Communitygithub.com

paperzilla

에이전트와 채팅하며 Paperzilla의 프로젝트, 추천, 표준 논문에 대해 논의하세요. 사용자가 최근 프로젝트 추천, 표준 논문 세부 정보, 마크다운 기반 요약, 추천 피드백, 피드 내보내기 또는 Atom 피드 URL을 요청할 때 사용합니다.

paperzilla란 무엇인가요?

Paperzilla 스킬은 AI 에이전트가 Paperzilla 플랫폼과 직접 상호작용할 수 있도록 설계되었습니다. 이 스킬을 통해 에이전트는 프로젝트 목록 조회, 추천 사항 확인, 표준 논문을 마크다운으로 가져와 요약하는 등의 작업을 수행할 수 있습니다. 사용자에게 최신 프로젝트 추천, 특정 논문의 중요성 설명, 연구 관련성 분석, 피드 내보내기 등의 기능을 제공합니다. 이 스킬은 CLI(`pz`)를 기반으로 하며, macOS, Windows(Scoop), Linux에서 설치 가능하고 Go 소스에서 직접 빌드할 수도 있습니다. 인증은 `pz login` 명령어로 처리하며, 모든 데이터 조회와 피드백은 `pz` 명령어를 통해 JSON, 마크다운, Atom 형식으로 출력할 수 있습니다. 워크플로나 외부 전달 통합을 강제하지 않으며, 에이전트가 Paperzilla 데이터를 자유롭게 활용할 수 있도록 돕는 핵심 스킬입니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/paperzilla
원본 보기→모든 스킬 둘러보기

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

문서

paperzilla은(는) 무엇을 하나요?

이 스킬은 에이전트와 채팅하며 Paperzilla의 프로젝트, 추천, 표준 논문에 대해 논의할 때 사용합니다.

질문할 수 있는 내용

  • "프로젝트 X의 최신 추천을 알려줘."
  • "추천 Y를 열고 그 중요성을 설명해줘."
  • "표준 논문 Z를 마크다운으로 가져와서 요약해줘."
  • "이 논문이 내 연구와 어떻게 관련되는지 알려줘."
  • "프로젝트 X의 피드를 보여줘."
  • "추천에 피드백을 남겨줘."
  • "이 논문, 추천 또는 피드를 JSON으로 내보내줘."

이것은 핵심 Paperzilla 스킬입니다. 에이전트가 Paperzilla 데이터에 직접 접근할 수 있게 하지만, 워크플로나 외부 전달 통합을 강제하지는 않습니다.

접근 방법

이 저장소의 대부분의 현재 프로필은 pz CLI를 사용합니다.

현재 프로필에 에이전트별 추가 지침이 포함되어 있다면 그 지침도 따르세요.

설치

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

공식 Linux 설치 가이드를 참조하세요:

소스에서 빌드 (Go 1.23+)

소스 빌드는 CLI 저장소를 참조하세요:

업데이트

CLI가 최신 버전인지 확인하고 설치별 업그레이드 방법을 확인하세요:

pz update

감지가 모호한 경우 명시적으로 재정의하세요:

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

지원되는 값은 auto, homebrew, scoop, release, source입니다.

인증

pz login

CLI 참조

현재 프로필이 pz를 사용하는 경우, 다음은 핵심 명령어입니다.

프로젝트 목록

pz project list

프로젝트 하나 보기

pz project <project-id>

프로젝트 피드 탐색

pz feed <project-id>

유용한 플래그:

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

예시:

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

피드 출력에는 기존 추천 피드백 표시가 포함될 수 있습니다:

  • [↑] 추천
  • [↓] 비추천
  • [★] 즐겨찾기

표준 논문 읽기

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

프로젝트 중 하나의 추천 열기

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

추천 피드백 남기기

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>

출력 및 자동화

  • 기계 파싱에는 --json을 선호하세요.
  • pz paper --markdown은 이미 준비된 경우에만 마크다운을 반환합니다.
  • pz rec --markdown은 마크다운 생성을 대기열에 추가하고 준비 중일 때 친절한 재시도 메시지를 출력합니다.
  • --atom은 피드 리더용 개인 피드 URL을 반환합니다.

설정

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

참고 자료

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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Extract frames or short clips from videos using ffmpeg.

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Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

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openclaw/openclaw

Use when testing, fixing, or extending the OpenClaw Control UI GUI with Vitest + Playwright end-to-end checks, mocked Gateway WebSocket flows, mocked dashboard runs, screenshots/videos, or agent-verifiable browser proof.

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obra/superpowers

Obra superpowers agent skills.

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See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...

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affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

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