Coding & Dev Tools

sboghossian-mini-claude-for-legal Skill

Use when collecting Net Promoter Score (NPS) feedback within a legal AI chat session. Defines the trigger conditions (10th successful turn, major milestone, quarterly heartbeat), the in-chat survey flow (0–10 scale + open-ended follow-up), the output schema, and the downstream routing — detractors to the churn risk detector, promoters to the case-study asker.

sboghossian-mini-claude-for-legal Skill

Use when structuring a legal memo or analytical response using the CREAC framework (Conclusion → Rule → Explanation → Application → Conclusion). CREAC leads with the bottom line, making it the preferred structure for partner-facing and client-facing legal memos where the reader wants the answer first. Includes MENA-jurisdiction examples (UAE non-compete, DIFC contract law) and a comparison with IRAC.

sboghossian-mini-claude-for-legal Skill

Use when a contract needs a completeness check against the standard skeleton for its document type. Identifies absent mandatory, strongly recommended, and optional clauses across NDAs, MSAs, leases, employment agreements, and other common commercial instruments. Flags jurisdiction-specific mandatory provisions and links to drafting skills for remediation. Suitable as a first-pass quality gate before any detailed clause-by-clause review.

sboghossian-mini-claude-for-legal Skill

Use when a fast (5-minute) NDA review is needed to assess whether an NDA is safe to sign, needs negotiation, or should be rejected as drafted. Runs a 10-point checklist covering mutual vs unilateral structure, Confidential Information definition, permitted recipients, term, return/destruction, no-license language, remedies, governing law, jurisdiction-specific execution requirements, and boilerplate. Produces a traffic-light rating and links to counter-proposal drafting.

sboghossian-mini-claude-for-legal Skill

Use when a lawyer or legal professional has dictated text via voice-to-text (STT) and the raw transcript needs to be cleaned into polished, formal written output. This skill governs filler removal, STT error correction, punctuation reconstruction, defined-term formatting, and the strict constraints on what must not be changed — preserving legal substance and numeric precision exactly while removing transcription artifacts.

gptomics Bundle

Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.

gptomics Bundle

Validates chosen PCR/qPCR oligos for intramolecular thermodynamic liabilities with primer3-py - hairpins, self-dimers, cross-dimers (calc_hairpin/homodimer/heterodimer), and 3'-end stability (calc_end_stability) - returning ThermoResult dG/Tm and ASCII structures. Covers why a "dimer-free" verdict is a PREDICTION at the supplied salt/Mg/dNTP/oligo conditions and temp_c (so the same primer is fine or dimer-prone depending on conditions), why a 3'-END dimer or hairpin is the lethal class (polymerase-extendable into primer-dimer) so structures are ranked by dG at the annealing temperature and 3'-end involvement rather than global Tm, that ThermoResult dG is in cal/mol not kcal/mol, and that .structure_found must gate the numbers. Use when checking primer pairs before ordering, troubleshooting primer-dimers or smears, or screening oligos for secondary structure. Genome off-target/mispriming is primer-specificity; design is primer-basics; probe assays are qpcr-primers.

gptomics Bundle

Aligns RNA-seq reads to a genome with HISAT2, the splice-aware aligner whose hierarchical graph FM-index runs at roughly a quarter of STAR's memory (~7 GB for human), whose SNP/haplotype graph index reduces reference bias in the index itself, and whose MAPQ is GATK-friendly (60 for unique, no 255 problem). Use when RNA alignment must fit a memory-constrained machine, when feeding StringTie/Cufflinks transcript assembly via --dta, or when a SNP-aware graph index is wanted for allele-robust mapping. Feature-rich/high-RAM RNA alignment and fusion detection are star-alignment; DE on known transcripts only should skip alignment for rna-quantification/alignment-free-quant; the QC gate and contig-naming reconciliation are alignment-files; counting is rna-quantification.

gptomics Bundle

Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from buffering, or choosing between riborex, Xtail, anota2seq, and DESeq2 interaction models.

gptomics Bundle

Processes experimental RNA structure probing data (SHAPE-MaP, DMS-MaPseq) into per-nucleotide reactivity profiles with ShapeMapper2, then uses them as soft restraints on thermodynamic folding. Covers reagent and readout choice (SHAPE vs DMS, mutational-profiling vs RT-stop), the three control samples, per-transcript normalization, the Deigan vs Zarringhalam pseudo-energy models, in-cell versus in-vitro interpretation, and multi-conformation deconvolution. Use when converting probing reads to reactivities; deciding SHAPE versus DMS parameters; judging whether low reactivity means base-paired or protein-bound; or detecting whether an RNA populates more than one structure.

gptomics Bundle

Calculate assembly and sequence statistics (N50/L50, auN, NG50/NGA50, length distribution, GC content with ambiguity handling, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing genome assemblies.

gptomics Bundle

Predicts and prioritizes miRNA target genes with seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR). Use when deciding that a predicted target is a hypothesis not a finding; ranking by the right score (weighted context++, mirSVR, miRDB); raising confidence by intersecting predictions with inversely-correlated mRNA DE; weighing validated (CLIP/reporter) over predicted evidence; or avoiding the circular enrichment of unfiltered target lists.

intent-solutions-io Bundle

Incorporates external variables (holidays, weather, events) into TimeGPT forecasts to improve accuracy. Use when forecasts require external data, holidays impact sales, or weather affects demand. Trigger with 'include holidays', 'add weather data', 'integrate events'.

intent-solutions-io Bundle

Transform prediction market data to Nixtla format (unique_id, ds, y). Use when preparing datasets for forecasting. Trigger with 'convert to Nixtla format' or 'schema mapping'.

intent-solutions-io Bundle

Analyze forecast uncertainty using conformal prediction. Use when risk assessment or scenario planning is required. Trigger with 'quantify uncertainty' or 'prediction intervals'.

intent-solutions-io Bundle

Lists all installed Nixtla Skills and provides guidance on which skill to use for specific tasks. Scans skills directory, reads frontmatter, outputs formatted index with categories and usage recommendations. Activates when user wants to see available skills, needs guidance on skill selection, or asks about Nixtla capabilities.

oimiragieo Bundle

Validate whether a proposed new artifact is feasible in the current stack before creator workflows run.

oimiragieo Bundle

5-level error recovery escalation: retry, nudge, replan, fallback, force-done — each with entry criteria and timeout before escalating

oimiragieo Bundle

Defines the structure and implementation of service classes, enforcing the use of interfaces, ServiceImpl classes, DTOs for data transfer, and transactional management.

oimiragieo Skill

Memory index discipline — cap enforcement, pruning, archival, health reporting

oimiragieo Skill

Implements hierarchical routing architecture (sub-routers, routing tables, hook updates)

oimiragieo Bundle

Context-Driven Development methodology - treating project context as managed artifacts alongside code for consistent AI interactions and team alignment.

oimiragieo Bundle

Applies general rules for Convex development, emphasizing schema design, validator usage, index-first query patterns, function registration, and correct handling of system fields.

oimiragieo Bundle

Create structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use when building runbooks, responding to incidents, or establishing incident response procedures.

oimiragieo Bundle

General rules pertaining to Mobile UI development. Covers UI/UX best practices, state management, and navigation patterns.

oimiragieo Bundle

PyQt6 desktop GUI development rules -- signal/slot architecture, QSS theming, QThread concurrency, layout management, and cross-platform rendering. Enforces MVC separation and responsive UI patterns.

gptomics Bundle

Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB. Use when choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDB's ~62-chain / 99,999-atom limits; when residue numbers do not match the paper because of auth_* vs label_* numbering (MMCIFParser defaults auth_residues=True); when metadata (resolution, method, R-free) is missing because Bio.PDB drops it and MMCIF2Dict is needed; when the deposited coordinates are the asymmetric unit and the biological assembly must be downloaded separately; when downloading from RCSB (files.rcsb.org, PDBList); and when a legacy MMTF path is dead (RCSB retired MMTF July 2024, use BinaryCIF).

gptomics Bundle

Designs covalent inhibitors and warheads targeting cysteine, lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.

gptomics Bundle

Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.

gptomics Bundle

Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.

gptomics Bundle

Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker. Use when stratifying ICI eligibility (Le 2015), pairing MSI with TMB-H (Sha 2020 / Salem 2018), screening Lynch syndrome (universal IHC + MSI), or distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.

gptomics Bundle

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

gptomics Bundle

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

gptomics Bundle

Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.

gptomics Bundle

Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for counting, and how the estimate becomes the NG50 denominator, the Flye -g value, the hifiasm --hom-cov/purge setting, and the 1.5-2x-too-big haplotig sanity check. Use when starting any de novo assembly, deciding whether short reads can work, estimating genome size for an unknown organism, diagnosing ploidy, or sanity-checking an assembly's size against expectation.

intent-solutions-io Bundle

Assists users in migrating their codebase and data pipelines from TimeGPT-1 to TimeGPT-2. Use when upgrading to the latest version of TimeGPT, ensuring compatibility, and optimizing performance. Trigger with "migrate to TimeGPT-2", "upgrade TimeGPT", "TimeGPT compatibility".