productivity▌
6,493 indexed skills · max 10 per page
tooluniverse-gwas-snp-interpretation
mims-harvard/tooluniverse · Productivity
SNP interpretation: a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Fine-mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mappin
json-data-handling
bobmatnyc/claude-mpm-skills · Productivity
Working effectively with JSON data structures.
tooluniverse-immune-repertoire-analysis
mims-harvard/tooluniverse · Productivity
Comprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.
nemoclaw-setup
jezweb/claude-skills · Productivity
nemoclaw-setup
ljg-rank
lijigang/ljg-skills · Productivity
降秩引擎 \n 输入一个领域,输出它的秩。 \n 秩是什么 \n 秩不是\"关键要素\",不是\"核心原则\",不是\"总结要点\"。 \n 秩是:这个领域里真正独立的生成器有几个?用它们能反向生成全部现象?能,才算找到。 \n 四个判据 \n 这四条全过,秩才立。任何一条失败,推倒重来。 \n \n 生成性 ——用生成器能把每个观察到的现象推回来。一个都不能漏。 \n 最小性 ——关掉任何一个生成器,就有现象解释不了。没有冗余。 \n 独立性 ——每对生成器能找到真实案例:一个变了另一个没变。 \n 预测力 ——用生成器能推导出原始清单之外的现象,且现实中确实存在。 \n \n 怎么写 \n 写一篇散文。不是填一张表。 \n 你的任务是带着读者走一段路:从\"这个领域看着挺乱\"走到\"原来就这两三根线在牵\"。这段路怎么走,你自己决定。没有规定的章节、没有规定的格式、没有规定的小标题。 \n 唯一的要求是三条: \n \n 想一口气读完 ——不了解这个领域的人也停不下来 \n 记得住 ——读完能转身跟朋友用一句话说清楚 \n 有落差 ——从混沌到极简的反差,就是降秩的美感 \n \
triage
readwiseio/readwise-skills · Productivity
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data-analyst
404kidwiz/claude-supercode-skills · Productivity
Provides business intelligence and data analysis expertise specializing in SQL, dashboard design, and metric-driven insights. Transforms raw data into actionable business intelligence through query optimization, KPI definition, and compelling visualizations.
autonomous-agent-gaming
qodex-ai/ai-agent-skills · Productivity
Build sophisticated game-playing agents that learn strategies, adapt to opponents, and master complex games through AI and reinforcement learning.
protein-interaction-network-analysis
mims-harvard/tooluniverse · Productivity
Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.
monitoring-observability
yonatangross/orchestkit · Productivity
Comprehensive patterns for infrastructure monitoring, LLM observability, and quality drift detection. Each category has individual rule files in rules/ loaded on-demand.