AI导哈

营销与写作

pathogen-variant-surveillance

k-dense-ai/scie…t-skills

Queries public GenSpectrum LAPIS data for pathogen genomic surveillance, current lineage nomenclature, weekly sequence proportions, reporting delays, and descriptive mutation frequencies. Use for variant surveillance, Pango lineage validation, dominant submitted lineages, Nextclade assignment provenance, SARS-CoV-2, influenza/H5N1 clades, RSV, mpox, measles, dengue, or LAPIS queries. Distinguishes sequence prevalence from infection prevalence, clades from genotypes, missing calls from reference matches, and sampling changes from biological growth advantage.

  • 978 次安装
  • 5.5万 星标
  • 收录于

安装命令

npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill pathogen-variant-surveillance

SKILL.md

Pathogen Variant Surveillance

When to use

Use current data when a question depends on which lineages appear in submitted sequences, what a lineage name currently means, or how the submitted sequence distribution changed. Never answer a current circulation question from remembered lineage names or old examples.

This skill supports descriptive surveillance research. Counts describe sequences submitted to one database under stated filters; they are not case counts, infection prevalence, clinical interpretations, outbreak recommendations, or evidence of enhanced pathogen function.

Verified scope

Reviewed on 2026-10-01 against official documentation, live schemas for all 15 registered instances, and small public queries. SARS-CoV-2 served LAPIS 0.8.7/SILO 0.14.3; the other registered deployments served LAPIS 0.8.0/SILO 0.11.0. Do not assume identical feature support. Bundled standard-library scripts have synthetic regression tests and bounded live smoke checks. Nextclade, GenoFLU, authenticated APIs, and sequence-level assay validation are not executed here.

相关 Skills

scientific-writingk-dense-ai/scie…t-skillsDrafts, revises, and audits scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.营销与写作2480scientific-visualizationk-dense-ai/scie…t-skillsCreates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.数据与研究2447scientific-critical-thinkingk-dense-ai/scie…t-skillsEvaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.测试与安全2404literature-reviewk-dense-ai/scie…t-skillsConducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.测试与安全2382scientific-brainstormingk-dense-ai/scie…t-skillsFacilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.智能体工作流2335statistical-analysisk-dense-ai/scie…t-skillsGuided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.数据与研究2318