Coding & Dev Tools

gptomics Bundle

Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

gptomics Bundle

Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.

gptomics Bundle

Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

gptomics Bundle

Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.

gptomics Bundle

Cleans a shotgun metagenome of everything that is not the target community before profiling - host-read depletion (Hostile, bowtie2/T2T-CHM13), reagent/kitome contamination control with blanks and decontam, mock-community validation, and depth-adequacy checks (Nonpareil). Covers why a metagenomic result is a position in a choice-chain rather than a direct observation, why extraction is the experiment, why a low-biomass community can be entirely kitome, why absence means not-detectable-by-this-chain, and why a confident classifier call can still be wrong when the reference is contaminated. Use when designing controls, removing host reads, identifying reagent contaminants, validating with mocks, or judging whether a low-biomass result is real. For adapter/quality trimming see read-qc; for MAG-level decontamination see genome-assembly/metagenome-assembly.

gptomics Bundle

Selects and prepares the reference panel that phasing/imputation copies haplotypes from (1000 Genomes, HRC, TOPMed, HGDP+1kGP/gnomAD, CAAPA), matching panel ancestry to the target, reconciling genome build and chromosome naming, and running the strand/allele harmonization gate. Covers why ancestry-match beats panel size (imputation can only copy haplotypes the panel contains), why palindromic A/T and C/G SNPs flip strand without erroring, why liftover is a strand-flip generator in between-build inverted regions, that HRC is SNP-only and TOPMed is never downloadable (governance can override accuracy), and panel formats (msav, bref3, imp5). Use when choosing a panel for a target ancestry, preparing or converting a panel, aligning study data, or deciding between downloadable and server-only panels. Phasing is haplotype-phasing; imputation is genotype-imputation; PCA for ancestry is population-genetics/population-structure; HLA panels are clinical-databases/hla-typing.

gptomics Bundle

Generate consensus FASTA sequences by applying VCF variants onto a reference with bcftools consensus, or build viral/amplicon consensus with iVar. Use when reconstructing a sample-specific reference or haplotype, deciding -H haplotype vs IUPAC vs all-ALT projection, masking no-coverage sites so a consensus does not manufacture false reference calls, or setting iVar min-depth/min-frequency policy for surveillance genomes.

gptomics Bundle

Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

gptomics Bundle

Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.

gptomics Bundle

Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.

sboghossian-mini-claude-for-legal Skill

Use when a user wants to draft or negotiate a Master Services Agreement (MSA) and Claude must gather the commercial, IP, liability, and data-protection inputs before generating the document. Triggers on requests to prepare a framework services contract, master agreement, or any umbrella commercial arrangement that will govern future statements of work. Covers multi-jurisdictional use cases (UAE, DIFC, KSA, LB, EU, UK, US) with attention to GDPR / PDPL data-processing addendum requirements.

sboghossian-mini-claude-for-legal Skill

Use when a user wants to draft a non-disclosure agreement (NDA) or confidentiality agreement and Claude must gather the five minimum inputs before generating the document. Triggers on any request to prepare an NDA, mutual NDA, one-way confidentiality agreement, or CDA. Applies across all jurisdictions (LB, UAE, DIFC, KSA, UK, US-DE, FR). Designed for efficient single-turn intake with intelligent defaults. Routes to draft-nda-mutual or draft-nda-unilateral.

sboghossian-mini-claude-for-legal Skill

Use when asked to draft an Asset Purchase Agreement (APA) for an M&A transaction structured as an asset deal rather than a share deal. Covers the asset/liability split mechanics, purchase price allocation, contract assignment, employee transfer (TUPE/ARD and MENA equivalents), IP recordation, and jurisdictional nuances for UAE, KSA, Lebanon, DIFC, ADGM, EU, and US. P0 priority — asset deal structure has major tax and liability consequences.

sboghossian-mini-claude-for-legal Skill

Use when drafting a share purchase agreement (SPA) for a sale of shares in a private company, whether in a simple bilateral transaction or a structured M&A deal with earn-outs, escrow, and multiple parties. Covers the full SPA structure including representations and warranties, indemnification caps and baskets, conditions precedent, pre-closing covenants, and tax provisions. Addresses MENA-specific considerations including foreign ownership rules, regulatory approvals, and civil-law vs common-law warranty regime differences.

sboghossian-mini-claude-for-legal Skill

Use when scoring the Arabic language quality of AI legal outputs. A 0–5 rubric covering grammatical correctness, legal register, terminology precision, script directionality, and dialect appropriateness for MENA legal practice. Arabic legal output requires MSA with correct technical terminology — not informal Arabic or transliterated loanwords.

sboghossian-mini-claude-for-legal Skill

Use when scoring the English language quality of AI legal outputs. A 0–5 rubric covering grammatical correctness, legal register, precision of expression, clarity for the intended audience (professional vs client-facing), and avoidance of verbose filler. Applies to outputs in English across all jurisdictions.

sboghossian-mini-claude-for-legal Skill

Use when scanning a contract for clauses that deviate materially from market norms for that document type and jurisdiction. Identifies atypical provisions — including below-market liability caps, IP ownership reversals, disproportionate termination fees, retroactive most-favored-customer clauses, currency-risk shifts, and non-standard arbitration seat selection — and contrasts them with market-standard alternatives. Severity-rated output with a caution against treating atypical as automatically wrong.

sboghossian-mini-claude-for-legal Skill

Use to classify the primary legal practice area of an incoming request from a defined 22-label taxonomy. Labels include corporate, ip, employment, real-estate, m-and-a, litigation, data-privacy, arbitration, shariah-finance, and others. Uses keyword signals for high-confidence classification, falls back to admin with a clarifying-question trigger at confidence below 0.50. Output consumed by skill selection, persona selection, and knowledge retrieval routing.

autohandai-community-skills Bundle

AI-assisted label triage for NVIDIA/NemoClaw issues and PRs. Reads triage-instructions.md at runtime for consistent label guidance. Supports single-item mode (give it a number) and batch mode (fetches up to 50 unlabeled open items). On approval, applies labels and an optional triage comment via gh CLI, then logs the session to the daily-rhythm activity folder. Trigger keywords - triage, label issues, suggest labels, batch triage, triage issue, triage PR, label this, what labels.

autohandai-community-skills Bundle

Malware IOC extraction is the process of analyzing malicious software to identify actionable indicators of compromise including file hashes, network indicators (C2 domains, IP addresses, URLs), regist

autohandai-community-skills Bundle

Test web applications for HTTP Host header injection vulnerabilities to identify password reset poisoning, web cache poisoning, SSRF, and virtual host routing manipulation risks.

gptomics Bundle

Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.

gptomics Bundle

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.

gptomics Bundle

Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

gptomics Bundle

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

gptomics Bundle

Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

gptomics Bundle

Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.

gptomics Bundle

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

gptomics Bundle

Infers metabolite-mediated cell-cell communication from scRNA-seq by scoring enzyme-to-sensor pairs (MEBOCOST), with metabolic flux (scFEA), FBA state (Compass), and neurotransmitter (NeuronChat) alternatives. Use when studying metabolic crosstalk between cell types, predicting metabolite secretion and sensing, or deciding which metabolic-communication method fits and how speculative the result is.

freedomintelligence Bundle

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.

freedomintelligence Bundle

Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.

freedomintelligence Bundle

Call SNPs and indels from aligned reads using bcftools mpileup and call. Use when detecting variants from BAM files or generating VCF from alignments.

freedomintelligence Bundle

Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.

freedomintelligence Bundle

Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function.

freedomintelligence Bundle

Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.

freedomintelligence Bundle

Searches molecular libraries for substructure matches using SMARTS patterns with RDKit. Filters compounds by pharmacophore features, functional groups, or scaffold matches with atom mapping. Use when finding compounds containing specific chemical moieties or filtering libraries by structural features.