explore-run
bylllllllama· 44 skills
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.
How to install explore-run?
lllllllama/rigorpilot-skills/explore-run
$npx -y skills add lllllllama/rigorpilot-skills --skill explore-runInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/lllllllama/rigorpilot-skills" --skill "lllllllama/rigorpilot-skills/explore-run"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/lllllllama/rigorpilot-skills" that are relevant to the current task. Run `npx skills add "https://github.com/lllllllama/rigorpilot-skills"` and select the relevant skills, then follow their instructions.
SKILL.md
| 1 | # explore-run |
| 2 | |
| 3 | Use this as the Rigor Improve / Rigor Explore run leaf skill. The installed slug |
| 4 | remains `explore-run` for compatibility. |
| 5 | |
| 6 | Use the shared operating principles in |
| 7 | `../../references/agent-operating-principles.md`; this skill should guide |
| 8 | candidate run planning while preserving model judgment about the active repo. |
| 9 | |
| 10 | ## When to apply |
| 11 | |
| 12 | - When the researcher explicitly authorizes exploratory runs. |
| 13 | - When the task is a small-subset validation, short-cycle training probe, batch sweep, idle-GPU search, or quick transfer-learning trial. |
| 14 | - When the output should rank candidate runs rather than certify trusted success. |
| 15 | |
| 16 | ## When not to apply |
| 17 | |
| 18 | - When the user wants trusted training execution or conservative verification. |
| 19 | - When there is no explicit exploratory authorization. |
| 20 | - When the task is repository setup, intake, or debugging. |
| 21 | |
| 22 | ## Clear boundaries |
| 23 | |
| 24 | - This skill owns exploratory execution planning and summary only. |
| 25 | - Use `ai-research-explore` instead when the task spans both current_research coordination and exploratory code changes. |
| 26 | - It may hand off actual command execution to `minimal-run-and-audit` or `run-train`. |
| 27 | - It should keep experiment state isolated from the trusted baseline. |
| 28 | - It should prefer small-subset and short-cycle checks before heavier exploratory runs. |
| 29 | - It should label run results as bounded evidence and explain when a comparison |
| 30 | is not directly fair. |
| 31 | |
| 32 | ## Ranking Semantics |
| 33 | |
| 34 | - Pre-execution candidate selection uses three factors: `cost`, `success_rate`, and `expected_gain`. |
| 35 | - Default weights should stay conservative unless the researcher explicitly provides `selection_weights`. |
| 36 | - Budget pruning still applies after scoring through `max_variants` and `max_short_cycle_runs`. |
| 37 | - If runs are executed later, downstream ranking should switch to real execution evidence, not stay purely heuristic. |
| 38 | |
| 39 | ## Variant Spec Hints |
| 40 | |
| 41 | - Use `variant_axes` to define the candidate dimension grid. |
| 42 | - Use `subset_sizes` and `short_run_steps` to express exploratory run scale. |
| 43 | - Use `selection_weights` to rebalance `cost`, `success_rate`, and `expected_gain`. |
| 44 | - Use `primary_metric` and `metric_goal` so downstream ranking can order executed candidates consistently. |
| 45 | |
| 46 | ## Output expectations |
| 47 | |
| 48 | - `explore_outputs/CHANGESET.md` |
| 49 | - `explore_outputs/SCIENTIFIC_CHANGELOG.md` |
| 50 | - `explore_outputs/COMPARABILITY_REPORT.md` |
| 51 | - `explore_outputs/TOP_RUNS.md` |
| 52 | - `explore_outputs/status.json` |
| 53 | |
| 54 | ## Notes |
| 55 | |
| 56 | 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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