paper-context-resolver
von lllllllama
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or...
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill paper-context-resolverpaper-context-resolver
Use this as the Rigor Paper Context helper. The installed slug remains
paper-context-resolver for compatibility.
When to apply
- README and repo files leave a reproduction-critical gap.
- The gap concerns dataset version, split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumptions.
- The main skill needs a narrow evidence supplement instead of a full paper summary.
- There is already a concrete reproduction question to answer.
When not to apply
- The README already gives enough reproduction detail.
- The user wants a general paper explanation rather than reproduction support.
- The goal is to override README instructions without documenting the conflict.
- The only available input is a paper title and there is no concrete reproduction gap yet.
Clear boundaries
- This skill is optional.
- This skill is helper-tier and should usually be orchestrator-invoked.
- It supplements README-first reproduction.
- It does not replace the main orchestration flow.
- It does not summarize the whole paper by default.
Input expectations
- target repo metadata
- reproduction-critical question
- existing README or repo evidence
- any already known paper links
Output expectations
- narrowed source list
- reproduction-relevant answer only
- explicit README-paper conflict note when applicable
- clear distinction between direct evidence and inference
Notes
Use references/paper-assisted-reproduction.md.
Mehr Skills von lllllllama
ai-research-explore
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,...
researchdata-analysisapi
analyze-project
lllllllama
Rigor Analyze / Rigor Audit schreibgeschützte Fähigkeit für Deep-Learning-Forschungsrepositorien. Verwenden, wenn der Benutzer ein Repository lesen und verstehen, Modellstruktur und Trainings- oder Inferenz-Einstiegspunkte inspizieren, Konfigurationen und Einfügepunkte überprüfen oder verdächtige Implementierungsmuster kennzeichnen möchte, ohne Code zu ändern oder schwere Jobs auszuführen. Nicht für aktive Befehlsausführung, umfassendes Refactoring, spekulative Code-Anpassung oder automatische Fehlerbehebung verwenden.
developmentcode-reviewresearch
ai-research-reproduction
lllllllama
RigorPilot reproduce-mode orchestrator for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points,...
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
Rigor Setup-Fähigkeit zur Reproduktion eines README-zuerst Deep-Learning-Repositorys. Verwenden, wenn die Aufgabe spezifisch darin besteht, eine konservative conda-erste Umgebung, Checkpoint- und Dataset-Pfadannahmen, Cache-Speicherort-Hinweise und Setup-Notizen vor einem Durchlauf in einem README-dokumentierten Repository vorzubereiten. Nicht verwenden für Repository-Scans, vollständige Orchestrierung, Papierinterpretation, abschließende Durchlaufberichte oder generische Umgebungseinrichtung, die nicht an ein spezifisches Reproduktionsziel gebunden ist.
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
Rigor Debug / Rigor Audit-Fähigkeit für Deep-Learning-Forschung. Verwenden, wenn der Benutzer einen Traceback, Terminalfehler, CUDA OOM, Checkpoint-Ladefehler, Shape-Mismatch, NaN-Verlustsymptom oder Trainingsfehler einfügt und eine konservative Diagnose vor jeglichem Patchen wünscht, wobei Debug-Fixes klar von Forschungsbeiträgen getrennt sind. Nicht verwenden für breites Refactoring, spekulative Anpassung, automatisches exploratives Patchen oder allgemeine Repository-Einarbeitung.
developmenttestingcode-review