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paperzilla

Converse com seu agente sobre projetos, recomendações e artigos canônicos no Paperzilla. Use quando usuários pedirem recomendações de projetos recentes, detalhes de artigos canônicos, resumos em markdown, feedback sobre recomendações, exportação de feed ou URLs de feed Atom.

O que é paperzilla?

Esta habilidade permite que um agente de IA (como Claude Code, Cursor ou Codex) interaja diretamente com a plataforma Paperzilla. Ela facilita a consulta de projetos, recomendações e artigos canônicos, fornecendo acesso a dados como feeds de projetos, detalhes de artigos em markdown e feedback sobre recomendações. O agente pode usar comandos da CLI `pz` para listar projetos, visualizar feeds com filtros como `--must-read` e `--since`, obter artigos canônicos em formato markdown ou JSON, e gerenciar feedback (upvote, downvote, estrela). A habilidade suporta automação através de flags como `--json` para saída estruturada e `--atom` para URLs de feed pessoal. É útil para desenvolvedores e pesquisadores que desejam integrar recomendações de artigos e projetos do Paperzilla em seus fluxos de trabalho, seja via linha de comando ou por meio de interações conversacionais com o agente.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/paperzilla
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Abre um novo chat com esta habilidade de agente já pré-carregada.

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Documentação

O que paperzilla faz?

Use esta habilidade quando quiser conversar com seu agente sobre projetos, recomendações e artigos canônicos no Paperzilla.

O que você pode perguntar

  • "Me dê as últimas recomendações do projeto X."
  • "Abra a recomendação Y e explique por que ela é importante."
  • "Busque o artigo canônico Z como markdown e resuma-o."
  • "Diga-me como este artigo é relevante para minha pesquisa."
  • "Mostre-me o feed do projeto X."
  • "Deixe feedback sobre uma recomendação."
  • "Exporte este artigo, recomendação ou feed como JSON."

Esta é a habilidade principal do Paperzilla. Ela dá ao seu agente acesso direto aos dados do Paperzilla, mas não impõe um fluxo de trabalho ou integração de entrega externa.

Método de acesso

A maioria dos perfis atuais neste repositório usa a CLI pz.

Se o perfil atual incluir instruções extras específicas do agente, siga-as também.

Instalação

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

Use o guia oficial de instalação para Linux:

Compilar a partir do código-fonte (Go 1.23+)

Veja o repositório da CLI para compilações a partir do código-fonte:

Atualização

Verifique se sua CLI está atualizada e veja as etapas de atualização específicas do método de instalação:

pz update

Se a detecção for ambígua, substitua-a explicitamente:

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

Os valores suportados são auto, homebrew, scoop, release e source.

Autenticação

pz login

Referência da CLI

Se o perfil atual usar pz, estes são os comandos principais.

Listar projetos

pz project list

Mostrar um projeto

pz project <project-id>

Navegar pelo feed do projeto

pz feed <project-id>

Flags úteis:

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

Exemplos:

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

A saída do feed pode incluir marcadores de feedback de recomendações existentes:

  • [↑] upvote
  • [↓] downvote
  • [★] estrela

Ler um artigo canônico

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

Abrir uma recomendação de um dos seus projetos

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

Deixar feedback sobre uma recomendação

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>

Saída e automação

  • Prefira --json para análise por máquina.
  • pz paper --markdown retorna markdown apenas quando já estiver preparado.
  • pz rec --markdown pode enfileirar a geração de markdown e imprime uma mensagem amigável de nova tentativa enquanto ainda está sendo preparado.
  • --atom retorna uma URL de feed pessoal para leitores de feed.

Configuração

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

Referências

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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