Gives Claude Code verified know-how for running DNAnexus genomics data, apps, and workflows via the dx CLI and dxpy.
An end-to-end PyMC skill covering hierarchical model building, NUTS sampling, convergence diagnostics, and LOO/WAIC model comparison.
A ready-to-run skill for processing, quantifying, and annotating proteomics and metabolomics LC-MS/MS data with pyOpenMS.
Combines body weight, temperature, clinical scores and biomarkers into a single RELSA severity score, then forecasts humane endpoints with ARIMA.
Triage compound libraries with medicinal chemistry rules (Lipinski, PAINS, NIBR) and the medchem query language.
Simulate and audit closed and open quantum systems with QuTiP 5.3, with explicit physical assumptions and convergence checks.
Turns a research question into a manuscript-ready packet of ~60 verified academic references with an evidence matrix, claim-source map, and BibTeX.
A local-only, evidence-bounded workflow for drafting rigorous, structured peer reviews of manuscripts, protocols, preprints, and proposals.
Builds safety-bounded draft structures and runs offline deterministic checks for clinical case, trial, and safety reports.
A Geniml-focused skill that validates BED/universe contracts and plans Region2Vec, scEmbed, and consensus-universe runs with an audit-first mindset.
Analyze, validate, convert, and transform crystal structures and computed materials data with reproducible, provenance-preserving pymatgen workflows.
Equips Claude Code to run FBA, FVA, knockout screens, and flux sampling on genome-scale metabolic models with COBRApy.
A guardrailed guide for using gtars across Python, Rust, and the CLI for BED set algebra, coverage, consensus, tokenization, and refget.
Version-aware guidance for PufferLib: wrapping environments, vectorization, PuffeRL training plans, and safe checkpoint review across the 3.0.0 release and the 4.0 source line.
Generates and audits macro-free, editable PowerPoint conference posters from author-approved local content only.
Mints tamper-evident, post-quantum-signed receipts for consequential agent actions and verifies them offline from the certificate alone.
Spawns N parallel subagents that solve the same task in isolated git worktrees, then evaluates and merges the winning branch.
Designs multi-agent architectures from requirements, generates validated Anthropic/OpenAI tool schemas, and evaluates execution logs for cost, latency, and bottlenecks.
Compiles a goal into a verifiable task plan and enforces an execute–verify–retry–escalate loop through a state machine.
Helps organize, query, validate, and convert neuroscience data (MRI, EEG, PET and more) into the BIDS standard.