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Paperzilla

與你的代理討論專案、推薦和權威論文。當使用者詢問近期專案推薦、權威論文細節、基於 Markdown 的摘要、推薦反饋、摘要匯出或 Atom feed 網址時使用。

Paperzilla 是什麼？

Paperzilla 技能讓 AI 代理能直接透過 `pz` CLI 與 Paperzilla 平台互動，使代理能夠檢索專案、權威論文和個人化推薦。它支援跨 macOS、Windows 和 Linux 的安裝，並提供統一的命令列介面來瀏覽摘要、閱讀論文和提交反饋。關鍵操作包括：使用 `pz feed` 列出摘要並套用 `--must-read` 或 `--since` 等篩選條件、使用 `pz paper` 或 `pz rec` 取得詳細資訊並附帶 `--markdown` 和 `--json` 輸出選項，以及使用 `pz feedback` 表達喜好。此技能強化了代理協助研究探索的能力，讓它能基於最新的專案資料提供脈絡化的回答、摘要和推薦。

相容平台~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→瀏覽所有技能

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說明文件

Paperzilla 是做什麼的？

當你想與你的代理討論專案、推薦和權威論文時，請使用此技能。

你可以詢問的事項

  • "給我專案 X 的最新推薦。"
  • "打開推薦 Y 並解釋它為何重要。"
  • "以 Markdown 格式取得權威論文 Z 並摘要。"
  • "告訴我這篇論文與我的研究有何關聯。"
  • "顯示專案 X 的摘要。"
  • "對推薦留下反饋。"
  • "將此論文、推薦或摘要匯出為 JSON。"

這是 Paperzilla 的核心技能。它讓你的代理能直接存取 Paperzilla 資料，但不會強加工作流程或外部傳送整合。

存取方式

此儲存庫中的多數設定檔都使用 pz CLI。

如果目前的設定檔附帶額外的代理專用指示，請一併遵循。

安裝

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

請使用官方的 Linux 安裝指南：

從原始碼建置 (Go 1.23+)

如需原始碼建置，請參閱 CLI 儲存庫：

更新

檢查你的 CLI 是否為最新版本，並取得安裝專屬的升級步驟：

pz update

如果偵測不明確，請明確覆蓋：

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

支援的值為 auto、homebrew、scoop、release 和 source。

驗證

pz login

CLI 參考

如果目前的設定檔使用 pz，以下是核心命令。

列出專案

pz project list

顯示單一專案

pz project <project-id>

瀏覽專案摘要

pz feed <project-id>

有用的旗標：

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

範例：

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

摘要輸出可包含現有的推薦反饋標記：

  • [↑] 讚
  • [↓] 倒讚
  • [★] 星號

閱讀權威論文

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

從你的專案中開啟一則推薦

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

留下推薦反饋

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>

輸出與自動化

  • 偏好使用 --json 進行機器解析。
  • pz paper --markdown 僅在 Markdown 已準備好時才會回傳。
  • pz rec --markdown 可將 Markdown 生成排入佇列，並在準備中時顯示友善的重試訊息。
  • --atom 會傳回適用於摘要閱讀器的個人摘要網址。

設定

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

參考資料

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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