paper-context-resolver 是做什么的？
用于README优先的深度学习仓库复现的Rigor论文上下文助手。仅在README和仓库文件留下狭窄的复现关键缺口，且任务是从原始论文源解析特定论文细节（如数据集划分、预处理、评估协议、检查点映射或运行时假设）并记录冲突时使用。不得用于通用论文摘要、仓库扫描、环境设置、命令执行、仅标题论文查找或默认替代README指导。
用于README优先的深度学习仓库复现的Rigor论文上下文助手。仅在README和仓库文件留下狭窄的复现关键缺口，且任务是从原始论文源解析特定论文细节（如数据集划分、预处理、评估协议、检查点映射或运行时假设）并记录冲突时使用。不得用于通用论文摘要、仓库扫描、环境设置、命令执行、仅标题论文查找或默认替代README指导。
`llllllama/paper-context-resolver` 是一个严格限定的AI技能，专为“README优先”的深度学习仓库复现场景设计。其核心价值在于精确填补README和仓库文件未能覆盖的、影响复现关键步骤的微小信息缺口。当开发者需要从原始论文中解析特定技术细节（如数据集划分、预处理流程、评估协议、检查点映射、运行时假设）并记录与现有实现的冲突时，该技能提供结构化指引。它强调使用场景的狭窄性：仅在默认依赖README会导致失败，且问题明确指向论文特定条目时启用；明确禁止用于通用论文摘要、仓库扫描、环境配置、执行命令或替换README指导。该技能通过强制记录冲突来提升复现的可追溯性和严谨性，适用于所有支持Markdown技能文件的AI代理平台（如Claude Code、Cursor、Codex）。
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill/tree/main/skills/paper-context-resolverInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
用于README优先的深度学习仓库复现的Rigor论文上下文助手。仅在README和仓库文件留下狭窄的复现关键缺口，且任务是从原始论文源解析特定论文细节（如数据集划分、预处理、评估协议、检查点映射或运行时假设）并记录冲突时使用。不得用于通用论文摘要、仓库扫描、环境设置、命令执行、仅标题论文查找或默认替代README指导。
This repo contains 4 individual skills — each has its own dedicated page.
Main orchestrator for README-first AI repo reproduction. Use when the user wants an end-to-end, minimal-trustworthy reproduction 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, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, or broad research assistance outside repository-grounded reproduction.
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.
RigorPilot trusted execution and reporting 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.
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.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.