Claude Skill

WILLOSCAR/research-units-pipeline-skills

Python framework for research pipelines as Claude Skills with declared I/O, acceptance criteria, and guardrails. Uses evidence-first methodology to prevent hollow writing through structured artifacts.

Overview

Stars97
Forks10
LanguagePython
Last pushed2026-01-25
Last synced2026-07-01
View on GitHub

Repository

OwnerWILLOSCAR
Repositoryresearch-units-pipeline-skills
Full nameWILLOSCAR/research-units-pipeline-skills
Repo ID1,129,831,537

Install this Skill

git clone https://github.com/WILLOSCAR/research-units-pipeline-skills.git

Registry

Typecodex_skill
Quality score80/100
Verificationreadme_parsed
Last verified2026-07-01
Platforms
ClaudeCodex
Capabilities
pdfmemorysearchworkflowclaudeclaude-codecodexgptpipelineresearch
Detected files
README.mdREADME.zh-CN.mddocspyproject.tomltests

Summary

A Python framework for structuring research pipelines as semantic execution units (Claude Skills), where each skill declares inputs/outputs, acceptance criteria, and guardrails. It employs an evidence-first methodology to prevent hollow writing through structured intermediate artifacts.

Chinese description

研究管道作为语义执行单元:每个Claude Skill声明输入/输出、验收标准和防护机制。证据优先方法论通过结构化中间产物,防止空洞写作。

Key features

  • Semantic execution units (Claude Skills)
  • Declared inputs/outputs and acceptance criteria
  • Guardrails for pipeline safety
  • Evidence-first methodology
  • Structured intermediate artifacts
  • Prevents hollow writing in research

Use cases

  • Research paper writing pipelines
  • Structured research project management
  • Academic workflow automation
  • Evidence-based documentation generation
  • Collaborative research tooling
  • Reproducible research processes

README excerpt

# research-units-pipeline-skills > Languages: **English** | [简体中文](README.zh-CN.md) An Auto Research Design System for agent-assisted research work. This repository combines **semantic research skills** with a **file-first harness**. It is meant for people using coding agents such as Codex to run research workflows without losing the intermediate evidence. A run becomes a durable workspace: planned units, intermediate artifacts, checkpoints, audits, and improvement records. The short version: ```text intent -> workflow -> workspace -> unit -> skill -> artifact -> audit -> improvement ``` It is not a generic workflow engine, a prompt collection, or a claim that research can be fully automated. The repo is narrower and more practical: it keeps research work inspectable, resumable, and improvable while the model handles the semantic reading and writing. ## What It Produces Use this repo when the output matters enough that you want files, checkpoints, and reviewable evidence rather than a one-off chat answer. | Goal | Path to use | Main deliverable | |---|---|---| | Evidence-first literature survey | `arxiv-survey` | `output/DRAFT.md` | | Survey with LaTeX/PDF delivery | `arxiv-survey-latex` | `output/DRAFT.md`, `latex/main.pdf` | | Course paper or end-of-term report from a topic | Use `arxiv-survey` for Markdown or `arxiv-survey-latex` for PDF; this is a use-case overlay, not a new workflow | report draft, optional PDF | | Fast topic briefing and reading path | `research-brief` | `output/SNAPSHOT.md` | | Single-paper critique or referee-style review | `paper-review` | `output/REVIEW.md` | | Protocol-driven evidence synthesis | `evidence-review` | `output/SYNTHESIS.md` | | Literature-grounded research ideas | `idea-brainstorm` | `output/REPORT.md`, `output/REPORT.j

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Data from GitHub. Synced on 2026-07-01