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hayashishungenn

Ai Paper Pipeline

by hayashishungenn · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install ai-paper-pipeline
Description
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...
README (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.md and push bulky reference text into references/.
  • 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

  1. 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
  2. If the user pasted a large workflow prompt, extract and organize it into:
    • SKILL.md for concise usage instructions
    • references/ for long-form reference content
    • templates/ for starter files like RESTRICTS.example.yaml
  3. Keep only trigger logic, workflow guidance, and file navigation in SKILL.md.
  4. Put long source material, detailed prompts, and heavy policy text in references/.
  5. If the user wants a paper project initialized, create at minimum:
    • MEGA_PROMPT.md
    • RESTRICTS.yaml
    • PROGRESS.md
    • plans/
    • code/, data/, docs/, results/
    • paper/mypaper/main.tex
    • paper/mypaper/sections/
  6. 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.md when you need the concise built-in version of the 25-stage workflow.
  • Read references/full-pipeline-template.md when the user wants the verbose original template or asks to reconstruct/port the full prompt.
  • Read references/project-scaffold.md when the user wants to initialize a concrete paper project directory.
  • Read templates/RESTRICTS.example.yaml when 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.md concise 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.md with 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.

Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install ai-paper-pipeline
  3. After installation, invoke the skill by name or use /ai-paper-pipeline
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Slug ai-paper-pipeline
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Ai Paper Pipeline?

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... It is an AI Agent Skill for Claude Code / OpenClaw, with 97 downloads so far.

How do I install Ai Paper Pipeline?

Run "/install ai-paper-pipeline" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Ai Paper Pipeline free?

Yes, Ai Paper Pipeline is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Ai Paper Pipeline support?

Ai Paper Pipeline is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Ai Paper Pipeline?

It is built and maintained by hayashishungenn (@hayashishungenn); the current version is v1.0.1.

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