FridrichMethod
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- ▌ Bio Methylation Dmr Detection 2 · fridrichmethod bundleDifferentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.
- ▌ Bio Copy Number Cnv Annotation 2 · fridrichmethod bundleAnnotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.
- ▌ Deseq2 Differential Expression · fridrichmethodBulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.
- ▌ Bio Data Visualization Dimensionality Reduction Plots · fridrichmethod bundleProduce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
- ▌ Finishing A Development Branch · fridrichmethodUse when implementation is complete, all tests pass, and you need to decide how to integrate the work
- ▌ Bio Rna Structure Secondary Structure Prediction · fridrichmethod bundlePredicts RNA secondary structure with ViennaRNA, treating the Boltzmann ensemble (partition function, base-pair probabilities, centroid, MEA, stochastic samples) as the object rather than a single MFE fold. Covers consensus folding from alignments (RNAalifold), SHAPE-constrained folding, RNA-RNA interaction (RNAcofold/RNAduplex/RNAup), local and linear-time methods for long RNA, and pseudoknot-aware tools. Use when folding an RNA and choosing between MFE, centroid, MEA, or ensemble sampling; judging whether a single structure is well-defined; folding long RNAs where a global MFE is meaningless; handling suspected pseudoknots; or weighing thermodynamic versus comparative versus deep-learning prediction.
- ▌ Bio Causal Genomics Transcriptome Wide Association · fridrichmethod bundlePerforms gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.
- ▌ Bio Variant Calling Deepvariant 2 · fridrichmethod bundleDeep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.
- ▌ Molfeat Molecular Featurization · fridrichmethod bundleMolecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
- ▌ Bio Causal Genomics Fine Mapping 2 · fridrichmethod bundleIdentify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.
- ▌ Pydeseq2 Differential Expression · fridrichmethodBulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts.
- ▌ Bio Methylation Bismark Alignment 2 · fridrichmethod bundleBisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.
- ▌ Bio Comparative Genomics Comparative Annotation Projection · fridrichmethod bundleProject gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
- ▌ Bio Crispr Screens Mageck Analysis 2 · fridrichmethod bundleMAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.
- ▌ Bio Atac Seq Nucleosome Positioning 2 · fridrichmethod bundleExtract nucleosome positions from ATAC-seq data using NucleoATAC, ATACseqQC, and fragment analysis. Use when analyzing chromatin organization, identifying nucleosome-free regions at promoters, or characterizing nucleosome occupancy patterns from ATAC-seq fragment size distributions.
- ▌ Bio Crispr Screens Batch Correction 2 · fridrichmethod bundleBatch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.
- ▌ Bio Crispr Screens Crispresso Editing 2 · fridrichmethod bundleCRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.
- ▌ Bio Comparative Genomics Gene Tree Species Tree Reconciliati · fridrichmethod bundleReconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.
- ▌ Bio Causal Genomics Mediation Analysis 2 · fridrichmethod bundleDecompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.
- ▌ Bio Comparative Genomics Genome Distance And Species Delinea · fridrichmethod bundleCompute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
- ▌ Bio Clinical Databases Somatic Signatures 2 · fridrichmethod bundleExtract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.
- ▌ Bio Causal Genomics Colocalization Analysis 2 · fridrichmethod bundleTest whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.
- ▌ Bio Causal Genomics Mendelian Randomization 2 · fridrichmethod bundleEstimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
- ▌ Bio Variant Calling Filtering Best Practices 2 · fridrichmethod bundleComprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.
- ▌ Bio Variant Calling Structural Variant Calling 2 · fridrichmethod bundleCall structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data.
- ▌ Onekgpd · fridrichmethod bundleQuery the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals are homozygous-reference at a position, which variants exist in the dataset or carried by specified individuals in a gene or region, the relatedness between two specified individuals. Variants are returned with 1000 Genomes allele frequencies (AF), gnomAD v4.1 exome and genome AF, AlphaMissense score, and HGVSp annotations.
- ▌ Blueprint · fridrichmethodTurn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".
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- ▌ Nature Data · fridrichmethod bundlePrepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.
- ▌ Bio Workflows Cnv Pipeline · fridrichmethod bundleOrchestrates the copy-number pipeline from BAM to segmented, integer-called, annotated CNVs, forking on germline-vs-somatic - CNVkit (somatic exome/panel: coverage -> assay-matched reference/PoN -> fix -> segment -> purity/ploidy-aware call), GATK gCNV (germline rare-CNV cohort), and allele-specific callers (ASCAT/FACETS/PURPLE) for purity/ploidy. Use when committing the build + target/access BED + PoN once (assay-matched), building the reference from normals BEFORE segmenting, fitting purity/ploidy BEFORE integer calls in tumors, centering on the true (non-diploid) mode before GISTIC2 recurrence, or routing cfDNA to ichorCNA. Hands mechanism to the copy-number component skills; not a re-teach of any single step.
- ▌ Bio Population Genetics Plink Basics · fridrichmethod bundleManages PLINK genotype filesets - format conversion (VCF, BED/BIM/FAM, PED/MAP, pgen/pvar/psam) and sample/variant QC (missingness, MAF, HWE, sex check, heterozygosity, KING relatedness) with PLINK 1.9 and 2.0. PLINK rewrites allele bookkeeping: PLINK 1.x A1 defaults to the minor allele and is recomputed every load, silently flipping effect-allele meaning unless --keep-allele-order, while PLINK 2.0 tracks explicit REF/ALT. QC order matters (variant before sample missingness), HWE is controls-only in 1.9 but not 2.0, add midp, and differential case/control missingness injects false hits. Use when converting between PLINK formats or running genotype QC before association, structure, or LD analysis. For LD pruning/clumping see linkage-disequilibrium; for GWAS see association-testing; VCF input from variant-calling/vcf-basics.
- ▌ Bio Tcr Bcr Analysis Mixcr Analysis · fridrichmethod bundleAlign V(D)J reads and assemble TCR/BCR clonotypes with MiXCR, driven by a chemistry-matched preset. Use when choosing/auditing the preset for a library (5'RACE/template-switch vs multiplex-primer amplicon -> rigid vs floating boundaries; RNA vs gDNA -> --rna/--dna; bulk vs 10x single-cell; UMI vs no-UMI -> tag pattern and barcode collapse; kit presets Takara/NEBNext/QIAseq/BD/MiLaboratory); assembling clonotypes by CDR3 vs VDJRegion; setting the reads-vs-UMI-vs-cell quantitation denominator; exporting native MiXCR fields vs AIRR rearrangement TSV for downstream Immcantation/scirpy/VDJtools; and running alignment/chain-usage QC. Keywords: MiXCR, analyze, align, refineTagsAndSort, assemblePartial, assemble, assembleCells, exportClones, exportAirr, exportQc, CDR3, V(D)J, clonotype, UMI, cell barcode, 10x VDJ, license.
- ▌ Pubmed Database 2 · fridrichmethod bundleProgrammatic PubMed access via NCBI E-utilities REST API. Covers Boolean/MeSH queries, field-tagged search, endpoints (ESearch, EFetch, ESummary, EPost, ELink), history server for batches, citation matching, systematic review strategies. Use for biomedical literature search or automated pipelines.
- ▌ Prompt Optimizer · fridrichmethodAnalyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
- ▌ Brand Landingpage · fridrichmethod bundleBrand-first landing page designer — runs a brand-identity interview (colors, typography, shape language), then generates and iterates on a polished landing page via Stitch with deployment-ready HTML. Use when the user asks to create, design, or build a landing page, homepage, or marketing page and has no established visual direction. Skip when they have a design mockup, need a dashboard or app UI, are working at component level, building a multi-page app, or restyling with known design tokens — use frontend-design instead.
- ▌ Bio Methylation Epigenetic Clocks · fridrichmethod bundleComputes DNA methylation age (DNAm age) and pace of aging by applying frozen elastic-net epigenetic clocks to a clean beta matrix with methylclock, dnaMethyAge, or methylCIPHER. Covers the clock menu by question (chronological Horvath/Hannum/skin&blood; health-mortality PhenoAge/GrimAge; DunedinPACE pace; pediatric/gestational; mitotic epiTOC), age acceleration (EAA/IEAA/EEAA) as the real endpoint, the principal-component (PC) clock fix for the per-CpG reliability crisis, and EPICv2 clock-CpG dropout with missing-CpG imputation bias. Use when estimating epigenetic age, computing age acceleration, choosing a clock for an outcome, assessing clock reliability, or porting a clock to EPICv2. A clock is a frozen predictor: do not GO-enrich its CpGs and do not train it here. For cell-count adjustment (IEAA) see cell-type-deconvolution; for predictor training/validation/leakage see machine-learning/model-validation; for survival modeling of age acceleration see clinical-biostatistics/survival-analysis.
- ▌ Bio Methylation Array Qc Filtering · fridrichmethod bundlePerforms probe filtering and sample-level QC on Illumina Infinium methylation arrays (450K / EPIC / EPICv2) to decide which probes and samples to trust. Drops detection-p-failed and low-bead-count probes, removes cross-reactive/non-specific probes (Chen 2013 / Pidsley 2016 lists via maxprobes), excludes SNP-overlapping probes with dropLociWithSnps, and handles sex-chromosome probes. Collapses EPICv2 replicate probes with betasCollapseToPfx and harmonizes across array versions (EPICv2 hg38 vs 450K/EPIC hg19, intersect plus mLiftOver). Runs sample-identity QC: getSex sex prediction vs sample sheet for swap detection, rs-SNP fingerprint clustering for duplicates/swaps, and Sentrix chip/array-position batch diagnosis. Use when filtering methylation array probes, detecting sample swaps or mislabels, collapsing EPICv2 replicates, or merging 450K/EPIC/EPICv2 cohorts. For IDAT-to-corrected-beta normalization see array-preprocessing; for batch correction and study design see ewas-design.
- ▌ Energy Procurement · fridrichmethodCodified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.
- ▌ Bio Long Read Splicing · fridrichmethod bundleAnalyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3 (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.
- ▌ Bio Methylation Methylkit · fridrichmethod bundleImports Bismark coverage or cytosine-report files into the methylKit object model, then runs the import-to-results spine - filterByCoverage, normalizeCoverage, unite/destrand, calculateDiffMeth, getMethylDiff - for both per-CpG (DMC) and fixed-tile (DMR) differential methylation, plus tileMethylCounts, PCA/correlation/clustering QC, and assocComp/removeComp batch handling. Covers the silent default traps that shape the false-positive rate: overdispersion='none' does no correction while 'MN' forces the F-test (ignoring test='Chisq'), adjust defaults to SLIM not BH, getMethylDiff defaults difference=25/qvalue=0.01, cov.bases=0 admits single-CpG tiles, and pool destroys biological replication. Use when importing bisulfite count tables, filtering/normalizing/uniting methylation samples, running methylKit differential testing, or QC-ing methylomes. For per-site test-choice (count vs continuous) see differential-cpg-testing; for selection-aware region FDR (dmrseq/DSS) see dmr-detection.
- ▌ Bio Population Genetics Association Testing · fridrichmethod bundleSingle-variant common-variant GWAS with plink2 --glm (linear/logistic, Firth) and the linear mixed models GEMMA, BOLT-LMM, SAIGE, regenie (SPA). A GWAS statistic is valid only when genotype is independent of unmodeled phenotype drivers after the chosen covariates and random effects, so the engine follows sample structure and case:control imbalance, not taste: PC covariates absorb continuous ancestry but cannot remove relatedness (a covariance structure needing an LMM), genomic inflation above 1 is mostly true polygenic signal not confounding (the LDSC intercept is the diagnostic), LOCO prevents proximal contamination, and SPA/Firth keep the tail calibrated at extreme imbalance and low MAC. Use when running single-variant GWAS, choosing between a GLM and a mixed model, or controlling stratification, relatedness, and case:control imbalance. For rare-variant aggregation (burden, SKAT, SKAT-O, ACAT) see rare-variant-association; fine-mapping and MR see causal-genomics; PRS see clinical-databases/polygenic-risk.
- ▌ Bio Temporal Genomics Trajectory Modeling · fridrichmethod bundleModels continuous temporal trajectories from BULK or time-resolved omics where the x-axis is measured experimental time: penalized GAMs (mgcv) for smooth trends and changepoint detection (segmented, ruptures) for abrupt regime shifts. Use when deciding between a smooth GAM and a changepoint model; choosing the GAM distribution (nb() plus a library-size offset for raw counts vs Gaussian on vst/log-CPM); setting the basis-dimension ceiling k below the number of timepoints and letting REML pick wiggliness; handling residual autocorrelation across timepoints with corAR1/bam(rho=); testing whether two conditions' trajectories diverge with an ordered-factor difference smooth; and choosing a changepoint search/cost/penalty (Pelt/Binseg, l2/rbf). Not for single-cell pseudotime (see single-cell/trajectory-inference).
- ▌ Bio Population Genetics Population Structure · fridrichmethod bundleInfers and describes population structure with PCA (plink2 --pca, smartpca/EIGENSOFT, FlashPCA2), model-based clustering (ADMIXTURE, fastSTRUCTURE), FST estimators (Weir-Cockerham vs Hudson), and f-statistics (f3/f4/D via AdmixTools/admixr), plus Python plotting of PCs and Q barplots. Every output is a model-conditioned description of variance, not truth: PCs conflate ancestry with LD/inversions/relatedness/batch, ADMIXTURE Q-values are panel- and K-dependent artifacts, and CV-minimum K is a guide not the true population count. FST must combine SNPs as a ratio of averages (sum numerators / sum denominators), never an average of per-SNP FST; negative per-SNP FST is normal and must not be clamped. f3/f4/D need a block jackknife or the significance is fake. Use when running PCA, ADMIXTURE, FST, or f-statistics on QC'd genotypes. For QC and KING relatedness see plink-basics; for LD pruning see linkage-disequilibrium; for array-based Python pipelines see scikit-allel-analysis.
- ▌ Token Budget Advisor · fridrichmethodOffers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
- ▌ Production Scheduling · fridrichmethodCodified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.
- ▌ Bio Population Genetics Scikit Allel Analysis · fridrichmethod bundleIn-memory Python population genetics with scikit-allel - GenotypeArray/HaplotypeArray/AlleleCountsArray, diversity (pi, theta, Tajima's D), SFS, FST (Weir-Cockerham, Hudson, Patterson), f3/D admixture stats, LD pruning, PCA, and selection scans (iHS, XP-EHH, nSL, Garud H). Nearly every statistic is a ratio or density with one silent denominator bug in two faces: omit is_accessible= and per-base pi/theta divide by total span not accessible bp (deflated 2-5x); average per-SNP FST instead of sum(a)/(sum(a)+sum(b)+sum(c)) and the estimate is rare-variant-biased - scikit-allel returns the (a,b,c) and (num,den) components on purpose to force ratio-of-sums. to_n_alt default fill=0 imputes missing to reference; sfs() is unfolded and wants derived not alt counts; iHS/XP-EHH need phased data and standardization. Use when computing population-genetics statistics in Python, scanning for selection, or building array pipelines. For PLINK QC see plink-basics; for VCF input see variant-calling/vcf-basics.
- ▌ Quality Nonconformance · fridrichmethodCodified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier quality issues.
- ▌ Bio Sequence Similarity 2 · fridrichmethod bundleFind homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.
- ▌ Iso 13485 Certification · fridrichmethod bundlePrepare and structurally review ISO 13485 QMS scope, controlled documentation, and local evidence manifests. Use for ISO 13485 certification-readiness documentation or for separating related FDA QMSR, MDSAP, and EU MDR/IVDR evidence boundaries; not for legal applicability, compliance, or certification decisions.
- ▌ Customs Trade Compliance · fridrichmethodCodified expertise for customs documentation, tariff classification, duty optimization, restricted party screening, and regulatory compliance across multiple jurisdictions. Informed by trade compliance specialists with 15+ years experience. Includes HS classification logic, Incoterms application, FTA utilization, and penalty mitigation. Use when handling customs clearance, tariff classification, trade compliance, import/export documentation, or duty optimization.
- ▌ Inventory Demand Planning · fridrichmethodCodified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
- ▌ Returns Reverse Logistics · fridrichmethodCodified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.
- ▌ Bio Tumor Fraction Estimation · fridrichmethod bundleEstimates tumor fraction (the genome-wide proportion of cfDNA molecules that are tumor-derived, the cfDNA analogue of bulk-tumor purity) from shallow whole-genome sequencing with ichorCNA, an HMM over 1 Mb bins that jointly EM-estimates tumor fraction, ploidy, and subclonal prevalence over a normal/ploidy grid. Encodes the load-bearing reframes: tumor fraction is the quantity that travels across assays and is NOT mutation VAF (clonal-het VAF approximately TF/2), CNA-based estimation has a hard ~3 percent limit-of-detection floor, and near-diploid or copy-neutral-LOH genomes return a falsely low value. Selects the estimator by data type (sWGS to ichorCNA, deep panel to max-VAF, methylation to deconvolution, sub-3 percent to fragmentomics or methylation). Use when quantifying tumor burden from a liquid biopsy, picking a tumor-fraction estimator for a given assay, or reconciling a TF estimate against a panel VAF.
- ▌ Bio Workflows Cnv Pipeline 2 · fridrichmethod bundleEnd-to-end copy number variant detection workflow from BAM files. Covers CNVkit analysis for exome/targeted sequencing with visualization and annotation. Use when detecting copy number alterations from sequencing data.
- ▌ Logistics Exception Management · fridrichmethodCodified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when handling shipping exceptions, freight claims, delivery issues, or carrier disputes.
- ▌ Carrier Relationship Management · fridrichmethodCodified expertise for managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by transportation managers with 15+ years experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance vetting. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.
- ▌ Bio Population Genetics Plink Basics 2 · fridrichmethod bundlePLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files or running QC.
- ▌ Bio Population Genetics Association Testing 2 · fridrichmethod bundleGenome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association tests.
- ▌ Bio Data Visualization Genome Browser Tracks 2 · fridrichmethod bundleGenerate genome browser visualizations using pyGenomeTracks or IGV batch scripting for publication figures. Use when creating publication figures of genomic regions with multiple data tracks.
- ▌ Bio Population Genetics Population Structure 2 · fridrichmethod bundleAnalyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population stratification with PCA or admixture.
- ▌ Bio Population Genetics Scikit Allel Analysis 2 · fridrichmethod bundlePython population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.
- ▌ Bio Data Visualization Specialized Omics Plots 2 · fridrichmethod bundleReusable plotting functions for common omics visualizations. Custom ggplot2/matplotlib implementations of volcano, MA, PCA, enrichment dotplots, boxplots, and survival curves. Use when creating volcano, MA, or enrichment plots.
- ▌ Bio Spatial Transcriptomics Image Analysis · fridrichmethod bundleProcess and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.
- ▌ Bio Consensus Sequences · fridrichmethod bundleGenerate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.
- ▌ Bio Spatial Transcriptomics Spatial Data Io · fridrichmethod bundleLoad spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.
- ▌ Bio Spatial Transcriptomics Spatial Domains · fridrichmethod bundleIdentify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.
- ▌ Bio Gatk Variant Calling · fridrichmethod bundleVariant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.
- ▌ Bio Variant Normalization · fridrichmethod bundleNormalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis.
- ▌ Bio Spatial Transcriptomics Spatial Neighbors · fridrichmethod bundleBuild spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.
- ▌ Bio Spatial Transcriptomics Spatial Multiomics · fridrichmethod bundleAnalyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.
- ▌ Bio Spatial Transcriptomics Spatial Proteomics · fridrichmethod bundleAnalyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.
- ▌ Bio Spatial Transcriptomics Spatial Statistics · fridrichmethod bundleCompute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.
- ▌ Bio Variant Calling Clinical Interpretation · fridrichmethod bundleClinical variant interpretation using ClinVar, ACMG guidelines, and pathogenicity predictors. Prioritize variants for diagnostic and research applications. Use when interpreting clinical significance of variants.
- ▌ Bio Variant Calling Filtering Best Practices · fridrichmethod bundleComprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.
- ▌ Bio Spatial Transcriptomics Spatial Communication · fridrichmethod bundleAnalyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.
- ▌ Bio Spatial Transcriptomics Spatial Deconvolution · fridrichmethod bundleEstimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots.
- ▌ Bio Spatial Transcriptomics Spatial Preprocessing · fridrichmethod bundleQuality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.
- ▌ Bio Spatial Transcriptomics Spatial Visualization · fridrichmethod bundleVisualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.
- ▌ Bio Variant Calling Structural Variant Calling · fridrichmethod bundleCall structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data.
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- ▌ Bids · fridrichmethod bundleUse 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.
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- ▌ Pymol · fridrichmethod bundleVisualize, analyze, and render protein and molecular structures using PyMOL. Use when the user wants to create images of protein structures, perform structural alignments or superposition, measure distances or contacts, highlight binding sites or active site residues, color by B-factor/pLDDT, or analyze protein-ligand interactions. Do not use for docking, molecular dynamics, or sequence-only analysis.
- ▌ Setup · fridrichmethodFirst-time setup for protein design tools. Use this skill when: (1) User is new and hasn't run any tools yet, (2) Commands fail with "file not found" or "modal: command not found", (3) Modal authentication errors occur, (4) User asks how to get started or set up the environment, (5) biomodals directory is missing or tools aren't working.
- ▌ Matlab · fridrichmethod bundleBuild, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
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- ▌ Medchem · fridrichmethod bundleMedicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.
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- ▌ Academic Paper Composer · fridrichmethod bundleSystematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
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- ▌ Scrna Qc · fridrichmethod bundleExecute the MAD-based single-cell RNA-seq QC workflow (scripts + Python API) to filter low-quality cells and emit reports plus filtered AnnData files.
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