upstream/K-Dense-AI-scientific-agent-skills/catalogue/docs/skills.md

@@ -2,9 +2,9 @@
 title: "Scientific Skills"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/991bd993/docs/skills.md
-upstream_sha: 991bd993
-imported_at: 2026-08-08
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/b2a92ba0/docs/skills.md
+upstream_sha: b2a92ba0
+imported_at: 2026-08-14
 prompt_class: catalogue
 upstream_changes: accepted
 author: upstream
@@ -19,6 +19,7 @@ validated: false
 - **[DepMap](../skills/depmap/)** - Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets
 - **[Imaging Data Commons](../skills/imaging-data-commons/)** - Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses
 - **[PrimeKG](../skills/primekg/)** - Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological data including genes, drugs, diseases, phenotypes, and more. Integrates 20+ biomedical resources into a single knowledge graph for drug repurposing, disease mechanism exploration, and target identification
+- **[NCATS ARAX](../skills/ncats-arax/)** - Query the NCATS Biomedical Data Translator ARAX production API for bounded, typed, provenance-rich biomedical knowledge-graph relationships. Supports Biolink-constrained one-hop lookups over RTX-KG2, endpoint-pinned two-hop traversal, explicit selection of two to five ARAX federation providers, separate entity normalization of free text to canonical CURIEs before any graph query, and qualifier-aware edges. The bundled standard-library client (`arax_client.py`, Python 3.10+, no API key) runs a `preflight` check against the production OpenAPI, performs review-only `normalize` calls, and saves both a bounded `summary.json` of query-edge bindings, publications, and knowledge-source provenance and the exact TRAPI `response.json`. Queries and caller metadata may be publicly visible, so the skill is for public, nonsensitive research questions only; returned paths are candidates to verify against literature and authoritative databases, response order is not a rank, and a zero result means "not returned under these constraints" rather than absence of a relationship. Not for inference, ranking, open-ended pathfinding, or clinical guidance
 - **[U.S. Treasury Fiscal Data](../skills/usfiscaldata/)** - Free, open REST API from the U.S. Department of the Treasury providing 54 datasets and 179 data tables covering federal fiscal data. No API key required. Access national debt (Debt to the Penny back to 1993, Historical Debt back to 1790), Daily Treasury Statements (TGA balances, deposits/withdrawals), Monthly Treasury Statements (federal budget receipts and outlays), Treasury securities auctions data (bills, notes, bonds, TIPS, FRNs since 1979), average interest rates on Treasury securities, Treasury reporting exchange rates (quarterly for 170+ currencies), I Bond and savings bond rates, TIPS/CPI data, and more. Supports filtering, sorting, pagination, and CSV/XML/JSON output formats
 - **[Ontology Term Resolution](../skills/ontology-term-resolution/)** - Resolve free-text scientific labels to ontology term IDs and validate existing CURIEs against the EBI Ontology Lookup Service (OLS4). Annotate tissue, cell type, disease, phenotype, assay, chemical, organism, sex, and developmental-stage fields; prepare metadata for GEO, ENA, BioSamples, CELLxGENE, HCA, or ISA-Tab submission; audit a metadata table of term IDs; check whether a term is obsolete and what replaced it; and map between ontologies (UBERON, CL, MONDO, HPO, EFO, ChEBI, NCBITaxon, GO, PATO). Python 3.11+ standard-library scripts with no third-party packages; needs public network access to the OLS4 API, no API key
 - **[Pathogen Variant Surveillance](../skills/pathogen-variant-surveillance/)** - Query live pathogen genomic surveillance data through the GenSpectrum LAPIS API to find which viral lineages are circulating now, how fast they are growing, and what mutations they carry. Covers 15 instances spanning SARS-CoV-2 (open GenBank data via CoV-Spectrum), influenza A including H5N1 and the seasonal H3N2/H1N1pdm clades, and the Pathoplexus organisms — RSV-A/B, mpox, measles, dengue, West Nile, hMPV, Ebola Zaire/Sudan, and CCHF. Field names, lineage columns, and date columns are read from each instance's live schema rather than assumed, because they differ: `dateFrom` is correct on SARS-CoV-2 and a hard error on H5N1. Lineage names are resolved against the live pango-designation nomenclature, which catches the withdrawn and redesignated names that make remembered lineage facts actively wrong rather than merely stale, and the recombinant parentage that the query API does not carry. Bundled Python 3.11+ scripts are standard-library only and need no API key: `resolve_lineage.py` (is this name still valid, what does it expand to), `lineage_prevalence.py` (discovers the most common lineages in a window with `--top`, then reports weekly prevalence with Wilson intervals, coverage flags, and a dispersion-guarded log-odds growth fit), `mutation_profile.py` (defining mutations, or a diff between two lineages, for assay-match questions), and `reporting_lag.py` (measures how long sequences take to arrive and derives a trust cutoff). Use cases: variant situation reports, vaccine and assay target monitoring, checking whether a lineage in a manuscript is still designated, H5N1 clade tracking by host and region, and any question whose answer changed after the model was trained. Sequence counts are not case counts, and outputs are surveillance research, not clinical or public-health guidance
@@ -98,6 +99,9 @@ validated: false
 ### Pharmacology & Pharmacometrics
 - **[PK/PD Modelling](../skills/pkpd-modeling/)** - Pharmacokinetic and pharmacodynamic modelling and simulation across the full development path: non-compartmental analysis, compartmental fitting and model selection, population PK dataset preparation, regimen simulation, exposure-response, bioequivalence, allometric scaling and first-in-human dose, static drug-interaction prediction, and Bayesian therapeutic drug monitoring. Nine Python 3.11+ scripts (numpy and scipy, network-free, no proprietary software) each make the choices that decide the answer explicit rather than implicit: `nca.py` requires the AUC method, BLQ rule, and lambda_z window up front and selects the terminal phase by *adjusted* r-squared with Tmax excluded, then flags excessive AUCinf extrapolation and a terminal window shorter than two half-lives; `fit_compartmental.py` estimates on the log scale and compares models by AIC, BIC, and F test together — they disagree, and it reports per-parameter RSE and correlations because convergence is not identifiability — while separating structural misspecification (a residual runs test) from a wrong error model; `check_popk_dataset.py` catches the NM-TRAN defects that never stop a run (non-numeric DV read as zero, a blank covariate read as 0 kg, `ADDL` without `II`, duplicate timestamps applied in file order); `simulate_regimen.py` reports population target attainment rather than the typical patient, with analytical superposition for linear models and integrated Michaelis-Menten where superposition is invalid; `exposure_response.py` fits Emax/sigmoid models, flags a plateau outside the observed data, and evaluates concentration-QTc against the ICH E14 10 ms threshold using the upper bound of the 90% CI; `bioequivalence.py` keeps average BE, EMA ABEL, and FDA RSABE strictly apart and refuses reference-scaling on a 2x2 design, with sample size integrated over the sampling distribution of the SD; `allometry_and_fih.py` adds Anderson-Holford maturation below 20 kg and pairs a NOAEL-derived MRSD with MABEL for immunomodulators; `ddi_static.py` applies the ICH M12 basic and mechanistic static models with their cut-offs and the fm-implied ceiling; and `tdm_bayes.py` performs MAP Bayesian individualisation, flagging a single level as unable to separate clearance from volume. Fourteen reference files cover popPK estimation and BLQ methods, PBPK, TMDD and biologics, special populations, dataset standards, regulatory guidance, and the software ecosystem (Pharmpy, NONMEM, nlmixr2, Monolix, Simcyp, GastroPlus — oriented towards, never invoked). The skill computes, diagnoses, and structures; it does not conclude bioequivalence, select a trial dose, recommend a dose for a patient, rule out QT liability, or replace a qualified pharmacometrician, clinical pharmacologist, or the regulatory review
 
+### Preclinical Research & Animal Welfare
+- **[RELSA Severity Assessment](../skills/relsa-severity-assessment/)** - Multivariate severity assessment and humane endpoint prediction for laboratory animal studies, implementing the RELSA (RELative Severity Assessment) score (Talbot et al., 2022) and ARIMA-based foRcast forecasting (Lutscher et al., 2026). Combines welfare readouts — body weight, temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one score per animal per time point, expressed relative to a reference set of known burden (0 = baseline, 1 = the reference set's maximum deviation), with each variable's weight reported alongside the score so a score is explainable rather than opaque. Forecasts an individual animal's next score with a 95% prediction interval so animals heading for a humane endpoint can be flagged before they arrive, and derives candidate attention and danger zones on the RELSA scale by kernel density estimation. Three Python scripts (`relsa_score.py`, `forecast_relsa.py`, `kde_thresholds.py`; numpy/pandas/scipy, statsmodels for forecasting, matplotlib for figures, no network) make the four choices that actually determine the result explicit: variable directionality (a `--turned` variable that rises under worsening contributes nothing at all if misdeclared, silently, because deviations the wrong way are floored at zero — and body temperature falls in sepsis but rises in fever models), the reference set that fixes the scale, how a clinical score with a zero baseline is mapped rather than ratio-normalized, and which variables are measured throughout, since changing composition moves the score by itself. Missing values are dropped, never imputed. Forecast quality is evaluated by RMSE, PICP, and MPIW. Use cases: severity scoring across a cohort, endpoint risk triage, comparing burden between treatment groups on a common relative scale, threshold and zone definition, and writing the severity-assessment section of a 3Rs/refinement analysis or an EU Directive 2010/63/EU application. Both procedures are aids to severity assessment, not decision rules
+
 ### Proteomics & Mass Spectrometry
 - **[matchms](../skills/matchms/)** - Reproducible MS/MS processing and spectral-library search with metadata/peak filtering, cosine and exact/greedy modified-cosine scoring, fast BLINK and Flash modes, structured sparse scores, spectral networks, and MGF/MSP/mzML/mzXML/JSON/mzSpecLib/USI workflows
 - **[pyOpenMS](../skills/pyopenms/)** - Comprehensive mass spectrometry data analysis for proteomics and metabolomics (LC-MS/MS processing, peptide identification, feature detection, quantification, chemical calculations, and integration with search engines like Comet, Mascot, MSGF+)
@@ -154,6 +158,7 @@ validated: false
 - **[Pymatgen](../skills/pymatgen/)** - Analyze, validate, convert, and transform structures and computed materials data with the current split stack: `pymatgen==2026.5.4`, `pymatgen-core==2026.7.16`, and `mp-api==0.46.4` on Python 3.11+. Covers provenance-preserving local phase diagrams, symmetry sensitivity, transformations, and electronic-structure I/O; Materials Project queries are explicitly bounded and require user-approved network access plus the named `MP_API_KEY`
 
 ### Engineering & Simulation
+- **[Lab Hardware CAD](../skills/lab-hardware-cad/)** - Design custom laboratory hardware as parametric build123d 0.11.1 models and export fabrication-ready STEP, STL, and DXF: microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. The hard part is rarely the geometry — it is that the part must mate with equipment whose dimensions are fixed by a published standard or vendor drawing — so the workflow routes to exactly one device family reference (microfluidics, optomechanics, labware adapters, behavior rigs), requires every interface dimension to be looked up in the bundled `standards.json` (ANSI/SLAS microplate footprints, optical-table and cage-system patterns) rather than written from memory, sizes receiving features at the mating part's maximum material condition before clearance, and fixes the fabrication process and material first because process sets minimum wall, minimum feature, achievable tolerance, and autoclave/solvent compatibility. Models expose `build()` plus a machine-checkable `interfaces()` declaration with named unit-suffixed constants split into INTERFACE and DESIGN blocks; `gen.py` emits the STEP alongside a manifest recording source hash, resolved parameters, library versions, and measured bounding box, volume, and validity, `check.py facts`/`interfaces` gates fabrication against the standards database, and `snapshot.py` produces the required multi-view renders. Python 3.10-3.14; the standards lookup and interface check run on the standard library alone and no network access is needed. Model files are executed, not parsed, so only run sources authored in-session or from a trusted location. Not for FEA, CFD, molecular structure, or plotting
 - **[MATLAB/Octave](../skills/matlab/)** - Build, review, migrate, and safely plan numerical workflows against proprietary MATLAB R2026a or the distinct free GNU Octave 11.3.0 surface. Covers arrays, tables/timetables, tests, projects, graphics, MAT-file inventory, and explicit Python interoperability. Bundled Python 3.11+ tools are local static/dry-run helpers and never launch either runtime; do not assume product, toolbox, license, API, numerical, or graphics equivalence
 - **[FluidSim](../skills/fluidsim/)** - Plan, configure, inspect, restart, and analyze bounded FluidSim 0.9.0 pseudospectral CFD simulations, with the verified FluidFFT 0.4.5/pyFFTW 0.15.1 stack. Requires explicit equations, units, solver parameters, convergence tests, and CPU/RAM/disk/wall-time bounds; MPI/native FFT execution must follow an approved site scheduler/toolchain workflow. A completed or stable run is not proof of numerical convergence or physical validity
 - **[OpenPIV](../skills/openpiv/)** - Particle Image Velocimetry (PIV) analysis with OpenPIV 0.25.4 on Python 3.10+. Extract velocity fields from PIV image pairs by cross-correlating interrogation windows, validate and replace spurious vectors, and compute vorticity, strain rate, and turbulence statistics from measured velocity fields. Use for fluid-dynamics and flow-visualization experiments; numpy, scipy, scikit-image, and matplotlib arrive as dependencies, and no network access is needed after install