Agent skill · research science · aiskillstore
explore-run
Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.
Why this skill is useful
Provides specific guidelines and scripts for conducting bounded exploratory runs in deep learning research that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
explore-run Use this as the Rigor Improve / Rigor Explore run leaf skill. The installed slug remains explore-run for compatibility. Use the shared operating principles in ../../references/agent-operating-principles.md; this skill should guide candidate run planning while preserving model judgment about the active repo. When to apply When the researcher explicitly authorizes exploratory runs. When the task is a small-subset validation, short-cycle training probe, batch sweep, idle-GPU search, or quick transfer-learning trial. When the output should rank candidate runs rather than certify trusted success. When not to apply When the user wants trusted training execution or conservative verification. When there is no explicit exploratory authorization. When the task is repository setup, intake, or debugging. Clear boundaries This skill owns exploratory execution planning and summary only. Use ai-research-explore instead when the task spans both currentresearch coordination and exploratory code changes. It may hand off actual command execution to minimal-run-and-audit or run-train. It should keep experiment state isolated from the trusted baseline. It should prefer small-subset and short-cycle checks before heavier exploratory runs. It should label run results as bounded evidence and explain when a comparison is not directly fair. Ranking Semantics Pre-execution candidate selection uses three factors: cost, successrate, and expectedgain. Default weights should stay conservative unless the researcher explicitly provides selectionweights. Budget pruning still applies after scoring through maxvariants and maxshortcycleruns. If runs are executed later, downstream ranking should switch to real execution evidence, not stay purely heuristic. Variant Spec Hints Use variantaxes to define the candidate dimension grid. Use subsetsizes and shortrunsteps to express exploratory run scale. Use selectionweights to rebalance cost, successrate, and expectedgain. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/explore-run