Hypothesis Generation Loop

Hypothesis Generation 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 Hypothesis Generation Loop

Copy Hypothesis Generation 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/hypothesis-gen ~/.claude/skills/hypothesis-gen

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

What Hypothesis Generation Loop does

Hypothesis Generation Loop generates candidate research hypotheses and vets them against retrieved literature. Use it when you need a ranked pool of novel, testable hypotheses rather than a single research question or a proposal review.

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

Documentation

README

Excerpt

Hypothesis Generation Loop

A multi-agent, literature-grounded generation loop. The artifact is a growing pool of hypotheses; the feedback signal is the count of strong, distinct hypotheses that clear the bar — where "strong" is decided against real retrieved literature, not assertion. Each round: generate → ground → judge → keep → mutate toward the gaps, until the pool stops growing (saturation).

The discipline: a hypothesis enters the pool only if the literature says it is not already established (novelty), prior work makes it plausible (grounding), and a feasible test exists. Generating is not confirming — the output is a ranked set of strong candidates to test, each stated with how to test it.

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

Frequently asked about Hypothesis Generation Loop

What else does gaasher publish alongside Hypothesis Generation Loop?
Hypothesis Generation 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 Hypothesis Generation 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 Hypothesis Generation Loop compare to other AI Engineering skills?
Hypothesis Generation Loop ranks #3329 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 Hypothesis Generation Loop against them. Open each page to compare what they document and how they install.