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Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.

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Detects somatic mutations in circulating tumor DNA, treating low-VAF detection as a signal-versus-noise problem set by error suppression and molecules sampled, not by the choice of caller. Distinguishes de novo CALLING (scanning a panel for unknown variants, bounded by per-locus error and multiple testing) from tumor-informed DETECTION (tracking a pre-specified variant set, where panel integration reaches single-ppm). Covers VarDict and Mutect2 for de novo calling, UMI-aware callers, and a pysam-based known-variant VAF tracker, with matched-WBC subtraction as the mandatory defense against clonal hematopoiesis (the dominant false positive). Use when calling or tracking tumor mutations from plasma cfDNA, setting a VAF threshold, or deciding whether a low-VAF call is tumor versus CHIP.

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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.

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Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).

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Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.

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Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.

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Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

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Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.

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Track complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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This skill suggests using real-time continuous glucose monitoring (CGM) and algorithm-driven insulin pumps (ADIPs) rather than multiple daily injections (MDIs) with self-monitoring of blood glucose (SMBG) three or more times daily for adults and children with type 1 diabetes (T1D). Trigger phrases include "Should I use RT-CGM+ADIP or MDI+SMBG for this T1D patient?" or "Is intensive pump therapy indicated for this patient?"

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Recommends CGM over SMBG for type 1 diabetes patients on multiple daily injections to reduce hypoglycemia risk. Triggered by clinician questions such as "Should I use CGM or SMBG for this T1D patient on MDI?" or "Is CGM preferred for glucose monitoring in this patient?"

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This skill reassesses the insulin plan when discontinuing continuous subcutaneous insulin infusion (CSII) and transitioning to multiple daily injections (MDI). It is triggered by phrases such as 'Review planned basal-bolus regimen after pump' and 'Confirm MDI dose calculations and schedule'.

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Recommends use of hybrid closed-loop pump rather than insulin pump with CGM (without algorithm) or multiple daily injections with CGM for pregnant individuals with type 1 diabetes. Triggered when deciding on insulin delivery methods for pregnant patients with type 1 diabetes.

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Recommends using continuous glucose monitoring instead of fingerstick self-monitoring for type 1 diabetes patients on multiple daily injections to reduce hypoglycemia risk. Use when a clinician evaluates glucose monitoring options for T1D on MDIs or manages hypoglycemia-prone T1D patients.

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Recommends continuous subcutaneous insulin infusion (CSII) over multiple daily injections (MDI) for adults with type 1 diabetes who have achieved their A1C goal but experience severe hypoglycemia or high glucose variability, provided the patient and caregivers are willing and able to use the device. Consider CSII when clinicians hear 'A1C is at goal but patient has severe lows or glucose swings' or think 'pump for hypoglycemia-prone controlled T1DM'.

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Suggests CSII with good adherence to monitoring and dosing for adults with type 2 diabetes mellitus who have poor glycemic control despite intensive insulin therapy, oral agents, other injectable therapy, and lifestyle modifications. Trigger phrases include 'T2DM on multiple injectables still uncontrolled' and 'Consider pump for insulin-requiring T2DM failing other therapies'.

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This skill guides clinicians to reassess glucose monitoring frequency when discontinuing continuous subcutaneous insulin infusion (CSII) or transitioning to multiple daily injections (MDI). It is triggered by phrases such as 'Plan for glucose monitoring after pump stop' or 'Determine SMBG/CGM frequency post-pump' to ensure individualized glycemic goals are met.

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The skill suggests using real-time continuous glucose monitoring (CGM) combined with algorithm-driven insulin pumps (ADIPs) rather than multiple daily injections (MDIs) with self-monitoring of blood glucose (SMBG) three or more times daily for adults and children with type 1 diabetes (T1D) to lower hypoglycemia incidence. Clinical triggers include discussions about insulin pump therapy, CGM use, or reducing hypoglycemia in T1D patients on intensive insulin regimens.

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Work up Malignancy-Associated Hypercalcemia (MAH) — distinguish humoral hypercalcemia of malignancy (HHM, PTHrP-driven), local osteolytic hypercalcemia, 1,25(OH)2D-mediated lymphoma hypercalcemia, ectopic PTH secretion, and multiple myeloma — and pick the right confirmatory tests and therapy direction. Trigger when a clinician asks "is this hypercalcemia from cancer", "PTHrP positive what now", "hypercalcemia in lung cancer / breast cancer / lymphoma / myeloma", "humoral hypercalcemia of malignancy", "MAH workup", or any hypercalcemia case with suppressed PTH and a known or suspected malignancy.

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Set absolute LDL-C, non-HDL-C, and ApoB treatment goals, PLUS percent LDL-C reduction, for a primary-prevention patient on lipid-lowering therapy per the re-introduced treatment goals in the 2026 ACC/AHA multisociety dyslipidaemia guideline. Covers goals by PREVENT-ASCVD category (low / borderline / intermediate / high), by CAC score, in severe hypercholesterolaemia / FH, in diabetes with multiple ASCVD risk factors, and the ApoB targets once LDL-C and non-HDL-C goals are met. Use when a clinician asks what LDL goal to aim for, LDL target for intermediate risk, non-HDL goal, ApoB target, dual goals on statin, patient at goal or not, how low should LDL go, statin not enough what next. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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Suggests using longer-acting glucocorticoids when standard hydrocortisone regimens are impractical due to nonavailability, poor adherence, or need for once-daily dosing. Triggers include glucocorticoid replacement therapy decisions where patients report difficulty with multiple daily doses, lack of access to hydrocortisone, or preference for simplified regimens.

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