scanpy-complete
Scanpy 单细胞分析工具包 - 100%覆盖文档（API+教程+预处理+分析+可视化）
sc-best-practices-complete-100percent
单细胞分析最佳实践集合 - 100%完整覆盖（410个文件：407个HTML文档+3个项目文档）
remote-server-executor
Remote SSH server management and code execution with paramiko-based performance optimization
rapids-singlecell-complete
RAPIDS Single-Cell GPU 文档完整镜像
envi-pkg-local-improved
ENVI spatial transcriptomics analysis toolkit - comprehensive documentation with tutorials and Python source code
palantir-docs-local
Palantir 本地文档（en/latest）
osta-docs-local
OSTA 本地文档（OSTA/）
banksy-merged-v3
BANKSY spatial transcriptomics analysis tool - complete documentation with notebooks and source code
organ-axis-complete
Organ Axis tutorial docs - 100%覆盖文档（模型应用+公式+注释采样+教程)
archr-local
ArchR docs served from downloaded_docs/archr_scrape - comprehensive scATAC-seq analysis toolkit with all HTML files explicitly listed
nichecompass-docs-local
NicheCompass 本地文档（en/latest）
mudata-complete
MuData 多模态数据分析工具包 - 100%覆盖文档（API+教程+IO指南+核心功能）
monocle3-truly-complete
Monocle3 单细胞轨迹分析工具包 - 100%覆盖文档（18个文件：完整教程+API+轨迹推断）
liana-py-complete
Liana-py 细胞互作分析工具包 - 100%覆盖文档（60个核心文件+72个图片文件）
crewai
When to use this skill: When you need help with CrewAI - building collaborative AI agents, crews, and workflows. Use for agent orchestration, task automation, multi-agent systems, flow management, and enterprise AI automation.
developing-heart-local
Developing Heart Atlas notebooks converted to HTML (local)
scanpy-single-cell-analysis
Python-based single cell analysis using Scanpy for .h5ad files in conda scanpy environment
giottosuite-local
Giotto Suite 空间转录组学分析工具包 - 100%覆盖718个文件（37个教程+285个核心API+4个数据文档+344个扩展函数+49个工具函数）
envi-pkg-local
ENVI 本地文档+教程+源码 - 100%覆盖13个文件（Sphinx build + 手动转换 notebooks/py）
scarches-docs-complete
scArches 文档本地镜像全量
scarches-complete
scArches 单细胞深度学习参考图谱框架 - 100%覆盖文档（26个HTML文件，包含完整API、教程、模型训练、多模态整合）
cellphonedb
Comprehensive skill for CellPhoneDB - Database of cell type markers and cell-cell communication analysis for single-cell data. Use for cell type annotation, ligand-receptor analysis, cell-cell interaction inference, and communication network visualization.
developing-heart-local-improved
Developing Heart Atlas - comprehensive cardiac development analysis with spatial multi-omics integration
visualize-parsed-spatial-omics-metadata
Create an interactive HTML viewer to visualize and verify parsed metadata against source documents.
agent-memory
Persistent memory system for AI agents with semantic, episodic, and procedural memory types. Use when users want to (1) remember facts, preferences, or context across sessions, (2) track interaction history and experiences, (3) store reusable workflows or procedures, (4) build personalized agents that learn from conversations, or (5) implement any form of long-term memory for AI applications.
ReasoningBank Intelligence
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
AgentDB Learning Plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
AgentDB Memory Patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
AgentDB Performance Optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
backend-dev-guidelines
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equilateral-agents-refactored
Multi-agent orchestration system sử dụng Claude subagents thực tế từ thư mục agents/ cho security reviews, code quality analysis, deployment validation, infrastructure checks. Auto-activates với orchestrator-worker pattern và extended thinking mode.
error-tracking
Add Sentry v8 error tracking and performance monitoring to your project services. Use this skill when adding error handling, creating new controllers, instrumenting cron jobs, or tracking database performance. ALL ERRORS MUST BE CAPTURED TO SENTRY - no exceptions.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
ReasoningBank with AgentDB
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
repomix
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
AgentDB Advanced Features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
writing-plans
Create detailed implementation plans with bite-sized tasks for engineers with zero codebase context. Use this when design is complete and you need detailed implementation tasks for engineers with zero codebase context
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first
subagent-driven-development
Use when executing implementation plans with independent tasks in the current session - dispatches fresh subagent for each task with code review between tasks, enabling fast iteration with quality gates
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
playwright
Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.
extract-learnings
Use after completing a session to identify genuine learnings from mistakes, corrections, or rework - focuses only on patterns that were actually wrong, not things that worked correctly the first time
using-git-worktree
Create isolated git worktrees at bare repo root level
setup-assistant
Detect when user is working in an un-indexed project and proactively suggest enabling semantic memory. Activates on first code question in new projects to guide users through initial setup.
semantic-search
Automatically search indexed code when user asks questions about the codebase. Detects code-related queries and uses semantic memory to find relevant files. Activates for questions like "how does X work", "where is Y", "show me Z implementation".
cursor-explorer-mcp
Use for token-expensive operations requiring multi-file analysis - codebase exploration, broad searches, architecture understanding, tracing flows, finding implementations across files. Uses MCP cursor-agent server (company pays) with clean async interface. Do NOT use for single-file analysis, explaining code already in immediate context, or pure reasoning tasks.
yanex-experiment-tracking
Use this skill when running, managing, or analyzing yanex experiments. Includes executing experiments via CLI, parameter sweeps, dependencies, querying experiment history, comparing results, and maintaining experiment logs. Invoke when users mention yanex, experiments, training runs, parameter sweeps, or need to track ML experiments.
cip-document-generation
Generate professional contract analysis reports (.docx) for the Contract Intelligence Platform. Three report types: (1) Contract Risk Review - initial analysis with risk ratings, heat map, clause analysis, negotiation playbook; (2) Suggested Redlines and Revisions - proposed changes with before/after risk matrix, implementation notes, negotiation guide; (3) Version Comparison - compare two contract versions with delta analysis and grouped comparison tables. Use when user requests contract risk analysis, redline suggestions, or version comparison reports.
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