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Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.

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Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

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Infers person-to-person transmission from pathogen genomes using outbreaker2 (Campbell 2018), TransPhylo (Didelot 2017), phybreak (Klinkenberg 2017), BadTrIP (De Maio 2018), SCOTTI (De Maio 2016), BEASTLIER (Hall 2015), and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs / Walker 2013; MRSA <=15 / Coll 2017; C. difficile <=2 / Eyre 2013; Klebsiella <=21 / Snitkin 2012), models within-host diversity and transmission bottlenecks (Worby-Lipsitch-Hanage 2014; McCrone 2018; Sobel Leonard 2017; Lythgoe 2021 SARS-CoV-2), integrates contact-tracing data, distinguishes generation interval from serial interval (Britton & Scalia Tomba 2019; Ali 2020), attributes source via Bayesian source attribution (Mather 2013 DT104; islandR), and reconciles transmission-network reconstruction with epi metadata. Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB / chronic HIV.

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Assigns isolate identity at the right resolution for the question -- ANI / Mash species triage, 7-locus MLST historical comparability, cgMLST / wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR, SeqSero2 for Salmonella; SerotypeFinder for E. coli; Kaptive K/O for Klebsiella; SeroBA for pneumococcus; spa + SCCmec for S. aureus), and lineage callers (TB-Profiler / Mykrobe Coll-Napier barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas / database versions (chewBBACA vs Ridom vs EnteroBase), assigning MTBC lineage with the Napier 2020 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with explicit pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.

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Produce 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.

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Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

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Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CTCF-only/DNase-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Add MAUI DevFlow to a .NET MAUI project with agent package references, MauiProgram.cs registration, Blazor WebView support, GTK variants, Central Package Management guidance, and verification commands. USE FOR: first-time DevFlow setup, reviewing what files to edit, choosing DevFlow packages, or continuing after `maui devflow init` installs skills. DO NOT USE FOR: troubleshooting an already-integrated app that cannot connect, iterative app debugging, UI inspection, or generic MAUI build failures (use maui-devflow-debug). INVOKES: maui devflow CLI and dotnet CLI.

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Use this skill when the user asks about CDL driver wellness, retention strategies, what causes driver turnover, common health issues (obesity, hypertension, sleep apnea, mental health), benefits packages, home-time policies, and how to build a culture that keeps drivers. Reference ATA Workforce Development + FMCSA Wellness Initiative.

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Use this skill when the user asks about onboarding a new CDL driver — pre-hire process, day-zero requirements, application + DQF setup, road test, drug & alcohol pre-employment, Clearinghouse Full query, MVR initial pull, PSP report, ELDT verification, sequence of activities + timing, and what NOT to do (handing keys before testing complete). Cite 49 CFR 391, 382.

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Use this skill when the user asks about commercial driver drug & alcohol testing under 49 CFR 382 + 40 — the six testing reasons (pre-employment, random, post-accident, reasonable suspicion, return-to-duty, follow-up), MRO process, SAP/return-to-duty process, post-accident triggers, random selection rates, what panels are tested, refusal definitions, or testing programs and consortiums. Cite specific 49 CFR 382 or 40 subsection.

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Use this skill when the user asks about accident registers, DOT-recordable accident definitions, what counts as a "reportable accident" under 49 CFR 390.5 + 390.15, accident register retention, post-accident testing trigger logic, FMCSA accident reporting (no longer required to FMCSA directly for non-fatal accidents), insurance reporting timelines, and what to do after a CMV crash. Cite 49 CFR 390.5 + 390.15.

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Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies. Activate on: React Query, SWR, optimistic updates, cache invalidation, prefetching, stale-while-revalidate, infinite queries. NOT for: REST API design (use api-architect), database queries (use database skills), GraphQL schema (use graphql-expert).

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Validate finite-DAG order objectives and construct reachability-chain or direct-edge path partitions with explicit proof boundaries. NOT for equating reachability chains with direct-edge paths without checking the chosen objective.

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Treat tokens as a coding swarm's cost-of-goods-sold and legibility engine while accounting for finite subscription allowance when per-call price is unknown. Covers provider windows, remaining-usage evidence, burn forecasts, preemptive checkpoint/model switching, per-agent budgets, compaction, shared digests, context degradation, and spend metering. Activate on: "token budget", "context budget", "compaction strategy", "briefing as compression", "context rot", "summarization collapse", "COGS for agents", "subscription usage remaining", "five-hour or weekly limit", "burn forecast", "model switch before limit", or "/context-economics-for-agent-swarms". NOT for: memory architecture (use always-on-agent-architecture), single-prompt wording (use prompt-engineer), mechanism-design proofs (use nisan-et-al-2007-algorithmic-game-theory), physical containment (use sandboxed-adversarial-test-harness), or production rebodiment and fencing (use agent-resurrection-and-body-continuity).

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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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Construct a finite oriented simplicial or cellular cochain model and compute its stated linear invariants when coefficient spaces, maps, metrics, and observations are explicit. Use for scoped Hodge, rank, or residual calculations; not for attributing agent intent or authorizing effects. NOT for attributing intent, identifying a liar, or authorizing external effects.

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Overlay system on @ai-matrx/summon: dialogs, sheets and windows declared once in features/overlays/definitions, opened by dispatch. Use when opening, adding, debugging an overlay, or touching features/overlays/**, SummonHost, defineOverlay, useOpenX. NOT for WindowPanel chrome (use window-panels).

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Startup rules for the app shell, layouts, and global providers. Use when editing app/Providers.tsx, app/DeferredSingletonWrapper.tsx / DeferredSingletonCore.tsx, a layout.tsx, StoreProvider, or a global provider; adding startup fetches or mount-time useEffects high in the tree; or reading auth state (isAdmin, isGuest, fingerprintId) client-side.

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Locate CEconItemView::operator= in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CEconItemView::operator=" (symbol CEconItemView_operator=). Copy-assignment operator. Shortlist via call sites in weapon-give/refresh paths that copy an existing CEconItemView into another (this-pointer copy of definition index, seed, wear, attribute list and item ID fields — a field-by-field copy, not a memcpy). Trigger: CEconItemView_operator=, CEconItemView::operator=

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Locate CCSPlayerPawn::SetModelFromLoadout in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromLoadout" (symbol CCSPlayerPawn_SetModelFromLoadout). Shortlist alongside SetModelFromClass (same file/call region — sibling paths in pawn model selection). This variant resolves the model from the player's loadout/inventory items (agent skin selection), referencing inventory services and item definition indexes before calling SetModel. Trigger: CCSPlayerPawn_SetModelFromLoadout, CCSPlayerPawn::SetModelFromLoadout

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Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".

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