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Paperzilla

与您的智能体讨论项目、推荐及经典论文。当用户询问近期项目推荐、经典论文详情、基于Markdown的总结、推荐反馈、订阅源导出或Atom订阅源URL时使用。

Paperzilla 是什么？

Paperzilla技能使AI智能体能够直接与Paperzilla平台交互，主要用于项目管理、论文推荐和经典论文的查询。该技能支持多种交互方式，包括获取最新推荐、打开推荐并解释其重要性、以Markdown格式获取经典论文并生成摘要、探讨论文与研究的相关性、查看项目订阅源、提供推荐反馈以及以JSON格式导出论文、推荐或订阅源。该技能适用于Claude Code、Cursor、Codex等AI代理工具，通过命令行接口（CLI）实现访问。用户需安装`pz` CLI工具（支持macOS、Windows、Linux，或从源代码构建），并通过`pz login`进行身份验证。核心命令包括项目列表、项目展示、订阅源浏览（支持`--must-read`、`--since`、`--limit`、`--json`、`--atom`等标志）、经典论文阅读、推荐打开以及反馈提交。技能设计为轻量级，不强制工作流或外部交付集成，并可通过环境变量`PZ_API_URL`配置API地址。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/paperzilla
查看原文→浏览所有技能

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文档

Paperzilla 是做什么的？

当你想与你的智能体讨论项目、推荐和经典论文时，使用此技能。

你可以询问的内容

  • “给我项目X的最新推荐。”
  • “打开推荐Y并解释其重要性。”
  • “以Markdown格式获取经典论文Z并总结。”
  • “告诉我这篇论文与我的研究有何关联。”
  • “显示项目X的订阅源。”
  • “对推荐提供反馈。”
  • “以JSON格式导出这篇论文、推荐或订阅源。”

这是Paperzilla的核心技能。它让你的智能体直接访问Paperzilla数据，但不强制工作流或外部交付集成。

访问方式

本仓库中的大多数当前配置文件都使用pz CLI。

如果当前配置文件附带额外的智能体特定指令，也请遵循这些指令。

安装

macOS

brew install paperzilla-ai/tap/pz

Windows (Scoop)

scoop bucket add paperzilla-ai https://github.com/paperzilla-ai/scoop-bucket
scoop install pz

Linux

使用官方的Linux安装指南：

从源代码构建（Go 1.23+）\n有关源代码构建，请参阅CLI仓库：

更新

检查您的CLI是否是最新版本，并获取特定安装方式的升级步骤：

pz update

如果检测不明确，请显式覆盖：

pz update --install-method homebrew
pz update --install-method scoop
pz update --install-method release
pz update --install-method source

支持的值有 auto、homebrew、scoop、release 和 source。

身份验证

pz login

CLI参考

如果当前配置文件使用pz，以下是核心命令。

列出项目

pz project list

显示单个项目

pz project <project-id>

浏览项目订阅源

pz feed <project-id>

有用的标志：

  • --must-read
  • --since YYYY-MM-DD
  • --limit N
  • --json
  • --atom

示例：

pz feed <project-id> --must-read --since 2026-03-01 --limit 5
pz feed <project-id> --json
pz feed <project-id> --atom

订阅源输出可以包含现有的推荐反馈标记：

  • [↑] 点赞
  • [↓] 点踩
  • [★] 收藏

阅读经典论文

pz paper <paper-id>
pz paper <paper-id> --json
pz paper <paper-id> --markdown
pz paper <paper-id> --project <project-id>

打开来自你某个项目的推荐

pz rec <project-paper-id>
pz rec <project-paper-id> --json
pz rec <project-paper-id> --markdown

对推荐提供反馈

pz feedback <project-paper-id> upvote
pz feedback <project-paper-id> star
pz feedback <project-paper-id> downvote --reason not_relevant
pz feedback clear <project-paper-id>

输出与自动化

  • 对于机器解析，优先使用 --json。
  • pz paper --markdown 仅在Markdown已准备好时返回。
  • pz rec --markdown 可以排队生成Markdown，并在准备过程中打印友好的重试消息。
  • --atom 返回一个用于订阅阅读器的个人订阅源URL。

配置

export PZ_API_URL="https://paperzilla.ai"

参考

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

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