Research Question Loop

Research Question Loop is an AI Engineering skill for Claude Code, published by gaasher in Agent-Loop-Skills.

166 stars19 forkson gaasher/Agent-Loop-SkillsAdded 2026/09/08+2% in starsRepository updated 2026/06/30

agent-skillsagentic-loopsagentic-workflowsai-agentsanthropicautoresearchclaudeclaude-codedata-analysisliterature-reviewllm-agentsmachine-learningml-autoresearchopen-sourceprompt-engineeringred-teamingscientific-writingskillssubagents

Install in seconds

Install Research Question Loop

Copy Research Question Loop into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/research-question ~/.claude/skills/research-question

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/gaasher/Agent-Loop-Skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: loops/research-question/SKILL.md in gaasher/Agent-Loop-Skills
Installs to: ~/.claude/skills/research-question
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What Research Question Loop does

Research Question Loop sharpens a vague topic into a few strong research questions. It drafts candidates, scores them with a Specific/Answerable/Novel/Feasible/Significant rubric, and revises weak areas until enough clear the bar.

Research Question Loop is cataloged under AI Engineering on DirSkills. Research Question Loop comes from a repository tagged agent-skills, agentic-loops, agentic-workflows, ai-agents and anthropic.

Documentation

README

Excerpt

Research Question Loop

A sharpen → score → revise loop for the framing stage of research. The artifact is a small set of research questions; the feedback signal is how many clear the bar — each scored 0-5 on five fixed axes (Specific, Answerable, Novel, Feasible, Significant). You start from a vague topic, draft candidates, score each against the rubric (with a light novelty check against the literature), and rewrite the weakest axis of the promising ones until enough are strong.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Research Question Loop

What else does gaasher publish alongside Research Question Loop?
Research Question Loop is one of 25 skills that DirSkills catalogs from gaasher/Agent-Loop-Skills, the repository it ships in. Its siblings there include Alpha Evolve, Anomaly Investigation and Blue Team. Each one is a separate skill with its own page in this directory, installs the same way Research Question Loop does, and is maintained by gaasher in that same repository. The rest of the collection is listed on the gaasher/Agent-Loop-Skills page.
How does Research Question Loop compare to other AI Engineering skills?
Research Question Loop ranks #3348 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Research Question Loop against them. Open each page to compare what they document and how they install.