Coding & Dev Tools

ariffazil Skill

Hermes RSI worker under arifOS kernel governance. Review · Synthesize · Integrate. Use for every non-trivial request to lower entropy and emit compact, structured artifacts the kernel can judge.

ariffazil Bundle

Evaluate a proposed action against the arifOS F1-F13 constitutional floors and identify any failed gate

ariffazil Bundle

Apply the constitutional HOLD path when authority, evidence, system health, reversibility, or blast radius is insufficient

ariffazil Bundle

Discover, bind, and compose skills across all federation organs using AAA_SKILL.md orthogonal axes (Trinitarian Δ/Ω/ΦΙ + Functional). Enforces subagent contracts, evidence gates, F1-F13. Meta-skill for agentic elevation.

ariffazil Skill

Run benchmark prompts, collect pass/fail traces, latency, token cost, and false activation rates for each skill. Load when a skill changes behavior or a new version is proposed.

ariffazil Bundle

Check every skill’s trigger clauses for collisions, missing negatives, and vague verbs

gptomics Bundle

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

gptomics Bundle

Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-analysis.

gptomics Bundle

Exports publication-ready figures with the correct vector/raster split, embedded editable fonts, color-space-robust palettes, and journal-correct sizing and resolution in matplotlib and ggplot2. Use when preparing figures for journal submission, exporting a dense single-cell or GWAS plot without producing an unopenable vector file, or fixing fonts and colors that break in print.

gptomics Bundle

Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

gptomics Bundle

Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.

gptomics Bundle

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/CA-CTCF/CA-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.

gptomics Bundle

Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

gptomics Bundle

Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

oimiragieo Bundle

Rules for Drizzle ORM schema design, query patterns, migration workflows, and relational query usage. Ensures type-safe, production-ready database interactions.

oimiragieo Bundle

Load and synthesize framework architecture context for reflection and planning tasks.

oimiragieo Bundle

Google Imagen image generation via Vertex AI — text-to-image, image editing, inpainting, and upscaling using ImageGenerationModel

oimiragieo Bundle

Records atomic learned behaviors with confidence scores. Project-scoped instincts are isolated per project and auto-promote to global scope at confidence threshold 0.8. Stores instincts in .claude/context/memory/instincts.jsonl

ariffazil Bundle

Master the 6-layer memory architecture of arifOS. Use this skill whenever you need to store, recall, or organize information across Ephemeral (L1), Session (L2), Semantic (L3), Structured (L4), Relational (L5), or Immutable (L6) layers. Mandatory for maintaining "Besi-level" factual consistency and adhering to Sovereign Memory Directives.

ariffazil Bundle

High-level governance layer for pull request review across the federation Ensures separation of duties, required signers, and constitutional compliance before merge. This is the **policy layer** that decides who must approve. The **checklist** lives in `github-pr-review`; do not duplicate it here.

ariffazil Bundle

Bootstrap, design, and package new skills

ariffazil Skill

GEOX Earth Plane — Subsurface intelligence, petrophysics, well log interpretation, formation evaluation, and prospect evaluation. Use when Arif asks about well logs, LAS files, Vshale, porosity, permeability, GCoS, or geological risk assessment.

ariffazil Bundle

Detect conflicting or parallel source-of-truth claims across federation repositories and resolve the canonical owner

oimiragieo Bundle

PowerShell 7+ scripting and Windows system administration -- cross-platform automation, secure credential handling, Pester testing, DSC, and JEA patterns.

oimiragieo Bundle

Sentry error tracking and performance monitoring for real-time visibility into application errors, performance issues, and release health

oimiragieo Bundle

TypeScript and JavaScript expert including type systems, patterns, and tooling

gptomics Bundle

Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.

gptomics Bundle

Quality control for bottom-up proteomics across three levels -- instrument/raw-signal (mass accuracy, RT/iRT fit, FWHM, TIC vs injection time, % MS2 identified), identification/run (missed cleavages, charge states, PTM handling artifacts, contaminants), and experiment/quantitative (replicate correlation on log2, CV on the linear scale, completeness, MNAR-vs-MCAR missingness, PCA/batch, TMT channel balance, DIA q-values). Frames QC as a control chart against a per-instrument rolling baseline, not fixed cutoffs, and mandates inspecting raw boxplots, per-sample ID counts, total signal, and contaminant removal BEFORE normalizing -- because median normalization erases loading failures. Use when assessing proteomics data quality, diagnosing outlier samples, or deciding which samples to exclude before differential testing. The statistical test itself is differential-abundance; normalization mechanics are quantification; DIA q-value internals are dia-analysis.

gptomics Bundle

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.

gptomics Bundle

Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.

gptomics Bundle

Quality control, ambient-RNA handling, normalization, and feature selection for single-cell RNA-seq using Scanpy (Python) and Seurat (R). Use when filtering low-quality cells with MAD-adaptive thresholds, setting tissue-aware mito cutoffs, removing ambient RNA (SoupX/CellBender/DecontX), choosing a normalization (shifted-log vs scran vs sctransform vs Pearson residuals), selecting highly variable genes, or deciding whether to scale and regress out covariates.

ariffazil Skill

Build multi-step execution graphs, dependency-aware subtasks, checkpoints, and rollback points. Load when tasks exceed one-shot prompting and need subagent or staged execution.

ariffazil Bundle

Multi-harness skill catalog unity — AAA catalog, Grok/Claude/Codex views, alias table (V3 short→disk), mesh-sync, BOOT gate, Hermes bridge. Load when auditing skill mesh, resolving dual names, rebinding harness skills, or before claiming skill inventory complete.

ariffazil Bundle

Six-step incident response playbook with structured logging, backoff/circuit-breaker for restart loops, and verification-as-terminal-state. Lower machine entropy.

ariffazil Skill

WEALTH Capital Engine — NPV/EMV valuation, asset allocation, Makcik2 credit analysis, capital crisis triage, and investment decision support. Use when Arif asks about financial decisions, NPV, EMV, portfolio allocation, or economic evaluation of projects.

ariffazil Skill

Context window compression for massive log/output payloads