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Claude Skills by aicodedecode
github.com/aicodedecode1,364 skills83 installs820 views
- Competitive IntelSystematic competitor tracking that feeds CMO positioning, CRO battlecards,Votes: 0GitHub stars: 2
- Competitive TeardownAnalyzes competitor products and companies by synthesizing data fromVotes: 0GitHub stars: 2
- Constraint Driven DevelopmentEstablishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or eslint-disable suppressions, skipped or deleted tests, assertions stripped out, unimplemented stubs, thresholds edited down. Use when no quality bar is written down, when the user says "se...Votes: 0GitHub stars: 2
- Content BriefUse when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.Votes: 0GitHub stars: 2
- Content Humanizer'Makes AI-generated content sound genuinely human — not just cleanedVotes: 0GitHub stars: 2
- Content StrategistBuilds content engines that rank, convert, and compound. Thinks in systemsVotes: 0GitHub stars: 2
- Context EngineeringOptimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.Votes: 0GitHub stars: 2
- Contract And Proposal Writer'Generate professional, jurisdiction-aware business documents: freelanceVotes: 0GitHub stars: 2
- Copy EditingWhen the user wants to edit, review, or improve existing marketing copy.Votes: 0GitHub stars: 2
- Creative IntakeCreative brief intake: 7 required questions (purpose, audience, platform, tone, references, outcome, constraints), conversational technique.Votes: 0GitHub stars: 2
- Data Processing Nemo CuratorGPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.Votes: 0GitHub stars: 2
- Data Processing Ray DataScalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.Votes: 0GitHub stars: 2
- DatabaseAdd official Railway database services (Postgres, Redis, MySQL, MongoDB). Use when user wants to add a database, says "add postgres", "add redis", "add database", "connect to database", or "wire up the database". For other templates (Ghost, Strapi, n8n), use the railway-templates skill.Votes: 0GitHub stars: 2
- Deep Research'Run a disciplined, multi-source research investigation for a high-stakesVotes: 0GitHub stars: 2
- Deep WorkUse when someone wants to plan a deep work day, time-block their calendarVotes: 0GitHub stars: 2
- DeepreadUse when the user asks to deeply read a book, article, PDF, or documentVotes: 0GitHub stars: 2
- Deploy To VercelDeploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".Votes: 0GitHub stars: 2
- DeployDeploy code to Railway using "railway up". Use when user wants to push code, says "railway up", "deploy", "ship", or "push". For initial setup or creating services, use railway-new skill. For Docker images, use railway-environment skill.Votes: 0GitHub stars: 2
- DeploymentManage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes). NOT for deleting services - use railway-environment skill with isDeleted for that.Votes: 0GitHub stars: 2
- Design CodeGenerate production-ready, accessible, token-driven component code for ANY framework — React+Tailwind, Next.js, SwiftUI, Vue, Svelte, Angular, Solid, Web Components/Lit, React Native, Flutter, Jetpack Compose, vanilla CSS, or CSS-in-JS. Use when the user wants working UI code for a component or screen in a specific stack.Votes: 0GitHub stars: 2
- Design ComponentDesign a UI component spec to the house quality bar — anatomy, variants, sizes, the 8 states, token mapping, and accessibility. Use when the user wants to design or document a component (button, input, tabs, toast, combobox, date picker, modal, etc.) at the spec level before or alongside code. For generating framework code, use design-code.Votes: 0GitHub stars: 2
- Design ScreensUse when designing, building, or changing a product screen, flow, or component in Figma — new screens, states, redesigns, or edits to already-approved work. Use when a design result was rejected, when a screen must match an existing product, or when starting design on a product with no visual direction yet. Not for auditing or documenting an existing design system without changing screens — that is audit-design-system.Votes: 0GitHub stars: 2
- Design SlidesUse when creating, restyling, reviewing, or editing a presentation, pitch deck, slide deck, talk, or design review deck, in Figma Slides, Figma Design, pptx, Google Slides, or HTML. Also use when a deck looks generic, templated, or AI-made and needs fixing.Votes: 0GitHub stars: 2
- Design TokensGenerate, extend, or audit design tokens in DTCG format with the 3-tier architecture (primitive → semantic → component). Use when the user wants a color palette, type scale, spacing/shadow/radius/motion tokens, multi-brand theming, or wants to validate token files. Covers colors, typography, spacing, shadows, borders, breakpoints, motion, gradients, opacity, blur, sizing, states, theming.Votes: 0GitHub stars: 2
- Develop Web GameUse when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with render_game_to_text.Votes: 0GitHub stars: 2
- Devops EngineerBuilds infrastructure that scales without babysitting. Automates everythingVotes: 0GitHub stars: 2
- Diagnosing BugsDiagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.Votes: 0GitHub stars: 2
- Distributed Training AccelerateSimplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.Votes: 0GitHub stars: 2
- Distributed Training DeepspeedExpert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attentionVotes: 0GitHub stars: 2
- Distributed Training Megatron CoreTrains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.Votes: 0GitHub stars: 2
- Distributed Training Pytorch FsdpExpert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2Votes: 0GitHub stars: 2
- Distributed Training Pytorch LightningHigh-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.Votes: 0GitHub stars: 2
- Distributed Training Ray TrainDistributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.Votes: 0GitHub stars: 2
- DnsHostinger DNS API for zone record management, snapshots, and validation. Use when creating, updating, or deleting DNS records, restoring DNS snapshots, validating zone changes, or resetting DNS to defaults.Votes: 0GitHub stars: 2
- DocUse when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the bundled `scripts/render_docx.py` for visual checks.Votes: 0GitHub stars: 2
- Documentation And AdrsRecords decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.Votes: 0GitHub stars: 2
- Domain ModelingBuild and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a CONTEXT.md, or recording or editing an ADR.Votes: 0GitHub stars: 2
- DomainAdd, view, or remove domains for Railway services. Use when user wants to add a domain, generate a railway domain, check current domains, get the URL for a service, or remove a domain.Votes: 0GitHub stars: 2
- DomainsHostinger Domains API for domain portfolio management, availability checks, forwarding, WHOIS profiles, nameservers, domain lock, privacy protection, and domain access verifications. Use when registering domains, checking availability, managing DNS delegation, configuring redirects, handling WHOIS contact information, or checking domain verification status.Votes: 0GitHub stars: 2
- Doubt Driven DevelopmentSubjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production auth, security-sensitive logic, a high-stakes migration, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.Votes: 0GitHub stars: 2
- EcommerceHostinger Ecommerce API for managing online stores. Use when listing the stores on an account or creating a new store (which also provisions a primary sales channel).Votes: 0GitHub stars: 2
- Economy DesignerVirtual economy architect - Masters currency systems, sources and sinks, monetization modeling, inflation control, and data-driven economic balancing for live gamesVotes: 0GitHub stars: 2
- Eeat AuditUse when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.Votes: 0GitHub stars: 2
- Email Template Builder'Build complete transactional email systems: React Email templates, providerVotes: 0GitHub stars: 2
- Embedded Iot MentorMentor for embedded and IoT hardware projects. Helps select MCUs, devVotes: 0GitHub stars: 2
- Emerging Techniques Knowledge DistillationCompress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.Votes: 0GitHub stars: 2
- Emerging Techniques Long ContextExtend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.Votes: 0GitHub stars: 2
- Emerging Techniques Model MergingMerge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.Votes: 0GitHub stars: 2
- Emerging Techniques Model PruningReduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.Votes: 0GitHub stars: 2
- Emerging Techniques Moe TrainingTrain Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.Votes: 0GitHub stars: 2