所有 Skills

context-compression

46Ktesting-qa

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request.

sickn33

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cc-skill-frontend-patterns

46Kfrontend

Frontend development patterns for React, Next.js, state management, performance optimization, and UI best practices.

sickn33

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architecture

46Ktesting-qa

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

sickn33

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test-driven-development

46Ktesting-qa

Use when implementing any feature or bugfix, before writing implementation code

sickn33

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unreal-engine-cpp-pro

46Ktesting-qa

Expert guide for Unreal Engine 5.x C++ development, covering UObject hygiene, performance patterns, and best practices.

sickn33

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prompt-engineering

46Kprompting-reasoning

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

sickn33

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api-patterns

46Kbackend-api

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

sickn33

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code-review-checklist

46Ktesting-qa

Comprehensive checklist for conducting thorough code reviews covering functionality, security, performance, and maintainability

sickn33

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superpowers-lab

46Ktesting-qa

Lab environment for Claude superpowers

sickn33

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gitnexus-exploring

46Ksecurity

Use when the user asks how code works, wants to understand architecture, trace execution flows, or explore unfamiliar parts of the codebase. Examples: \"How does X work?\", \"What calls this function?\", \"Show me the auth flow\"

abhigyanpatwari

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gitnexus-impact-analysis

46Kmcp-integrations

Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: \"Is it safe to change X?\", \"What depends on this?\", \"What will break?\"

abhigyanpatwari

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frontend-dev-guidelines

46Kdesign-ui

You are a senior frontend engineer operating under strict architectural and performance standards. Use when creating components or pages, adding new features, or fetching or mutating data.

sickn33

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workflow-automation

46Kbackend-api

Workflow automation is the infrastructure that makes AI agents

sickn33

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ai-agents-architect

46Kprompting-reasoning

设计并构建自主AI代理的专家。精通工具使用、记忆系统、规划策略和多代理编排。

sickn33

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interactive-portfolio

46Kdesign-ui

Expert in building portfolios that actually land jobs and clients -

sickn33

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micro-saas-launcher

46Kmarketing-seo

专注于快速推出小型、聚焦的SaaS产品的专家——独立开发者打造盈利软件的方法。涵盖创意验证、MVP开发、定价、发布策略，以及实现可持续收入。数周内交付，而非数月。

sickn33

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bulk-rnaseq

46Kresearch-knowledge

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g. "analyze my RNA-seq", "FASTQ to DESeq2", "run nf-core/rnaseq", "STAR/Salmon quantification", "build a counts matrix for DESeq2", or "go from reads to differentially expressed genes and enriched pathways". Routes between an nf-core/rnaseq (Nextflow) path and a standalone STAR/Salmon path, and covers experimental design, strandedness, and QC gates. For single-cell RNA-seq use the scanpy skill instead.

k-dense-ai

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pathway-enrichment

46Kresearch-knowledge

Run pathway and gene-set enrichment analysis on gene lists or ranked gene data, then interpret the results. Use whenever the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".

k-dense-ai

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context-window-management

46Kresearch-knowledge

Strategies for managing LLM context windows including

sickn33

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aws-serverless

46Kdevops-cloud

Specialized skill for building production-ready serverless

sickn33

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pptx-official

46Kdesign-ui

A user may ask you to create, edit, or analyze the contents of a .pptx file. A .pptx file is essentially a ZIP archive containing XML files and other resources that you can read or edit. You have different tools and workflows available for different tasks.

sickn33

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react-ui-patterns

46Kdesign-ui

Modern React UI patterns for loading states, error handling, and data fetching. Use when building UI components, handling async data, or managing UI states.

sickn33

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business-analyst

46Kmarketing-seo

Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.

sickn33

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conversation-memory

46Kresearch-knowledge

Persistent memory systems for LLM conversations including

sickn33

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