1,847 free skills
AI & ML skills
Skills for AI/ML work — LLM prompt engineering, RAG pipelines, vector databases, fine-tuning, and agent orchestration.
Sourced from real, public repositories — synced daily, never invented.
1,847 free skills
Skills for AI/ML work — LLM prompt engineering, RAG pipelines, vector databases, fine-tuning, and agent orchestration.
Sourced from real, public repositories — synced daily, never invented.
15 tools across six categories
13 of them never send your data anywhere
Free · No signup · No trial clock
SEE THE DIRECTORY
machine-learning
mindrally/skills
Machine learning development with JAX, functional programming patterns, and high-performance computing.
google-cloud-storage-basics
google/skills
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scientific-writing
davila7/claude-code-templates
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process: (1) create section outlines with key points using research-lookup, (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
atxp
atxp-dev/cli
Agent wallet, identity, and paid tools in one package. Register an agent, fund it via Stripe or USDC, then use the balance for web search, AI image generation, AI video generation, AI music creation, X/Twitter search, email send/receive, SMS and voice calls, contacts management, and 100+ LLM models. The funding and identity layer for autonomous agents that need to spend money, send messages, make phone calls, or call paid APIs.
press-and-pr
eronred/aso-skills
When the user wants to get press coverage, media mentions, or editorial features for their app — including writing press releases, pitching journalists, getting on "best apps" lists, or building an app press kit. Use when the user mentions "press", "PR", "media coverage", "TechCrunch", "journalist", "press release", "app press kit", "get featured in media", "editorial coverage", "review from a blogger", or "app launch announcement". For Apple editorial featuring, see app-store-featured. For launch strategy, see app-launch.
google-cloud-solution-rag-enterprise-search-gke-sqldb
google/skills
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review-and-ship
cursor/plugins
Review the current branch for bugs, intent fit, and test coverage; run or write tests; commit focused work; open or update a PR.
good-strategy-bad-strategy
wondelai/skills
Formulate and audit real strategy using Richard Rumelt''s "Good Strategy Bad Strategy": an honest diagnosis, a guiding policy, and coherent action instead of goals, vision, and wishful thinking. Use when the user mentions "good strategy bad strategy", "strategy kernel", "diagnosis guiding policy coherent action", "our strategy is just goals", "strategic planning", "mission vs strategy", "annual plan", or "is this actually a strategy". Also trigger when auditing a strategy doc or pitch deck for fluff, turning a goal list into real strategy, formulating strategy for a product or company, or finding leverage and proximate objectives. Covers the kernel of strategy, bad-strategy detection, and sources of power. For product positioning, see obviously-awesome. For uncontested markets, see blue-ocean-strategy.
nemo-mbridge-perf-sequence-packing
nvidia/skills
Validate and use packed sequences and long-context training in Megatron-Bridge, including offline LLM packing, collate-time VLM packing, Energon online packing, and CP constraints.
nemo-mbridge-resiliency
nvidia/skills
Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.
langchain-python-quickstart
langchain-ai/langchain-skills
Scaffold a minimal local LangChain agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
langchain-typescript-quickstart
langchain-ai/langchain-skills
Scaffold a minimal local LangChain agent in TypeScript by following the official quickstart. Use when the user wants to quickly build or try a LangChain agent locally.
building-with-llms
refoundai/lenny-skills
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
gtm-0-to-1-launch
github/awesome-copilot
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
migrate
launchdarkly/ai-tooling
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
high-output-management
wondelai/skills
Manage for output using Grove''s "High Output Management": a manager''s output is their organization''s output, raised by high-leverage activities. Use when the user mentions "high output management", "managerial leverage", "one-on-ones", "1:1 agenda", "OKRs", "performance review", "task-relevant maturity", "delegation", "meeting overload", "new manager", "how do I run a 1:1", or "just got promoted to manager". Also trigger when structuring a manager''s calendar and meeting cadence, designing team metrics, running planning, coaching delegation, or preparing performance reviews. Covers leverage, production principles, meetings as the medium of management, decisions, OKRs, and task-relevant maturity. For intrinsic motivation, see drive-motivation. For a company operating system, see traction-eos.
initiating-coverage
anthropics/financial-services
Create institutional-quality equity research initiation reports through a 5-task workflow. Tasks must be executed individually with verified prerequisites - (1) company research, (2) financial modeling, (3) valuation analysis, (4) chart generation, (5) final report assembly. Each task produces specific deliverables (markdown docs, Excel models, charts, or DOCX reports). Tasks 3-5 have dependencies on earlier tasks.
cargo-segmentation
getcargohq/cargo-skills
Define and use segments — named, saved filters over a Cargo model that become the audience for a batch run, a play trigger, or an export. Triggers: \"build a segment of\", \"filter my contacts where\", \"who matches this criteria\", \"save this as a list\", \"how many companies match\", \"the Closed-Won segment\", \"everyone who has not been emailed\", \"target only accounts that\", \"what is in this segment\", \"narrow this down to\". Filter JSON uses `conjonction` (not `conjunction`) — misspelling it fails silently. Skip when: running something over the segment — use cargo-orchestration; exporting its rows — use cargo-analytics; ad-hoc SQL over the model — use cargo-storage.
coverage
alirezarezvani/claude-skills
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deep-learning-pytorch
mindrally/skills
Expert guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.
lbo-model
anthropics/financial-services
This skill should be used when completing LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. The skill fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.
dual-axis-skill-reviewer
tradermonty/claude-trading-skills
Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.
markdown-converter
intellectronica/agent-skills
Convert documents and files to Markdown using markitdown. Use when converting PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls), HTML, CSV, JSON, XML, images (with EXIF/OCR), audio (with transcription), ZIP archives, YouTube URLs, or EPubs to Markdown format for LLM processing or text analysis.
transformers-js
huggingface/skills
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
scientific-writing
k-dense-ai/scientific-agent-skills
Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.
markitdown
julianobarbosa/claude-code-skills
Guide for using Microsoft MarkItDown - a Python utility for converting files to Markdown. Use when converting PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs, Jupyter notebooks, RSS feeds, or Wikipedia pages to Markdown format. Also use for document processing pipelines, LLM preprocessing, or text extraction tasks.
wwdc
superwall/skills
Use this skill whenever the user asks about WWDC sessions, Apple Developer videos, WWDC transcripts, session IDs, technologies announced at WWDC, or wants an agent to find, compare, cite, summarize, or navigate WWDC session content. Fetch current docs from wwdc.ai via llms.txt and page markdown. Maintained by Superwall.com: the quickest way to add in-app subscriptions and paywalls to your app.
media-relations
refoundai/lenny-skills
Help users build relationships with journalists and get press coverage. Use when someone is pitching reporters, preparing for media outreach, trying to get press coverage, or managing ongoing journalist relationships.
tear-sheet
anthropics/financial-services
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
llm-wiki
nashsu/llm_wiki_skill
Query the user's LLM Wiki knowledge base (the LLM Wiki desktop app at 127.0.0.1:19828 — NOT Obsidian, Notion, Apple Notes, Logseq, or any other PKM tool). Trigger ONLY when the user explicitly names LLM Wiki, says 'my wiki', 'my 知识库 / 知识库 / knowledge base', or asks things like 'what does my wiki say about X', 'read wiki page Y', 'show my wiki graph / 知识图谱', 'search in my LLM Wiki project', 'rescan my wiki sources / 重新索引', or names a wiki project by ID. DO NOT trigger on generic 'search my notes', 'find in my notebook', 'check my Obsidian', etc. — those belong to other tools the user may have installed. Covers wiki page search, file listing, content read, knowledge graph navigation, and source rescan against the running LLM Wiki desktop app. Read-only except for source rescan.
coverage-analysis
dotnet/skills
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pydantic-ai-harness
pydantic/skills
Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness -- Code Mode (collapse many tool calls into one sandboxed Python execution), a filesystem and shell, sub-agents, planning, context compaction, and more. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want first-party filesystem/shell/sub-agent/planning/compaction capabilities for a Pydantic AI agent, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.
project-health
jezweb/claude-skills
All-in-one project configuration and health management. Sets up new projects (settings.local.json, CLAUDE.md, .gitignore), audits existing projects (permissions, context quality, MCP coverage, leaked secrets, stale docs), tidies accumulated cruft, captures session learnings, and adds permission presets. Uses sub-agents for heavy analysis to keep main context clean. Trigger with 'project health', 'check project', 'setup project', 'kickoff', 'bootstrap', 'tidy permissions', 'clean settings', 'capture learnings', 'audit context', 'add python permissions', or 'init project'.
convex-agents
waynesutton/convexskills
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
technology-selection
dotnet/skills
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).
conversation-memory
sickn33/agentic-awesome-skills
Persistent memory systems for LLM conversations including
shadertoy
bfollington/terma
This skill should be used when working with Shadertoy shaders, GLSL fragment shaders, or creating procedural graphics for the web. Use when writing .glsl files, implementing visual effects, creating generative art, or working with WebGL shader code. This skill provides GLSL ES syntax reference, common shader patterns, and Shadertoy-specific conventions.
dd-docs
datadog-labs/agent-skills
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
gepetto
softaworks/agent-toolkit
Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
survey-generation
lingzhi227/agent-research-skills
Generate complete academic survey papers using multi-LLM parallel outline generation, RAG-based subsection writing, citation validation, and local coherence enhancement. Based on AutoSurvey pipeline. Use for writing comprehensive literature surveys.
eval
alirezarezvani/claude-skills
Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner among completed AgentHub agents.
storage-analyzer
kkkkhazix/khazix-skills
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context-window-management
sickn33/agentic-awesome-skills
Strategies for managing LLM context windows including
scikit-learn
k-dense-ai/scientific-agent-skills
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
stock-daily-analysis
chjm-ai/stock-daily-analysis-skill
LLM驱动的每日股票分析系统。支持A股/港股/美股自选股智能分析，生成决策仪表盘和大盘复盘报告。提供技术面分析（均线、MACD、RSI、乖离率）、趋势判断、买入信号评分。可与market-data skill集成获取更稳定的ETF数据。触发词：股票分析、分析股票、每日分析、技术面分析。
macos-cleaner
daymade/claude-code-skills
Analyze and reclaim macOS disk space through intelligent cleanup recommendations. This skill should be used when users report disk space issues, need to clean up their Mac, or want to understand what's consuming storage. Focus on safe, interactive analysis with user confirmation before any deletions.
ai-agents-architect
sickn33/agentic-awesome-skills
Expert in designing and building autonomous AI agents. Masters tool
phoenix-evals
arize-ai/phoenix
Build and run evaluators for AI/LLM applications using Phoenix.
netlify-agent-runner
netlify/context-and-tools
Run AI agent tasks remotely on Netlify using Claude, Codex, or Gemini. Use when the user wants to run an AI agent on their site, get a second opinion from another model, or delegate development tasks to run remotely against their repo.
design-token-audit
owl-listener/designer-skills
Audit token usage across a product for coverage, drift, and hard-coded values. Use when tokens exist and you suspect they are being bypassed. For defining tokens in the first place, use `design-token` (design-systems).
symbolic-equation
lingzhi227/agent-research-skills
Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.
research-paper-writing
master-cai/research-paper-writing-skills
Improve academic paper writing quality for ML/CV/NLP-style papers with clear section structure, paragraph flow, and reviewer-facing presentation. Use when drafting or revising Abstract, Introduction, Related Work, Method, Experiments, or Conclusion; polishing figures/tables; checking claim-support alignment; or performing self-review before submission.
prompt-engineer
davila7/claude-code-templates
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
amazon-listing-optimization
nexscope-ai/amazon-skills
Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.
tanstack-cli
tanstack-skills/tanstack-skills
Project scaffolding CLI with 30+ integrations, custom templates, and MCP server for AI agents.
audio-transcriber
sickn33/agentic-awesome-skills
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
avoid-feature-creep
waynesutton/convexskills
Prevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.
crm-lookup
hubspot/agent-cli-skills
Find a specific CRM record by ID, email, domain, or name fragment, and traverse associations for the full account picture.
hypogenic
k-dense-ai/scientific-agent-skills
Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
rag-retrieval
yonatangross/orchestkit
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.