ai-research-explore

par lllllllama

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,...

npx skills add https://github.com/lllllllama/rigorpilot-skills --skill ai-research-explore
Télécharger ZIPGitHub

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 ../../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 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 ../../references/explore-variant-spec.md for run-level variant matrix details.
  • Load ../../references/research-rigor-principles.md before making novelty, contribution, SOTA, or comparability statements.
  • Load ../../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.

Plus de skills de lllllllama

analyze-project
lllllllama
Compétence en lecture seule Rigor Analyze / Rigor Audit pour les dépôts de recherche en deep learning. À utiliser lorsque l'utilisateur souhaite lire et comprendre un dépôt, inspecter la structure du modèle et les points d'entrée d'entraînement ou d'inférence, examiner les configurations et les points d'insertion, ou signaler des schémas d'implémentation suspects sans modifier le code ni exécuter de tâches lourdes. Ne pas utiliser pour l'exécution active de commandes, le refactoring large, l'adaptation spéculative de code ou la correction automatique de bugs.
developmentcode-reviewresearch
ai-research-reproduction
lllllllama
Orchestrateur en mode reproduction de RigorPilot pour la reproduction de dépôts d'apprentissage profond avec approche README-first. À utiliser lorsque l'utilisateur souhaite un flux de bout en bout minimal et fiable qui lit d'abord le dépôt, sélectionne la plus petite cible d'inférence ou d'évaluation documentée, coordonne la prise en charge, la configuration, l'exécution de confiance, l'entraînement de confiance optionnel, l'analyse de dépôt optionnelle et la résolution optionnelle des lacunes documentaires, applique des règles de correctif conservatrices, enregistre les preuves, les hypothèses, les écarts et les points de décision humaine,...
researchdevelopmentdocument
explore-code
lllllllama
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted...
developmentresearchcode-review
minimal-run-and-audit
lllllllama
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.
developmenttestingcode-review
env-and-assets-bootstrap
lllllllama
Compétence de configuration Rigor pour la reproduction d’un dépôt d’apprentissage profond prioritaire sur README. À utiliser lorsque la tâche consiste spécifiquement à préparer un environnement conda-first conservateur, des hypothèses de chemins de points de contrôle et de jeux de données, des indications d’emplacement de cache et des notes de configuration avant toute exécution sur un dépôt documenté par README. Ne pas utiliser pour l’analyse de dépôt, l’orchestration complète, l’interprétation d’articles, le rapport d’exécution final ou la configuration générique d’environnement non liée à une cible de reproduction spécifique.
developmentdevops
explore-run
lllllllama
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...
researchdevelopmentdata-analysis
safe-debug
lllllllama
Compétence de débogage rigoureux / audit rigoureux pour les travaux de recherche en deep learning. À utiliser lorsque l'utilisateur colle un traceback, une erreur de terminal, une OOM CUDA, un échec de chargement de checkpoint, une incompatibilité de forme, un symptôme de perte NaN ou un échec d'entraînement et souhaite un diagnostic conservateur avant toute correction, avec des correctifs de débogage clairement séparés des contributions de recherche. Ne pas utiliser pour un refactoring large, une adaptation spéculative, un patching exploratoire automatique ou une familiarisation générale avec le dépôt.
developmenttestingcode-review
paper-context-resolver
lllllllama
Assistant contextuel Rigor Paper pour la reproduction de dépôts deep learning à lecture prioritaire du README. Utiliser uniquement lorsque le README et les fichiers du dépôt laissent un écart étroit et critique pour la reproduction, et que la tâche consiste à résoudre un détail spécifique d’un article tel que la division des ensembles de données, le prétraitement, le protocole d’évaluation, le mappage des points de contrôle ou une hypothèse d’exécution à partir des sources primaires de l’article, tout en enregistrant les conflits. Ne pas utiliser pour un résumé général d’article, une analyse de dépôt, une configuration d’environnement, une exécution de commande, une recherche d’article par titre uniquement, ou...
researchdocumentdata-analysis