statistical-metric-analysis

星标0

分支0

更新时间2026年7月10日 19:24

Orchestration for bibliometrics, topological network analysis, and hypothesis scoring.

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

在 Manus 中运行

来源

docxology

docxology/template_literature_meta_analysis

打开 GitHub 仓库查看创作者相关仓库

下载

在 Manus 中运行

Instructions

You are interacting with the src/analysis/ module of the Meta-Analysis project. This layer transforms raw literature and structured knowledge graph edges into quantifiable metrics.

Agentic Interface (MCP Strategy)

Deterministic Operations: Always fix random_seed=42 across any clustering, layout algorithms (e.g. UMAP/t-SNE if added), or network metrics (Louvain community detection) to ensure reproducible builds.
Data Abstractions: Rely on Pandas and NetworkX for scalable computation. Optimize grouping and aggregation passes to use vectorized functions.
No-Mock Constraint: Ensure tests use local fixtures (like sample graphs or dummy DataFrames) rather than mocking out computation passes. Mocking statistical layers masks data-typing and floating-point errors.

同仓库更多 Skills

同仓库

llm-extraction-kg-construction

docxology/template_literature_meta_analysis

Core inference logic, prompt construction, and RDF/Nanopublication serialization.

2026-07-110

meta-analysis-source-api

docxology/template_literature_meta_analysis

Core orchestration guidelines and MCP interactions for the src/ library.

2026-07-110

template-literature-meta-analysis

docxology/template_literature_meta_analysis

Literature meta-analysis exemplar — multi-engine retrieval, dedup, full-text, bibliometrics, embeddings, optional knowledge graph. Default term: modafinil.

2026-07-100

systematic-literature-retrieval

docxology/template_literature_meta_analysis

Operations for federated API querying across OpenAlex, Semantic Scholar, and arXiv.

2026-07-100

manuscript-templating-injection

docxology/template_literature_meta_analysis

Tooling for injecting JSON/CSV statistics into Markdown evaluation templates.

2026-07-100

data-visualization-library

docxology/template_literature_meta_analysis

Orchestrates matplotlib and seaborn pipelines for rendering figures.

2026-07-100

name	Statistical & Metric Analysis
description	Orchestration for bibliometrics, topological network analysis, and hypothesis scoring.

Instructions

You are interacting with the src/analysis/ module of the Meta-Analysis project. This layer transforms raw literature and structured knowledge graph edges into quantifiable metrics.

Agentic Interface (MCP Strategy)

Deterministic Operations: Always fix random_seed=42 across any clustering, layout algorithms (e.g. UMAP/t-SNE if added), or network metrics (Louvain community detection) to ensure reproducible builds.
Data Abstractions: Rely on Pandas and NetworkX for scalable computation. Optimize grouping and aggregation passes to use vectorized functions.
No-Mock Constraint: Ensure tests use local fixtures (like sample graphs or dummy DataFrames) rather than mocking out computation passes. Mocking statistical layers masks data-typing and floating-point errors.