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paperzilla

Chatten Sie mit Ihrem Agenten über Projekte, Empfehlungen und kanonische Paper in Paperzilla. Verwenden Sie dies, wenn Benutzer nach aktuellen Projektempfehlungen, Details zu kanonischen Papern, Zusammenfassungen als Markdown, Feedback zu Empfehlungen, Feed-Export oder Atom-Feed-URLs fragen.

Was ist paperzilla?

Paperzilla ist eine Skill, die KI-Agenten direkten Zugriff auf die Daten von Paperzilla gibt, einer Plattform für projektbasierte Paper-Empfehlungen. Der Skill ermöglicht es Benutzern, mit ihrem Agenten zu chatten, um aktuelle Projektempfehlungen abzurufen, kanonische Paper als Markdown zu laden und zu analysieren, Feedback zu Empfehlungen zu geben (Upvote/Downvote/Stern) und Feeds als JSON oder Atom-Feed-URLs zu exportieren. Er basiert auf dem `pz` CLI-Tool, das über Homebrew (macOS), Scoop (Windows) oder als Release/Linux-Quelle installiert werden kann. Die wichtigsten Befehle umfassen `pz feed`, `pz paper`, `pz rec` und `pz feedback`, und der Skill eignet sich sowohl für manuelle Interaktionen in der Konsole als auch für automatisierte Workflows. Er unterstützt Markdown-Ausgabe, JSON-Parsing und das Setzen von Filtern wie `--must-read` und `--since` sowie persönliche Atom-Feed-URLs für Feed-Reader.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/paperzilla
Original anzeigen→Alle Skills durchsuchen

In Ihrer bevorzugten KI fragen

Öffnet einen neuen Chat, in dem dieser Agent-Skill bereits geladen ist.

OpenAIChatGPTClaudeClaudeGeminiGeminiGrokGrokPerplexityPerplexityDeepSeekDeepSeek

Dokumentation

Was macht paperzilla?

Verwenden Sie diesen Skill, wenn Sie mit Ihrem Agenten über Projekte, Empfehlungen und kanonische Paper in Paperzilla chatten möchten.

Was Sie fragen können

  • "Gib mir die neuesten Empfehlungen von Projekt X."
  • "Öffne Empfehlung Y und erkläre, warum sie wichtig ist."
  • "Rufe kanonisches Paper Z als Markdown ab und fasse es zusammen."
  • "Sag mir, wie dieses Paper für meine Forschung relevant ist."
  • "Zeig mir den Feed von Projekt X."
  • "Hinterlasse Feedback zu einer Empfehlung."
  • "Exportiere dieses Paper, diese Empfehlung oder diesen Feed als JSON."

Dies ist der Kern-Skill von Paperzilla. Er gibt Ihrem Agenten direkten Zugriff auf Paperzilla-Daten, erzwingt jedoch keinen Workflow oder eine externe Zustellungsintegration.

Zugriffsmethode

Die meisten aktuellen Profile in diesem Repo verwenden die pz CLI.

Wenn das aktuelle Profil zusätzliche agentenspezifische Anweisungen enthält, befolgen Sie auch diese.

Installieren

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

Verwenden Sie die offizielle Linux-Installationsanleitung:

Aus dem Quellcode erstellen (Go 1.23+)

Siehe das CLI-Repository für Quellcode-Builds:

Aktualisieren

Prüfen Sie, ob Ihre CLI auf dem neuesten Stand ist, und erhalten Sie installationsspezifische Upgrade-Schritte:

pz update

Wenn die Erkennung nicht eindeutig ist, überschreiben Sie sie explizit:

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

Unterstützte Werte sind auto, homebrew, scoop, release und source.

Authentifizierung

pz login

CLI-Referenz

Wenn das aktuelle Profil pz verwendet, sind dies die Kernbefehle.

Projekte auflisten

pz project list

Ein Projekt anzeigen

pz project <project-id>

Projekt-Feed durchsuchen

pz feed <project-id>

Nützliche Flags:

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

Beispiele:

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

Die Feed-Ausgabe kann vorhandene Feedback-Markierungen für Empfehlungen enthalten:

  • [↑] Upvote
  • [↓] Downvote
  • [★] Stern

Ein kanonisches Paper lesen

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

Eine Empfehlung aus einem Ihrer Projekte öffnen

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

Feedback zu einer Empfehlung hinterlassen

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>

Ausgabe und Automatisierung

  • Bevorzugen Sie --json für maschinelles Parsen.
  • pz paper --markdown gibt nur dann Markdown zurück, wenn es bereits vorbereitet ist.
  • pz rec --markdown kann die Markdown-Erzeugung in eine Warteschlange stellen und gibt eine freundliche Wiederholungsmeldung aus, solange sie noch vorbereitet wird.
  • --atom gibt eine persönliche Feed-URL für Feed-Reader zurück.

Konfiguration

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

Referenzen

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.

Verwandte Skills

steipete/video-frames

Extract frames or short clips from videos using ffmpeg.

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steipete/summarize

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

community

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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affaan-m/ECC

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.

community