---
id: daily-ai-paper-reproduction
name: "ai-paper-reproduction"
url: https://skills.yangsir.net/skill/daily-ai-paper-reproduction
author: lllllllama
domain: ai-dev-tools-workflow
tags: ["ai-engineering", "reproducibility", "model-training", "automation"]
install_count: 9100
rating: 4.50 (20 reviews)
github: https://github.com/lllllllama/ai-paper-reproduction-skill
---

# ai-paper-reproduction

> 复现AI论文中的代码仓库，自动分析README、识别训练脚本、执行最小可信运行，输出标准化的复现报告

**Stats**: 9,100 installs · 4.5/5 (20 reviews)

## Before / After 对比

### 论文复现效率

**Before**:

手动阅读论文和代码、理解实验设置、配置参数、运行训练、整理结果，复现一篇论文需要1-2天，且难以保证可重复性

**After**:

自动分析代码结构、识别关键命令、执行最小可信运行、生成结构化报告，复现论文仅需2-4小时，可重复验证

| Metric | Before | After | Change |
|---|---|---|---|
| 复现时间 | 1440分钟 | 180分钟 | -88% |

## Readme

# ai-paper-reproduction

# ai-paper-reproduction

## Use when

- The user wants Codex to reproduce an AI paper repository.

- The target is a code repository with a README, scripts, configs, or documented commands.

- The goal is a minimal trustworthy run, not unlimited experimentation.

- The user needs standardized outputs that another human or model can audit quickly.

- The task spans more than one stage, such as intake plus setup, or setup plus execution plus reporting.

## Do not use when

- The task is a general literature review or paper summary.

- The task is to design a new model, benchmark suite, or training pipeline from scratch.

- The repository is not centered on AI or does not expose a documented reproduction path.

- The user primarily wants a deep code refactor rather than README-first reproduction.

- The user is explicitly asking for only one narrow phase that a sub-skill already covers cleanly.

## Success criteria

- README is treated as the primary source of reproduction intent.

- A minimum trustworthy target is selected and justified.

- Documented inference is preferred over evaluation, and evaluation is preferred over training.

- Any repo edits remain conservative, explicit, and auditable.

- `repro_outputs/` is generated with consistent structure and stable machine-readable fields.

- Final user-facing explanation is short and follows the user's language when practical.

## Interaction and usability policy

- Keep the workflow simple enough for a new user to understand quickly.

- Prefer short, concrete plans over exhaustive research.

- Expose commands, assumptions, blockers, and evidence.

- Avoid turning the skill into an opaque automation layer.

- Preserve a low learning cost for both humans and downstream agents.

## Language policy

- Human-readable Markdown outputs should follow the user's language when it is clear.

- If the user's language is unclear, default to concise English.

- Machine-readable fields, filenames, keys, and enum values stay in stable English.

- Paths, package names, CLI commands, config keys, and code identifiers remain unchanged.

See `references/language-policy.md`.

## Reproduction policy

Core priority order:

- documented inference

- documented evaluation

- documented training startup or partial verification

- full training only when the user explicitly asks later

Rules:

- README-first: use repository files to clarify, not casually override, the README.

- Aim for minimal trustworthy reproduction rather than maximum task coverage.

- Treat smoke tests, startup verification, and early-step checks as valid training evidence when full training is not appropriate.

- Record unresolved gaps rather than fabricating confidence.

## Patch policy

- Prefer no code changes.

- Prefer safer adjustments first:

command-line arguments

- environment variables

- path fixes

- dependency version fixes

- dependency file fixes such as `requirements.txt` or `environment.yml`

- Avoid changing:

model architecture

- core inference semantics

- core training logic

- loss functions

- experiment meaning

- If repository files must change:

create a patch branch first using `repro/YYYY-MM-DD-short-task`

- apply low-risk changes before medium-risk changes

- avoid high-risk changes by default

- commit only verified groups of changes

- keep verified patch commits sparse, usually `0-2`

- use commit messages in the form `repro: <scope> for documented <command>`

See `references/patch-policy.md`.

## Workflow

- Read README and repo signals.

- Call `repo-intake-and-plan` to scan the repository and extract documented commands.

- Select the smallest trustworthy reproduction target.

- Call `env-and-assets-bootstrap` to prepare environment assumptions and asset paths.

- Run a conservative smoke check or documented command with `minimal-run-and-audit`.

- Use `paper-context-resolver` only if README and repo files leave a narrow reproduction-critical gap that blocks the current target.

- Write the standardized outputs.

- Give the user a short final note in the user's language.

## Required outputs

Always target:

```
repro_outputs/
  SUMMARY.md
  COMMANDS.md
  LOG.md
  status.json
  PATCHES.md   # only if patches were applied

```

Use the templates under `assets/` and the field rules in `references/output-spec.md`.

## Reporting policy

- Put the shortest high-value summary in `SUMMARY.md`.

- Put copyable commands in `COMMANDS.md`.

- Put process evidence, assumptions, failures, and decisions in `LOG.md`.

- Put durable machine-readable state in `status.json`.

- Put branch, commit, validation, and README-fidelity impact in `PATCHES.md` when needed.

- Distinguish verified facts from inferred guesses.

## Maintainability notes

- Keep this skill narrow: README-first AI repo reproduction only.

- Push specialized logic into sub-skills or helper scripts.

- Prefer stable templates and simple schemas over ad hoc prose.

- Keep machine-readable outputs backward compatible when possible.

- Add new evidence sources only when they improve auditability without raising learning cost.

Weekly Installs510Repository[lllllllama/ai-p…on-skill](https://github.com/lllllllama/ai-paper-reproduction-skill)GitHub Stars1First SeenTodaySecurity Audits[Gen Agent Trust HubFail](/lllllllama/ai-paper-reproduction-skill/ai-paper-reproduction/security/agent-trust-hub)[SocketWarn](/lllllllama/ai-paper-reproduction-skill/ai-paper-reproduction/security/socket)[SnykPass](/lllllllama/ai-paper-reproduction-skill/ai-paper-reproduction/security/snyk)Installed onopencode510gemini-cli510deepagents510antigravity510github-copilot510codex510

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*Source: https://skills.yangsir.net/skill/daily-ai-paper-reproduction*
*Markdown mirror: https://skills.yangsir.net/api/skill/daily-ai-paper-reproduction/markdown*