ai-research-explore
๐ฏSkillfrom lllllllama/rigorpilot-skills
|The primary explore-lane orchestrator in the RigorPilot suite that enables meaningful and potentially novel exploration on top of current deep learning research, with idea gating, bounded experiments, and candidate ranking.
Overview
AI Research Explore is the primary explore-lane orchestrator in the RigorPilot suite. It enables meaningful and potentially novel exploration on top of current deep learning research, with idea gating, bounded experiments, and structured evidence collection. This skill only activates when the researcher explicitly requests exploratory work, and it keeps all candidate experiments isolated from the trusted research baseline.
Key Features
- Idea gating - Evaluates proposed exploration ideas for feasibility and scientific merit before committing compute and effort to running them
- Bounded experiments - Structures each exploration as a well-defined experiment with clear hypotheses, success criteria, and resource limits
- Candidate-only isolation - All exploratory work runs on top of
current_researchwithout modifying it, preserving the ability to compare against the original baseline - Structured evidence collection - Saves all exploration results, configurations, and comparisons to
explore_outputs/with full provenance - Explicit-only activation - Never drifts into exploration mode on its own; requires a clear researcher directive to switch from the trusted to the explore lane
Who is this for?
- Deep learning researchers who have successfully reproduced a paper's results and now want to systematically explore improvements or variations
- Research teams that need a structured way to test hypotheses on top of existing codebases without contaminating the baseline
- ML practitioners who want to maintain scientific rigor while iterating on novel ideas in an auditable, traceable manner
Same repository
lllllllama/rigorpilot-skills(14 items)
Installation
npx vibeindex add lllllllama/rigorpilot-skills --skill ai-research-explorenpx skills add lllllllama/rigorpilot-skills --skill ai-research-explore~/.claude/skills/ai-research-explore/SKILL.mdSKILL.md
More from this repository10
The primary end-to-end orchestrator in the RigorPilot suite that guides AI agents through README-first reproduction of deep learning repositories, ensuring scientific rigor, comparability, and auditable outputs.
A RigorPilot explore-lane skill for candidate implementation, code transplanting, and stitching on isolated branches when the researcher has explicitly authorized exploratory work on top of current research.
A helper skill in the RigorPilot suite that resolves gaps between a deep learning repository's README and its associated research paper, helping agents understand the paper context needed for faithful reproduction.
A RigorPilot trusted-lane skill for research-safe debugging of deep learning experiments that analyzes failures first and applies patches only after explicit approval, preserving scientific comparability.
A RigorPilot trusted-lane skill that performs read-only project analysis on deep learning repositories, including model mapping, structure inspection, and risk surfacing, without making any edits.
A RigorPilot helper skill that scans deep learning repositories and extracts README commands to build an initial reproduction plan, typically invoked by the main reproduction orchestrator.
A RigorPilot trusted-lane skill for conservatively starting or verifying deep learning training runs, including resume handling, bounded monitoring, and generating durable training records.
A RigorPilot trusted-lane skill for conservatively setting up deep learning environments, including datasets, pretrained weights, checkpoints, and cache assumptions required for research reproduction.
A RigorPilot explore-lane skill for running small-subset probes, short-cycle trials, and ranked exploratory experiments when the researcher explicitly authorizes candidate-only exploration.
A RigorPilot trusted-lane skill for conservatively running documented evaluation, inference, smoke tests, and sanity checks in deep learning repositories without introducing unaudited changes.