Agent skill · research science · aiskillstore
ai-research-explore
Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.
Why this skill is useful
Provides structured workflows and methodologies for deep learning research exploration that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 currentresearch 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 discovery, global benchmark completeness, novelty proof, or trusted reproduction success. Start from the shared operating principles in ../../references/agent-operating-principles.md, then load ../../references/research-rigor-principles.md for research claims and ../../references/deep-learning-experiment-principles.md when experiment details affect comparability or reproducibility. Fit Use this skill only when the request has both: Explicit exploration authorization such as candidate-only work, isolated branch or worktree, sweep, several variants, or exploratory ranking. A durable currentresearch context such as a branch, commit, checkpoint, run record, or already-trained local model state. Keep narrow code-only requests on explore-code. Keep narrow run-only requests on explore-run. Keep passive repository analysis on analyze-project. Keep README-first reproduction on ai-research-reproduction. Research Rhythm Use a two-loop rhythm: Outer loop: understand the repository, freeze task/dataset/evaluation/budget, preserve user ideas, map sources, gate ideas, and decide whether the next experiment is worth running. Inner loop: make one bounded candidate change or run, smoke-check it, collect evidence, rank it against the current anchor, and either stop or return to the outer loop with the new evidence. This rhythm is a guide, not a rigid autonomous loop. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-research-explore