paper-context-resolver
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 replacing README guidance by default.
Works with
--- name: paper-context-resolver description: 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 replacing README guidance by default. license: MIT --- # paper-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`.
More Writing & Documentation skills
repo-intake-and-plan
lllllllama/rigorpilot-skills
Rigor Intake helper for README-first deep learning repo reproduction. Use when the task is specifically to scan a repository, read the README and common project files, extract documented commands, classify inference, evaluation, and training candidates, and return the smallest trustworthy reproduction plan to the main orchestrator. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
minimal-run-and-audit
lllllllama/rigorpilot-skills
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.
env-and-assets-bootstrap
lllllllama/rigorpilot-skills
Rigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
