ReddRadar
Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
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book-sft-pipeline
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
muratcankoylan/book-training 32
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iosdev-cn
通用 iOS App 开发、构建、签名、测试与 App Store 上架流程（中国区）指南。用于当用户询问 iOS 开发/上架/审核/签名/TestFlight/App Store Connect/隐私合规/订阅配置，或输入触发词 iosdev 时。
kuangre123/iosdev 16
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programming-advisor
Build vs Buy advisor. Use when users say: 'I want to build...', 'Help me create...', 'Can you code...', 'I need a tool/app/script'. Searches for existing libraries, SaaS, and open source solutions before vibe coding. Estimates token costs and provides comparison tables.
gaupoit/programming-advisor 12
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mbt-wasip1-tools
Build small MoonBit WASIp1 CLI tools using the peter-jerry-ye/wasi library, focused on simple read/write tasks (echo, cat, wc, simple file ops). Use when creating or updating CLI examples, scripts, or skills for this repo.
moonbit-community/wasip1 12
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portable-safe-skills
Create portable, safe Codex skills focused on file or I/O operations under WASIp1 constraints. Use when authoring skills that must be portable across environments and avoid unsafe assumptions about paths, preopens, or stdio.
moonbit-community/wasip1 12
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pytorch
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
itsmostafa/llm-engineering-skills 17
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lora
Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). Use when fine-tuning large language models with limited GPU memory, creating task-specific adapters, or when you need to train multiple specialized models from a single base.
itsmostafa/llm-engineering-skills 17
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mlx
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
itsmostafa/llm-engineering-skills 17
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context-engineering
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
itsmostafa/llm-engineering-skills 17
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prompt-engineering
Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.
itsmostafa/llm-engineering-skills 17
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qlora
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
itsmostafa/llm-engineering-skills 17
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agents
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
itsmostafa/llm-engineering-skills 17
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rlhf
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
itsmostafa/llm-engineering-skills 17
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transformers
Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
itsmostafa/llm-engineering-skills 17
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skill-customizer
Customize existing skills through iterative improvement based on user feedback and preferences. Use when users want to personalize a skill to match their specific workflow, output preferences, domain requirements, or company standards by forking and iteratively refining an existing skill.
nemori-ai/skills 11
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mcp-skill-creator
Meta-skill for creating workflow-optimized skills from MCP servers. Use when users want to create a custom skill that integrates one or more MCP servers into a specialized workflow. The user provides MCP server configurations and describes their work scenario (workflow, preferences, SOPs), and this skill generates a new skill with optimized scripts following Anthropic's MCP + code execution best practices.
nemori-ai/skills 11
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golang-pro
Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration.
DeevsDeevs/agent-system 36
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datetime
Get current date and time in various formats. Use whenever you need the current date, time, timestamps, or formatted datetime values for any purpose (logging, file naming, scheduling, comparisons, etc.)
DeevsDeevs/agent-system 36
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97-dev
Apply timeless programming wisdom from "97 Things Every Programmer Should Know" when writing, reviewing, or refactoring code. Use for design decisions, code quality checks, professional development guidance, testing strategies, and workflow optimization.
DeevsDeevs/agent-system 36
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dev-experts
Apply opinionated developer personas for architecture decisions, production debugging, language-specific code review, comprehensive reviewer passes, and test strategy. Use when you need an architect plan, devops investigation, Rust/Python/C++ review, grumpy reviewer audit, or tester-driven test plan. Triggers: architect, devops, rust-dev, python-dev, cpp-dev, reviewer, tester, pre-merge review, refactor for maintainability.
DeevsDeevs/agent-system 36
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polars-expertise
This skill should be used when the user asks about Polars DataFrame library (Apache Arrow) for Python or Rust. Triggers: "polars expressions", "lazy vs eager", "scan_parquet streaming", "convert pandas to polars", "pyspark to polars", "kdb to polars", "group_by_dynamic", "rolling_mean", "polars window functions", "asof join", "polars GPU", "polars parquet", "LazyFrame". Time series: OHLCV resampling, rolling windows, financial data patterns. Performance: native expressions over map_elements, early projection, categorical types, streaming.
DeevsDeevs/agent-system 36
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venue-expert
This skill should be used when the user asks about "market microstructure", "exchange mechanics", "order book", "auction", "NBBO", "Reg NMS", "trading venue", "halt", "LULD", "tick size", "maker-taker", "price-time priority", "SIP", "direct feed", "TRF", "wholesaler", "PFOF", "best execution", "trade-through", "ISO", "opening cross", "closing cross", "NOII", "ITCH", "OUCH", or mentions specific exchanges (Nasdaq, NYSE, CME, Binance, SHFE, DCE, CZCE, CFFEX, INE, etc.).
For Chinese futures: "CTP", "综合交易平台", "夜盘", "night session", "看穿式监管", "position limits", "持仓限额", queue position in Chinese markets, or Chinese product codes (rb, cu, sc, if, ic, i, j, ta, ma, etc.).
Provides hierarchical venue expertise for research and debugging trading systems.
DeevsDeevs/agent-system 36
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anti-ai-slop
After working on the code, ensure the branch contains only the minimal, idiomatic changes by removing AI-generated slop introduced on this branch.
DeevsDeevs/agent-system 36
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bug-hunters
Run systematic bug hunting with spec reconstruction, adversarial validation, and confidence scoring. Use when you want to hunt bugs (not fix them), validate correctness, or run logic-first/code-first investigations. Triggers: bug hunt, spec reconstruction, logic-first, code-first, orchestrator, logic-hunter, cpp-hunter, python-hunter.
DeevsDeevs/agent-system 36