ai-research-explore
The ai-research-explore skill facilitates candidate-only deep learning research exploration. It operates on a durable current_research anchor to manage experiments, rank ideas, and produce auditable outputs while maintaining scientific rigor.
Is ai-research-explore safe to install?
Review before installing: our audit of ai-research-explore's source files found 2 shell commands, 0 external URLs, file reads and writes (medium risk). Every command and URL listed appears verbatim in the skill's source. The skill reads local reference files and writes research outputs to directories including explore_outputs/ and analysis_outputs/.
How we audit skills: our security review methodology.
Who is this skill for?
Researchers conducting deep learning experiments who have established a task, dataset, benchmark, and evaluation method.
What can you do with it?
- Exploratory ranking of research candidates
- Bounded single-variable seed idea generation
- Campaign-based research with frozen evaluation sources
- Auditable experiment tracking and reporting
How good is this skill?
Quality score: 9/10. The skill documentation is clear and provides specific operational constraints. It correctly identifies the required file dependencies and workflow steps.
What does the skill file contain?
# ai-research-explore ## Purpose Use this as the Rigor Explore compatible skill slug after the researcher explicitly authorizes candidate-only work on top of a durable `current_research` anchor. The installed slug remains `ai-research-explore` for compatibility. Rigor Explore is for meaningful and potentially novel deep learning research candidates while preserving scientific rigor, comparability, reproducibility, and auditable collaboration. Novelty and significance remain hypotheses before literature contrast, ablation evidence, and fair comparison. The skill does not promise autonomous di...
Frequently asked questions
What is the required context for this skill?
The researcher must provide a durable current_research anchor, such as a branch, commit, or checkpoint, and explicit authorization for candidate-only work.
Does this skill perform autonomous research?
No. The skill does not promise autonomous discovery or global benchmark completeness. It requires human checkpoints and follows a defined two-loop rhythm.
Where are the research outputs saved?
The skill writes outputs to analysis_outputs/, sources/, and explore_outputs/.
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