Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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virtuoso
by Arcadia-1Bridge to remote Cadence Virtuoso via Python API. TRIGGER when user mentions: Virtuoso, Maestro, ADE, CIW, SKILL, layout, schematic, cellview, OCEAN, or any Cadence EDA operation.
optimizer
by Arcadia-1Black-box optimization of design parameters using TuRBO or scipy. TRIGGER when the user wants to optimize, tune, size, sweep, or explore a design space to meet specs. This includes circuit sizing (W/L, bias, passives), finding optimal operating points, minimizing power-delay or noise-power tradeoffs, or any task where multiple parameters need to be searched to hit a target. Also trigger when the user says things like 'find the best sizing', 'help me tune this', 'run an optimization', 'what values give me the best FOM', or 'sweep these parameters to meet spec'. Do NOT trigger for single-variable parametric sweeps or analytical calculations.
spectre
by Arcadia-1Run Cadence Spectre simulations remotely via virtuoso-bridge: upload netlists, execute, parse PSF results. TRIGGER when the user wants to run a SPICE/Spectre simulation from a netlist file, do transient/AC/PSS/pnoise analysis outside Virtuoso GUI, parse PSF waveform data, run multiple simulations in parallel across one or more servers, check simulation job status, or mentions Spectre APS/AXS modes. Also triggers for sim-jobs, sim-cancel, or parallel/concurrent simulation requests. Use this for standalone netlist-driven simulation — for GUI-based ADE Maestro simulation, use the virtuoso skill instead.
adctoolbox-user-guide
by Arcadia-1Router skill for using ADCToolbox from Python. Trigger when a task involves: computing or plotting spectra (SNDR, SFDR, ENOB, THD) from ADC output, fitting a sine to measured aout, calibrating SAR weights (weight_sine / weight_sine_lite), generating synthetic ADC stimulus/output, or validating aout/dout buffer shapes. For deeper debug (dashboards, phase-plane, bit-level, error decomposition, static nonlinearity, cap-to-weight), open references/advanced-debug.md. NOT for analog topology selection, transistor sizing, Spectre simulation, or layout/parasitic review — those belong to the analog-agents skills (analog-design, analog-verify, analog-audit). NOT for editing ADCToolbox source code — use adctoolbox-contributor-guide instead.
adctoolbox-contributor-guide
by Arcadia-1Maintainer-only guide for editing the ADCToolbox repository itself. Use this skill only when changing source, exports, examples, tests, packaging, or the bundled skills under `python/src/adctoolbox/_bundled_skills/skills/`. Do not use it for ordinary library usage questions; use `adctoolbox-user-guide` for that.
ngspice
by Arcadia-1ngspice simulation tutorial and template skill. Provides nine standard simulation examples: (1) Transient — RC charging voltage and current; (2) DC — NMOS Id-Vds family curves; (3) AC — RC low-pass filter frequency response; (4) Noise — RC filter output noise spectral density and kT/C; (5) Transient — sample-and-hold switch comparison; (6) Transient — kT/C noise time-domain statistical measurement; (7) DC — NMOS current mirror output characteristics; (8) AC — common-source amplifier frequency response; (9) DC — transmission gate on-resistance. Built-in PTM 180/45/22nm models included.
transistor-models
by Arcadia-1Complete PTM (Predictive Technology Model) MOSFET model library from mec.umn.edu/ptm, covering all nodes: bulk conventional 180/130/90/65nm, bulk HP/LP 45/32/22nm (BSIM4), and PTM-MG multi-gate FinFET 7/10/14/16/20nm (BSIM-CMG, HP + LSTP). No manual downloads required after installing this skill. Independent of the gmoverid skill — can be used directly in any ngspice/HSPICE project.
gmoverid
by Arcadia-1gm/ID transistor characterization and design methodology, based on ngspice + Python. Two independent workflows: (1) Characterization — generates three standard curve sets for any MOSFET model: gate capacitance (Cgg/Cgs/Cgd/Cgb vs Vgs), gm/ID four-quadrant characteristics (gm/Id vs Vov, Id/W vs gm/Id, fT vs gm/Id, gm·ro vs gm/Id), and IV characteristics (linear/log Id vs Vov, output curves). Supports 180 nm single-node and 45/22 nm HP multi-node flows with built-in PTM model files (180/45/22 nm) — no extra downloads required. (2) Design — the GmIdTable class builds a lookup table from simulation data (cached to logs/cache/) and provides lookup(), size(), size_from_ft(), size_from_gmro() APIs for NMOS/PMOS transistor sizing using the gm/ID methodology. Only depends on the ngspice skill. Use this skill when setting up or extending a gm/ID characterization project, generating characteristic curves, interpreting design curves, or sizing transistors by the gm/ID method.
analog-decompose
by Arcadia-1Architect Phase 1: decompose top-level analog spec into sub-block specs. Use when starting a new analog design to select architecture, allocate budgets, define sub-block specs, write testbenches, and create verification plans.
analog-explore
by Arcadia-1Explore analog design space without simulation. Compare topologies, sweep parameters with hand calculations, find theoretical limits and Pareto tradeoffs. Use for architecture selection, initial sizing, or understanding design space before committing to EDA time. TRIGGER on: "compare topologies", "explore", "design space", "what are my options", "tradeoff analysis", "Pareto".
analog-integrate
by Arcadia-1Architect Phase 3: replace behavioral models with verified transistor netlists and run top-level integration verification. Use after all sub-blocks pass L2.
analog-learn
by Arcadia-1Interactive analog design learning companion. Explains circuit design decisions step by step with underlying physics. Use when learning analog design, studying a topology, or wanting detailed explanations of design tradeoffs. TRIGGER on: "teach me", "explain", "why does", "how does", "learn", "tutorial", "walk me through", or any educational analog design question.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.