
    review_frequency=5,  # Review every 5 iterations
)

# The agent will pause for human review at specified intervals
# You can approve, reject, or modify proposed experiments

Research Applications

Ablation Studies at Scale

Use RD-Agent to systematically explore which components contribute most to model performance:

python
# Define ablation study
ablation_config = {
    "base_model": "your_full_model",
    "components_to_ablate": [
        "attention_mechanism",
        "residual_connections",
        "layer_normalization",
        "data_augmentation",
    ],
    "metric": "accuracy",
    "num_seeds": 5,  # Run each configuration with 5 seeds
}
Automated Feature Engineering

Let the agent discover and implement novel features for your dataset:

python
scenario = DataScienceScenario(
    task="feature_engineering",
    dataset_path="data/research_data.csv",
    existing_features=["feature_a", "feature_b", "feature_c"],
    target="outcome",
    max_new_features=20,
)
Reproducibility

Every experiment run by RD-Agent is fully reproducible. The framework saves the complete experiment specification including code, data transformations, random seeds, and environment details, enabling other researchers to reproduce and build upon the results.

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research/automation/rd-agent-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Rd Agent Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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scikit-survival Time-to-Event Modelingdavila7/claude-code-templates32k12 repos~3.7kAutomated safety check: PassMIT
Neuropixels AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~5kAutomated safety check: PassMIT
Molfeatdavila7/claude-code-templates32k10 repos~3.7kAutomated safety check: PassMIT
IcmlnanoAgentTeam/research-claw293—~2.4kAutomated safety check: PassMIT

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Categories

Questions about Rd Agent Guide

What does Rd Agent Guide do?

Microsoft AI-driven R&D agent for automated data and model development. Rd Agent Guide is an agent skill from wentorai/research-plugins.

When should I use Rd Agent Guide?

Rd Agent Guide fits situations like: tasks that involve Machine learning.

How do I install Rd Agent Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill rd-agent-guide -a claude-code`. Or copy the skill folder (skills/research/automation/rd-agent-guide in wentorai/research-plugins) into .claude/skills/rd-agent-guide in your project. Claude Code loads it when a task matches its description.

How do I install Rd Agent Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill rd-agent-guide -a codex`. Or copy the skill folder (skills/research/automation/rd-agent-guide in wentorai/research-plugins) into .agents/skills/rd-agent-guide in your project. Codex loads it when a task matches its description.

Can I use Rd Agent Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill rd-agent-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rd-agent-guide, .gemini/skills/rd-agent-guide, .github/skills/rd-agent-guide and .opencode/skills/rd-agent-guide in your project.

What does Rd Agent Guide need to run?

Going by SKILL.md and its folder, Rd Agent Guide needs the command-line tools its instructions call (pip, git and docker) and credentials named OPENAI_API_KEY and AZURE_OPENAI_API_KEY. Our summary lists: Python 3; Docker; A credential in OPENAI_API_KEY; A credential in AZURE_OPENAI_API_KEY.

Does Rd Agent Guide access the network?

SKILL.md names 3 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: microsoft.com and microsoft.github.io. This is read from the text; nothing was executed.

Is Rd Agent Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Rd Agent Guide use?

Rd Agent Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Rd Agent Guide use?

About 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Rd Agent Guide?

Skills that share tags, products or a category with Rd Agent Guide: Lammps Deepmd (Hello-QM/catgo-LRG, 205 stars), scikit-survival Time-to-Event Modeling (davila7/claude-code-templates, 32k stars), Neuropixels Analysis (K-Dense-AI/scientific-agent-skills, 48k stars) and Molfeat (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rd Agent Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.