What's on the bench.
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. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.
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.
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.
Bids
Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
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.
Autoskill
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.
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.
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.
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. "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.
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.
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.
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`.
Launch Readiness
Assesses pre-launch readiness across every function and produces an explicit Go / Conditional Go / No-Go recommendation. Use when preparing for any product or feature launch, running a pre-launch review, or determining whether a release is safe to ship. Produces a function-by-function readiness status, a ranked blockers list with owners and deadlines, a risk register, and a clearly reasoned launch recommendation.
Thumbnail Creator
Generate article or newsletter thumbnail candidates using the Gemini API from inside Claude Code. Claude reads article copy, proposes composition concepts, writes image generation prompts incorporating brand specs, calls Gemini to generate the images, evaluates the results via computer vision, and returns ranked candidates with rationale. Use when asked to create thumbnails, generate cover images, or produce visual candidates for an article or newsletter.
Substack Notes Scraper
Scrapes a Substack Notes page and exports engagement data to a formatted .xlsx file. Use when asked to download, analyse, or export Substack Notes performance data including likes, comments, and restacks. Produces a formatted spreadsheet with conditional formatting, summary stats, and per-note engagement metrics.
Notes Humanizer
Strips AI writing patterns from text and rewrites it to sound genuinely human by removing statistical defaults and injecting the specific signals that human writers produce. Use when a draft reads as AI-generated, over-polished, or rhythmically uniform — including blog posts, emails, LinkedIn posts, or any prose that needs to sound like a real person wrote it. Produces a pattern audit, side-by-side comparison, itemised change log, and clean rewritten output ready to paste.
Instagram Post Downloader
Downloads and saves Instagram posts as high-resolution files. Use when asked to download, save, or archive an Instagram post, reel thumbnail, or carousel. Fetches images from Instagram's CDN, saves them into a named folder, and stitches carousel slides into a single PDF. Supports batch downloading of multiple URLs at once.
Aeo Optimizer
Optimize an article for Answer Engine Optimization (AEO) — restructuring content so AI engines like ChatGPT, Perplexity, and Claude can extract, quote, and cite it. Rewrites headings as questions, drops 50-80 word answer capsules, audits paragraph length, and flags trust signals. Use when asked to AEO-optimize, make content AI-readable, improve AI citation chances, or adapt an article for answer engines.
Strategic Narrative Generator
Generate the strategic story connecting a product roadmap to company goals in a form non-technical stakeholders can repeat. Use when asked to explain the roadmap, present strategy to leadership or the board, write the why behind the roadmap, create a narrative for all-hands, or make the roadmap tell a story. Produces a themed narrative with executive summary, progression arc, hard-question preparation, and what's-not-on-the-roadmap section.
Stakeholder Influence Mapper
Map stakeholders for a product decision and produce a tailored influence strategy with talking points. Use when asked to get alignment, build consensus, get buy-in from engineering or finance or legal, navigate organisational resistance, or plan stakeholder conversations for a major initiative. Produces a stakeholder map, recommended conversation sequence, and tailored talking points per stakeholder.
Executive Update
Transform detailed product updates into concise executive briefings. Use when asked to write an executive update, leadership update, product update for the exec team, or a C-suite product briefing. Produces a structured 250-word briefing with headline, key metrics, progress, risks, decisions needed, and next steps.
Competitor Signal Tracker
Analyse competitor moves and translate them into strategic implications for your product roadmap. Use when a competitor announces a new feature, pricing change, partnership, or strategic shift, or when producing a periodic competitive intelligence report. Produces a categorised signal analysis with reactive-vs-proactive assessment, threat ratings, specific roadmap implications, and recommended responses with owners.
Competitive Intelligence Monitor
Monitor competitor signals and surface strategic implications for your roadmap. Use when asked to monitor competitors, track the competitive landscape, produce a competitive briefing, or understand what has changed in the market this week or month. Produces a structured intelligence brief with high/medium/low priority signals, roadmap implications, and a strategic landscape summary.
Ambiguity Resolver
Structure vague opportunities and unclear briefs into actionable one-page problem statements. Use when asked to clarify a vague brief, frame an undefined problem, make sense of an unclear opportunity, or when the user says 'we need to figure out what to do about X' or 'I've been asked to look into Y'. Produces a structured problem brief with reframed questions, scoped boundaries, and a minimum viable research plan.