# 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.

Source: https://skillsagentes.com/skills/lllllllama/rigorpilot-skills/explore-run
Repository: https://github.com/lllllllama/RigorPilot-Skills
Author: lllllllama
License: MIT
Updated: hace 19 días
Context cost: 150 tok installed, 817 tok once triggered, 4k tok with every bundled file
Bundle: 5 files, 15 KB
Permissions requested: none declared

## Install

```bash
npx -y skills add lllllllama/RigorPilot-Skills --skill explore-run --agent claude-code
```

## What it does

- Planifica y clasifica candidatos de ejecuciones exploratorias (subconjuntos pequeños, sweeps, búsqueda en GPU ociosa, transferencia rápida) sin certificar éxito confiable
- Genera resúmenes acotados en `explore_outputs/` con advertencias de comparación justa y sin sobreclamar
- Clasifica candidatos pre-ejecución por `cost`, `success_rate` y `expected_gain`, ajustable con `selection_weights`
- Aplica poda de presupuesto mediante `max_variants` y `max_short_cycle_runs`
- Delega la ejecución real de comandos a `minimal-run-and-audit` o `run-train`, manteniendo el estado aislado del baseline confiable

## Use it when

- El investigador autoriza explícitamente ejecuciones exploratorias
- La tarea es una validación en subconjunto pequeño, prueba de entrenamiento de ciclo corto, sweep por lotes, búsqueda en GPU ociosa o prueba rápida de transfer-learning
- El resultado debe clasificar ejecuciones candidatas en vez de certificar un éxito confiable

## Don't bother when

- El usuario quiere ejecución de entrenamiento confiable o verificación conservadora
- No hay autorización exploratoria explícita
- La tarea es configuración del repositorio, intake o depuración

## What triggers it

- "Autorizo una ejecución exploratoria: prueba este sweep en subconjuntos pequeños"
- "Haz una búsqueda rápida de transfer-learning en la GPU ociosa"
- "Clasifica estos candidatos de entrenamiento de ciclo corto antes de ejecutarlos"

## Before you install

- Requiere autorización explícita del investigador para ejecuciones exploratorias antes de aplicarse.

## Files

- SKILL.md — 3 KB
- agents/openai.yaml — 334 B
- references/execution-policy.md — 674 B
- scripts/plan_variants.py — 10 KB
- scripts/write_outputs.py — 820 B

## SKILL.md

Reproduced verbatim from lllllllama/RigorPilot-Skills under MIT. This section is the upstream document and is in English.

# 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 current_research 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`, `success_rate`, and `expected_gain`.
- Default weights should stay conservative unless the researcher explicitly provides `selection_weights`.
- Budget pruning still applies after scoring through `max_variants` and `max_short_cycle_runs`.
- If runs are executed later, downstream ranking should switch to real execution evidence, not stay purely heuristic.

## Variant Spec Hints

- Use `variant_axes` to define the candidate dimension grid.
- Use `subset_sizes` and `short_run_steps` to express exploratory run scale.
- Use `selection_weights` to rebalance `cost`, `success_rate`, and `expected_gain`.
- Use `primary_metric` and `metric_goal` so downstream ranking can order executed candidates consistently.

## Output expectations

- `explore_outputs/CHANGESET.md`
- `explore_outputs/SCIENTIFIC_CHANGELOG.md`
- `explore_outputs/COMPARABILITY_REPORT.md`
- `explore_outputs/TOP_RUNS.md`
- `explore_outputs/status.json`

## Notes

Use `references/execution-policy.md`, `../../references/explore-variant-spec.md`, `../../references/deep-learning-experiment-principles.md`, `scripts/plan_variants.py`, and `scripts/write_outputs.py`.

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Skills Agentes — https://skillsagentes.com/skills/lllllllama/rigorpilot-skills/explore-run