paperzilla
Paperzilla enables agents to query academic research projects, retrieve canonical papers as markdown, browse project feeds with filters, and manage recommendation feedback within Paperzilla's platform. Use this skill when users request recent paper recommendations from specific projects, seek detailed explanations of canonical papers, need markdown-formatted summaries, want to export feeds as JSON or Atom formats, or wish to provide feedback on recommendations.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/paperzilla && cp -r /tmp/paperzilla/skills/paperzilla ~/.claude/skills/paperzillaSKILL.md
# Paperzilla Use this skill when you want to chat with your agent about projects, recommendations, and canonical papers in Paperzilla. ## What you can ask - "Give me the latest recommendations from project X." - "Open recommendation Y and explain why it matters." - "Fetch canonical paper Z as markdown and summarize it." - "Tell me how this paper is relevant to my research." - "Show me the feed for project X." - "Leave feedback on a recommendation." - "Export this paper, recommendation, or feed as JSON." This is the core Paperzilla skill. It gives your agent direct access to Paperzilla data, but it does not impose a workflow or external delivery integration. ## Access method Most current profiles in this repo use the `pz` CLI. If the current profile ships extra agent-specific instructions, follow those as well. ## Install ### macOS ```bash brew install paperzilla-ai/tap/pz ``` ### Windows (Scoop) ```bash scoop bucket add paperzilla-ai https://github.com/paperzilla-ai/scoop-bucket scoop install pz ``` ### Linux Use the official Linux install guide: - https://docs.paperzilla.ai/guides/cli-getting-started ### Build from source (Go 1.23+) See the CLI repository for source builds: - https://github.com/paperzilla-ai/pz ## Update Check whether your CLI is up to date and get install-specific upgrade steps: ```bash pz update ``` If detection is ambiguous, override it explicitly: ```bash pz update --install-method homebrew pz update --install-method scoop pz update --install-method release pz update --install-method source ``` Supported values are `auto`, `homebrew`, `scoop`, `release`, and `source`. ## Authentication ```bash pz login ``` ## CLI reference If the current profile uses `pz`, these are the core commands. ### List projects ```bash pz project list ``` ### Show one project ```bash pz project <project-id> ``` ### Browse project feed ```bash pz feed <project-id> ``` Useful flags: - `--must-read` - `--since YYYY-MM-DD` - `--limit N` - `--json` - `--atom` Examples: ```bash pz feed <project-id> --must-read --since 2026-03-01 --limit 5 pz feed <project-id> --json pz feed <project-id> --atom ``` Feed output can include existing recommendation feedback markers: - `[↑]` upvote - `[↓]` downvote - `[★]` star ### Read a canonical paper ```bash pz paper <paper-id> pz paper <paper-id> --json pz paper <paper-id> --markdown pz paper <paper-id> --project <project-id> ``` ### Open a recommendation from one of your projects ```bash pz rec <project-paper-id> pz rec <project-paper-id> --json pz rec <project-paper-id> --markdown ``` ### Leave recommendation feedback ```bash 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> ``` ## Output and automation - Prefer `--json` for machine parsing. - `pz paper --markdown` only returns markdown when it is already prepared. - `pz rec --markdown` can queue markdown generation and prints a friendly retry message while it is still being prepared. - `--atom` returns a personal feed URL for feed readers. ## Configuration ```bash export PZ_API_URL="https://paperzilla.ai" ``` ## References - Docs: https://docs.paperzilla.ai/guides/cli - Quickstart: https://docs.paperzilla.ai/guides/cli-getting-started - Repo: https://github.com/paperzilla-ai/pz
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`.
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.
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 "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
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.
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. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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.
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.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.