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Ai Paper Pipeline
作者
hayashishungenn
· GitHub ↗
· v1.0.1
· MIT-0
97
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当前安装
2
版本数
在 OpenClaw 中安装
/install ai-paper-pipeline
功能描述
Build or improve a top-tier AI conference paper workflow for NeurIPS, ICML, ICLR, and similar venues. Use when the user asks to generate a paper pipeline, or...
使用说明 (SKILL.md)
AI Paper Pipeline
Turn a rough paper idea or a long "mega prompt" into a reusable, reality-grounded paper project scaffold.
What this skill should do
- Normalize a user's long paper-workflow prompt into a maintainable skill/project structure.
- Keep the main workflow concise in
SKILL.mdand push bulky reference text intoreferences/. - Preserve academic-integrity constraints: no fabricated experiments, no fake citations, no unsupported claims.
- Prefer creating reusable project scaffolding over dumping one giant prompt blob.
Default workflow
- Identify whether the user wants one of these:
- skill cleanup / packaging for the paper workflow itself
- project initialization for a specific paper
- template ingestion from a pasted mega prompt
- If the user pasted a large workflow prompt, extract and organize it into:
SKILL.mdfor concise usage instructionsreferences/for long-form reference contenttemplates/for starter files likeRESTRICTS.example.yaml
- Keep only trigger logic, workflow guidance, and file navigation in
SKILL.md. - Put long source material, detailed prompts, and heavy policy text in
references/. - If the user wants a paper project initialized, create at minimum:
MEGA_PROMPT.mdRESTRICTS.yamlPROGRESS.mdplans/code/,data/,docs/,results/paper/mypaper/main.texpaper/mypaper/sections/
- After edits, package or commit changes if appropriate.
File layout for this skill
ai-paper-pipeline/
├── SKILL.md
├── MEGA_PROMPT.md
├── references/
│ ├── full-pipeline-template.md
│ └── project-scaffold.md
└── templates/
└── RESTRICTS.example.yaml
When to read extra files
- Read
MEGA_PROMPT.mdwhen you need the concise built-in version of the 25-stage workflow. - Read
references/full-pipeline-template.mdwhen the user wants the verbose original template or asks to reconstruct/port the full prompt. - Read
references/project-scaffold.mdwhen the user wants to initialize a concrete paper project directory. - Read
templates/RESTRICTS.example.yamlwhen initializing a new paper project or drafting a restrictions file.
Working rules
- Treat the paper as a real research artifact, not a vibe-writing exercise.
- Never claim experiments, datasets, ablations, or statistical tests that are not actually present.
- Never keep huge duplicated prompt text in multiple files.
- Prefer editable project artifacts over giant single-message outputs.
- Keep the paper workflow cyclical: literature → design → run → analyze → draft → review → revise.
Good outputs
A. User says: "整理成一个 Skill"
Do this:
- Clean up the current skill folder.
- Convert ad-hoc text into proper
SKILL.md+references/+templates/. - Keep
SKILL.mdconcise and reusable.
B. User says: "按这个模板起一个论文项目"
Do this:
- Create a new
\x3Cproject>-paper/scaffold. - Copy in starter files.
- Replace placeholders with project-specific metadata where provided.
C. User says: "把这份 mega prompt 落库"
Do this:
- Save the raw template in
references/or project root. - Avoid bloating
SKILL.mdwith the full raw text.
Final step
After modifying this skill or creating paper-project files in the workspace, commit the changes with a clear git message.
安全使用建议
This skill is a heavy-duty instruction-only workflow for building paper projects and running experiments. Before installing or invoking it: 1) Confirm which credentials and services you allow it to access — the files reference OPENAI_API_KEY, Kaggle and other tokens but the registry does not declare them. 2) Only run it in an isolated/dev environment if you expect it to run code, detect hardware, or install packages — it can run experiments that consume compute and access local paths. 3) If you plan to let it use model APIs, supply ephemeral keys or restrict scopes; never expose long-lived admin keys. 4) Review the MEGA_PROMPT.md and RESTRICTS templates line-by-line for any mentions of private paths, network endpoints, or data sources you don't want the agent to touch. 5) Require explicit human approval for any step that executes experiments, installs packages, or commits/pushes to remote repos. If the author can clarify and update registry metadata to list the required env vars and permissions, the coherence concerns would be resolved.
功能分析
Type: OpenClaw Skill
Name: ai-paper-pipeline
Version: 1.0.1
The skill bundle defines an extensive 25-stage pipeline for automating academic research, which involves high-risk operations such as generating and executing arbitrary Python code (Stage 12), performing multi-source network searches, and managing sensitive environment variables (e.g., OPENAI_API_KEY, KAGGLE_API_TOKEN). While the instructions in SKILL.md and MEGA_PROMPT.md emphasize academic integrity and prohibit data fabrication, the requirement for the agent to run code in a sandbox and interact with external APIs (OpenAlex, Semantic Scholar) creates a significant attack surface. These capabilities are plausibly necessary for the stated purpose but carry inherent risks of unintended execution or data exposure.
能力评估
Purpose & Capability
The skill claims to scaffold/packaging paper projects and initialize experiment workflows (reasonable). However the included instructions and MEGA_PROMPT explicitly expect networked model APIs, literature APIs, Kaggle/Tavily tokens, and access to local project source paths and hardware detection. The registry shows no required env vars, binaries, or config paths — that is inconsistent (the skill likely needs credentials and runtime access beyond what it declares).
Instruction Scope
SKILL.md and MEGA_PROMPT instruct the agent to: read and create many project files, detect local GPU/CPU (hardware detection), call external literature APIs (OpenAlex, Semantic Scholar, arXiv, Google Scholar), call large-model endpoints (OPENAI_API_BASE/KEY, OPENAI_MODEL_NAME), use Kaggle/Tavily tokens, run sandboxed experiments, install libraries, and commit changes. Those are broad actions touching local filesystem, environment variables, network endpoints, and code execution — all legitimate for a research pipeline but they are not limited or reflected in the declared requirements and therefore grant wide runtime scope.
Install Mechanism
No install spec and no packaged code — this is instruction-only, so the skill itself does not download or execute third-party installers. That reduces supply-chain risk. Note: the instructions do tell the agent it may install Python libraries in the environment at runtime (venv/docker), which is normal for experiments but happens outside the skill packaging.
Credentials
Registry metadata declares no required environment variables or credentials, but MEGA_PROMPT lists several sensitive env vars (OPENAI_API_BASE, OPENAI_API_KEY, OPENAI_MODEL_NAME, KAGGLE_API_TOKEN, TAVILY_API_KEY) and expects access to local project paths and possibly system-level hardware info. This mismatch is disproportionate and could result in the agent attempting to read secrets or ask for credentials not declared up-front.
Persistence & Privilege
The skill is not always-enabled and does not request elevated persistent privileges in metadata. It instructs the agent to create and commit files in the workspace (normal for a scaffolding skill). No evidence it attempts to modify other skills or global agent settings.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install ai-paper-pipeline - 安装完成后,直接呼叫该 Skill 的名称或使用
/ai-paper-pipeline触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
- Major refactor for clarity and maintainability: skill is now modular with SKILL.md, references, and templates.
- SKILL.md rewritten for concise workflow instructions and file structure guidance; long-form prompts moved to references/.
- Added new reference and template files: full-pipeline-template.md, project-scaffold.md, MEGA_PROMPT.project.md, PROGRESS.template.md, and RESTRICTS.example.yaml.
- Clearer directions for project initialization, skill packaging, and how/when to use auxiliary files.
- Explicit constraints for academic integrity and file organization.
v1.0.0
ai-paper-pipeline v1.0.0
- Initial release of an end-to-end automated pipeline for generating top-tier AI conference papers (NeurIPS/ICML/ICLR level)
- Implements 25-stage, 9-stage-group framework covering research, experiments, writing, and optimization
- Automatic trigger on keywords or relevant file uploads
- Enforces high standards: real code, minimum experiment rounds, citation quality, LaTeX template compliance
- Outputs all research artifacts, including code, data, plans, and full-length LaTeX paper
- Integrates with supporting skills for web search, code execution, and document saving
元数据
常见问题
Ai Paper Pipeline 是什么?
Build or improve a top-tier AI conference paper workflow for NeurIPS, ICML, ICLR, and similar venues. Use when the user asks to generate a paper pipeline, or... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 97 次。
如何安装 Ai Paper Pipeline?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-paper-pipeline」即可一键安装,无需额外配置。
Ai Paper Pipeline 是免费的吗?
是的,Ai Paper Pipeline 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Ai Paper Pipeline 支持哪些平台?
Ai Paper Pipeline 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Ai Paper Pipeline?
由 hayashishungenn(@hayashishungenn)开发并维护,当前版本 v1.0.1。
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