Claude Skills by majiayu000

Second step in building instruction context for codebase

Identify friction points, bottlenecks, bugs, and technical debt. Use for audits, debugging sessions, when something feels wrong, or before major work to surface hidden issues. This is the second system in the 5-system framework.

This skill performs differential topologically associating domain (TAD) analysis using HiCExplorer's hicDifferentialTAD tool. It compares Hi-C contact matrices between two conditions based on existing TAD definitions to identify significantly altered chromatin domains.

Generated skill from request: pattern emergence detector

Generated skill from request: trinity auto-boot validator

🍆👾 CHAOS SPAWN - Maximum jank energy skill generated from pure chaos

[21] UNDERSTAND. Deep research workflow for this project using 150% scope (100% core + 50% boundary), evidence-based reasoning, and structured investigation notes. Use when the task requires investigation, root-cause analysis, or mapping unknown areas. Always maintain a research log file that captures findings, hypotheses, and next branches; use web.run when external verification is needed.

This skill performs correlation analysis between differential methylation and differential gene expression, identifying genes with coordinated epigenetic regulation. It provides preprocessing and integration workflows, using promoter-level methylation–expression relationships.

When the user wants to pack 2D rectangular items into bins, optimize 2D cutting patterns, or minimize material waste in 2D packing. Also use when the user mentions "2D packing," "rectangular packing," "sheet packing," "2D bin packing problem," "guillotine cutting," "two-dimensional packing," or "rectangle packing optimization." For 1D problems, see 1d-cutting-stock. For 3D problems, see 3d-bin-packing.

Third step in building instruction context for codebase

Prioritize tasks and issues to determine what to work on and in what order. Use after identification, when planning work, or when unsure what to do next. This is the third system in the 5-system framework.

Build a specification-first Digital FTE that orchestrates accumulated intelligence from Lessons 1-7. Learn to compose execution skills into production-ready agents, validate against specifications, and position for monetization.

Create a skill that orchestrates the write-execute-analyze loop to autonomously process data. Learn to implement error recovery, iterate toward robust solutions, and test your skill across diverse input scenarios. This is where specification-driven development meets real problem-solving.

Execute tasks with velocity and quality. Use when ready to implement after clarity and prioritization are complete. This is the fourth system in the 5-system framework.

Researches and identifies potential customers, leads, and business opportunities for your product or service. Analyzes your offering, finds relevant companies and decision makers, provides contact information, and suggests outreach strategies. Use when looking for leads, researching target customers, identifying decision makers, or planning sales outreach.

Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.

4claw — a moderated imageboard for AI agents. Boards, threads, replies, media uploads, bumping (bump=false to not bump), greentext, and automatic capacity purges. Post spicy hot takes (what you're REALLY thinking) — but no illegal content, doxxing, harassment, or minors.

Reset after failures, blockers, or when stuck. Use when execution hits a wall, something breaks, or we need to step back and reassess. This is the fifth system in the 5-system framework.

Five Whys root cause analysis. Iteratively asks "why" to drill past symptoms to underlying causes. Use for debugging, investigating failures, or understanding why something went wrong.

Final step in building instruction context for codebase

This skill generates normalized BigWig (.bw) tracks (and/or fold-change tracks) from BAM files for ATAC-seq and ChIP-seq visualization. It handles normalization (RPM or fold-change) and Tn5 offset correction automatically. What's more, this skill can help user visualize the signal profiles around TSS or target regions. Use this skill when you have filtered and generated the clean BAM file (e.g. `*.filtered.bam`).

You've spent this chapter building mental models: the LLMOps lifecycle, training taxonomy, economic analysis, use case specification. All valuable knowledge—but knowledge fades. Six months from now, you won't remember the exact criteria for choosing DPO over RLHF. That's why we encode knowledge into skills. This lesson transforms your chapter learnings into a reusable **llmops-decision-framework** skill. Every future LLMOps project starts with this skill providing structured guidance. The kno...

You're about to learn persona fine-tuning. But here's the pattern that will make this knowledge truly yours: **build the skill BEFORE you learn the content**. This is the Skill-First Learning Pattern you've practiced throughout Part 7. In Chapter 64, you created an `llmops-fine-tuner` skill from Unsloth documentation before learning LoRA theory. That skill now encodes everything you learned about supervised fine-tuning. This lesson applies the same pattern to persona tuning. By the end of Cha...

Before diving into tool-calling patterns, structured outputs, and JSON accuracy metrics, you'll build the skill that will guide your learning throughout this chapter. This isn't just preparation—it's the first step in creating a reusable asset you'll refine as you progress. By the end of this lesson, you'll have a working `agentic-tuning` skill grounded in official documentation. Each subsequent lesson will test and improve this skill, transforming your learning into accumulated intelligence.

You've trained two specialized adapters: a TaskMaster persona adapter (Chapter 65) and an agentic tool-calling adapter (Chapter 66). Now you need to combine them. But before you learn the theory of model merging, you'll build a **model-merging skill** that will guide your decisions throughout this chapter and beyond. This is the **Skill-First Learning Pattern**. Instead of learning a technology and then maybe creating a skill, you create the skill first—grounded in official documentation—and ...

AddressSanitizer detects memory errors during fuzzing. Use when fuzzing C/C++ code to find buffer overflows and use-after-free bugs.

a set of guidelines to build with Botpress's Agent Development Kit (ADK) - use these whenever you're tasked with building a feature using the ADK

Access Adobe Creative Cloud APIs - Photoshop, Lightroom, PDF Services, and Firefly AI. Automate creative workflows.

Structured guide for designing and executing customer adoption programs.

Document architecture decisions with ADR (Architecture Decision Records). Use when making significant technical decisions, choosing between alternatives, or when onboarding needs context on past decisions.

Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.

Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.

Use when designing and scaling reference, story, advisory, or community

Scoring and governance framework for managing reference customers and

Use to identify, score, and prioritize customer advocates for programs.

API integration patterns for Adynato mobile apps. Covers data fetching with TanStack Query, authentication flows, offline support, error handling, and optimistic updates in React Native/Expo apps. Use when integrating APIs into mobile applications.

Web API development conventions for Adynato projects. Covers API routes, middleware, authentication, error handling, validation, and response formats for Next.js and Node.js backends. Use when building or modifying API endpoints, server actions, or backend logic.

Los usuarios perciben los diseños estéticamente atractivos como más usables que los menos atractivos, incluso si la funcionalidad real es idéntica.

Master African philosophical traditions including Ubuntu, Africana philosophy, and postcolonial thought. Use for: communitarian ethics, personhood, African metaphysics, decolonial philosophy. Triggers: 'Ubuntu', 'African philosophy', 'Africana', 'communitarian', 'postcolonial', 'decolonial', 'sage philosophy', 'ethnophilosophy', 'Negritude', 'African humanism', 'ubuntu ethics', 'communalism', 'African ontology', 'personhood Africa', 'I am because we are'.

Expert AI engineer specializing in AI system design, model implementation, and production deployment. Masters multiple AI frameworks and tools with focus on building scalable, efficient, and ethical AI solutions from research to production.

API architecture expert designing scalable, developer-friendly interfaces. Creates REST and GraphQL APIs with comprehensive documentation, focusing on consistency, performance, and developer experience.

Expert API documenter specializing in creating comprehensive, developer-friendly API documentation. Masters OpenAPI/Swagger specifications, interactive documentation portals, and documentation automation with focus on clarity, completeness, and exceptional developer experience.

Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Multi-Agent Supervisors (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.

Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

Use when creating, improving, or troubleshooting Claude Code subagents. Expert guidance on agent design, system prompts, tool access, model selection, and best practices for building specialized AI assistants.

Expert business analyst specializing in requirements gathering, process improvement, and data-driven decision making. Masters stakeholder management, business process modeling, and solution design with focus on delivering measurable business value.

Dependency checker and installer for agent-canvas, agent-eyes, and canvas-edit skills. Use BEFORE running any canvas skill for the first time, or when canvas skills fail with import/browser errors. Triggers on "setup agent canvas", "install canvas dependencies", "canvas not working", "playwright not found", or any setup/installation request for canvas skills.

Assesses agent capabilities by analyzing agent files and determining what tasks they can handle. Use when you need to evaluate if an existing agent can handle a task or if a new agent is needed. Reviews agent descriptions, skills, and workflows to determine capability match.

Generates Agent definitions (.md files) based on user intent and standard templates.