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 audi

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 discovery, global benchmark completeness, novelty proof, or trusted reproduction success. Start from the shared operating principles in ../ai research reproduction/references/agent operating principles.md , then load ../ai research reproduction/references/research rigor principles.md for research claims and ../ai research reproduction/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 current research 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. Stop at explicit blockers, unclear scientific meaning, exhausted budget, missing anchor/evaluation, or a human checkpoint. Workflow 1. Confirm current research and explicit explore lane authorization. 2. Accept either legacy variant spec or higher level research campaign . 3. In campaign mode, freeze the task, dataset, benchmark, evaluation source, SOTA reference, and budget before candidate work. 4. Build only the repo understanding artifacts needed for the current campaign, usually through analyze project . 5. Run bounded, cache first source lookup when source support matters; prefer local curated literature such as Zotero if available, then seed sources, repo local locators, public locators, or optional web lookup. Treat lookup as source resolution, not an open ended literature search. 6. Preserve researcher provided ideas, optionally add a small bounded set of single variable seed ideas, and rank ideas with explicit gates and score breakdowns. 7. Prefer one clear candidate at a time. Use explore code for bounded code adaptation and explore run for short cycle trials or sweeps. 8. Use minimal run and audit or run train only when the exploratory plan requires real execution evidence. 9. Write candidate only outputs to analysis outputs/ , sources/ , and explore outputs/ as appropriate; never present exploratory gains as trusted reproduction success. Include SCIENTIFIC CHANGELOG.md and COMPARABILITY REPORT.md for candidate scientific meaning and comparison boundaries. Ranking and Evidence Before execution, prioritize candidates by expected gain, cost, success likelihood, patch surface, dependency drag, evaluation risk, and rollback ease. After execution, rank by real evidence first: command status, observed metrics, artifacts, changed paths, smoke results, and reproducibility notes. Keep researcher provided evaluation source and sota reference frozen for the campaign; do not claim they are globally complete. If the top ideas are too close or the implementation cannot be decomposed into auditable units, stop for a checkpoint instead of silently choosing. Campaign Inputs research campaign is preferred for Rigor Explore campaigns, but it should stay minimal. The durable core is: current research task family dataset benchmark evaluation source sota reference compute budget Use candidate ideas , variant spec , research lookup , idea policy , idea generation , source constraints , feasibility policy , baseline gate , and execution policy as optional guidance, not as fields the agent must fill for every campaign. See references/research campaign spec.md for the advanced schema and artifact expectations. Reference Loading Load references/ai research explore policy.md for lane safety and candidate semantics. Load references/research campaign spec.md only when a campaign file is present or the user asks for Rigor Explore campaign governance. Load ../ai research reproduction/references/explore variant spec.md for run level variant matrix details. Load ../ai research reproduction/references/research thinking loop.md before proposing or ranking candidate changes; it is the required greedy observe ground design compare cycle. Load ../ai research reproduction/references/research rigor principles.md before making novelty, contribution, SOTA, or comparability statements. Consult ~/.rigorpilot/PERSONAL RIGOR.md if present, under ../ai research reproduction/references/continuous learning policy.md (advisory only; core wins). Load ../ai research reproduction/references/deep learning experiment principles.md when training, evaluation, baseline, ablation, metric, checkpoint, or dataset details matter. Use scripts/orchestrate explore.py and scripts/write outputs.py for the existing deterministic artifact workflow.

ai-research-explore