402 skills · sorted by relevance
Selects the aldosterone-to-renin ratio (ARR) method over hypokalemia for screening hypertensive patients for primary aldosteronism. Use when a clinician asks what test to use for screening hypertensive patients with resistant hypertension or unexplained hypokalemia for PA.
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Suggests periodic screening for autoimmune diseases known to be more prevalent in PAI patients in whom autoimmune origin of PAI has not been excluded; optimal frequency unknown but can be done annually. Use when determining screening schedule for PAI patient with uncertain autoimmune origin.
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Reads one export transaction, order or shipment description and returns a DRAFT export review pack: parties to screen with a screening checklist, an item classification worksheet, a red-flag review, licence-determination questions and open items. Use when the user asks to "prepare the export review for this order", "pre-check this shipment for export control", "who do we need to screen on this deal", "build the classification worksheet for this item" or "assemble the trade-compliance file for this technology transfer". Do not use for vendor security or due-diligence screening of a supplier's questionnaire answers; use vendor-risk-screening-brief instead. Never classifies, screens, clears, decides licence need or releases a shipment; trade compliance does. Drafts for human review; never approves, authorises or signs off.
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Prepares a DRAFT vendor risk screening brief from the material the user provides on one vendor (questionnaire answers, certificates, assurance reports, policies, contract extracts): per screening topic, what a document evidences with reference, date and scope, what is only asserted, what is missing or contradicted, certificate and report details as stated, and ready-to-send questions for the vendor and the internal owners. No web research, no risk rating, tier, score or recommendation. Use when the user asks to "screen this vendor", "review the vendor's questionnaire answers", "check what the vendor's certificate covers", "what is missing from this due diligence pack" or "draft the follow-up questions for the vendor". Do not use for answering a customer's questionnaire about your own organisation, use vendor-security-questionnaire-prefill instead. Drafts for human review; never approves, authorises or signs off.
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Support import and export compliance by classifying goods under tariff codes, reviewing customs documents, and assessing restricted-party matches. Covers trade agreement eligibility, duty-saving options, and customs inquiries across the US, EU, UK, and Asia-Pacific.
Plan and write literature reviews using multiple scholarly databases. Document searches, screen papers, synthesize findings, and check citations, then prepare Markdown or PDF reports with transparent selection counts, evidence limitations, and reproducible search records.
Find stock investment candidates by combining financial filters with research into investment themes. Produce five to ten long or short ideas, each with a rationale and risks, plus a comparison table and priorities for further research.
Screen dependency names on npm, PyPI, and crates.io before installation to find lookalikes of legitimate packages. This skill compares spelling patterns and registry metadata, helping review AI-generated lists, lockfile changes, and new dependencies in CI/CD.
Build a screening pipeline for web pages, PDFs, and images before an AI agent reads them. Extract hidden text, check for malicious instructions with detection models, and record decisions to block or allow content.
Add checks to LLM prompts and responses using Llama Guard 3, NeMo Guardrails, and LLM Guard. Configure content screening, conversation boundaries, and sensitive-data filtering, then measure blocking and false-positive rates with labeled test prompts.
Screen documents and external URLs before they enter a processing workflow. Check blocked domains and document permissions, then produce a pass or reject decision, a risk report, and a tracking ID for later review.
Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.
Screen incoming nondisclosure agreements against a supplied playbook or default criteria. Receive a three-level classification, a summary of problematic clauses, suggested fixes, and recommended next steps for approval or legal review.
Research Guardian - Ethics Advisory & Bias Detection across all research stages Enhanced VS 3-Phase process: Surface-level screening, deep contextual analysis, constructive recommendations Use when: reviewing research ethics, checking for bias, assessing trustworthiness, QRP screening Triggers: ethics review, IRB, bias detection, QRP, trustworthiness, research integrity, p-hacking, HARKing
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Collaboratively build and refine paper screening rubrics through brainstorming, test-driven development, and iterative feedback
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NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
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Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.
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Turn Japanese or English stock screening requests into FinViz URLs and open them in a browser. Combine dividend, growth, price movement, and investment theme filters, with options to choose the results view and sorting order.
Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.
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Builds clear, executable, and auditable inclusion and exclusion criteria for biomedical and clinical research protocols. Always use this skill when a user needs to translate a target population into operational screening rules tied to chart fields, time windows, tests, procedures, prior therapies, exclusions, and reviewable edge cases. Focus on protocol-stage precision, ambiguity reduction, auditability, and screening reproducibility rather than generic study design advice.
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Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool. Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning. Requires real and verifiable literature records only. Every formal literature item must include a real link and DOI when available; never fabricate citations, titles, authors, years, journals, abstracts, PMIDs, or DOIs. If a DOI is unavailable or cannot be verified, state that explicitly rather than inventing one.
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Spot a bad client before you sign — the warning signs of the projects that turn into unpaid, scope-creeping nightmares, and how to screen, price, or decline them. Use when asked is this client a red flag, should I take this client, screening a difficult client, or how to avoid bad clients. Produces a read on the warning signs present (haggling, vagueness, disrespect, urgency, unrealistic expectations), what each predicts, screening questions to ask before committing, protective terms if you proceed anyway, and how to decline gracefully — so you avoid the clients who cost more than they pay.
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Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.
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Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.
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