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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fobis
by szaghiExpert knowledge of FoBiS.py (Fortran Building System for poor men) — an automatic Fortran build tool that resolves module dependency hierarchies without manual makefiles. Use this skill whenever the user asks about: writing or editing a fobos file, running fobis build/clean/fetch/install/rule commands, Fortran project build configuration, diagnosing FoBiS build errors, adding GitHub dependencies to a Fortran project, the --json output flag, multi-mode builds, templates, variables, library builds (static/shared), MPI/OpenMP/coarray builds, the fetch command, the install command, the cflags-heritage feature, parallel compilation, or any question that mentions "fobos", "FoBiS", or building Fortran projects. When in doubt, trigger this skill — it is better to consult it unnecessarily than to miss it.
mosaic
by szaghiExpert knowledge of MOSAIC (Multi-source Scientific Article Indexer and Collector) — a CLI tool for searching, downloading, and managing scientific papers from 21 sources with a single command. Use this skill whenever the user asks about: building a bibliography programmatically, searching for papers across multiple sources, downloading OA PDFs, exporting to BibTeX/Zotero/Obsidian, interpreting mosaic --json output in AI agent or CI workflows, RAG over a paper library, finding similar papers, or any task that involves mosaic search/get/similar/ask/chat/index/skill commands. When in doubt, trigger this skill — it is better to consult it unnecessarily than to miss it.
mosaic
by szaghiExpert knowledge of MOSAIC (Multi-source Scientific Article Indexer and Collector) — a CLI tool for searching, downloading, and managing scientific papers from 21 sources with a single command. Use this skill whenever the user asks about: building a bibliography programmatically, searching for papers across multiple sources, downloading OA PDFs, formatting citation strings (BibTeX/APA/ MLA/Chicago), exporting to BibTeX/Zotero/Obsidian, interpreting mosaic --json output in AI agent or CI workflows, RAG over a paper library, semantic search over a local paper library, finding similar papers, analysing citation networks, comparing papers across structured dimensions, or any task that involves mosaic search/get/cite/similar/ask/chat/index/network/compare/skill commands. When in doubt, trigger this skill — it is better to consult it unnecessarily than to miss it.
openacc-3-4
by szaghiAuthoritative knowledge base from the OpenACC Application Programming Interface v3.4 specification. CONSULT THIS BEFORE ANSWERING — do not answer OpenACC questions from memory; directive/clause semantics, data-clause behavior, and async-queue ordering rules are easy to misremember and version-sensitive. TRIGGER whenever a question concerns: writing/reading/debugging any OpenACC directive (#pragma acc / !$acc); offloading C/C++/Fortran to GPU or multicore; gang/worker/vector parallelism or execution modes; data clauses (copy/copyin/copyout/create/present/no_create/deviceptr/attach) or data-region/reference-counter behavior; loop/collapse/tile/reduction/private mapping; async/wait queues; the routine directive; atomic/declare/update directives; the acc_* runtime API; environment variables (ACC_*); or diagnosing GPU offload, data-movement, or benchmark-timing problems. SKIP only when the user explicitly wants OpenMP-offload, CUDA, or a vendor-compiler-specific (nvhpc/gcc) behavior rather than the OpenACC standar
openmp-6-0
by szaghiAuthoritative knowledge base from the OpenMP API v6.0 specification (Nov 2024) + the Nov-2025 errata (corrections applied inline). CONSULT THIS BEFORE ANSWERING — do not answer OpenMP questions from memory; directive/clause semantics, data-sharing vs data-mapping rules, the flush memory model, schedule/tasking/offload behavior, and the runtime API are subtle and version-sensitive. TRIGGER whenever a question concerns: writing/reading/debugging any OpenMP directive (#pragma omp / !$omp); multithreading or GPU/device offload in C/C++/Fortran via OpenMP; data-sharing clauses (shared/private/firstprivate/lastprivate/reduction) or data-mapping (map/target); parallel/teams/simd/masked, worksharing (for/sections/single/distribute/schedule), tasking (task/taskloop/taskgraph/depend), synchronization (barrier/critical/atomic/ordered/flush), the device model (target/declare target), memory allocators/spaces, variant directives (metadirective/declare variant), the omp_* runtime API, OMP_* environment variables/ICVs, OMPT
gpu-multithreading
by szaghiPractitioner knowledge base for parallel, multithreaded, and GPU programming — the design methodology, performance laws, and cross-technology optimization playbook. Use when designing or optimizing parallel software: choosing a parallel decomposition (PCAM, geometric/pipeline/master-worker patterns); reasoning about speedup and scalability (Amdahl, Gustafson, roofline, arithmetic intensity); writing shared-memory code (C++ threads, mutexes, atomics, memory_order, condition variables, lock-free/CAS, false sharing, deadlock); distributed-memory message passing (MPI, domain decomposition, halo exchange, collectives); GPU programming (CUDA/OpenCL thread hierarchy, warps, coalescing, shared-memory tiling, occupancy, host-device transfer); directive-based parallelism (OpenMP fork-join, data-sharing clauses, reductions); OpenMP GPU offload in depth (target/teams/distribute, the map clause and target-data regions, declare target, unified shared memory, async multi-device offload, the Eightfold Path to performance); h
fortran-2023-standard
by szaghiAuthoritative knowledge base from the ISO/IEC 1539-1:2023 Fortran standard (J3/23-007r1). CONSULT THIS BEFORE ANSWERING — do not answer Fortran-standard questions from memory; the standard's exact rules, constraints (Cxxx), and version deltas are easy to misremember. TRIGGER whenever a question concerns: what the Fortran standard requires/permits/forbids; whether code is standard-conforming; any modern-Fortran feature (conditional expressions, SIMPLE/PURE/ELEMENTAL, enum/enumeration types, TYPEOF/CLASSOF, DO CONCURRENT incl. REDUCE locality, coarrays/teams, C interoperability, IEEE arithmetic); a syntax rule (Rxxx) or constraint (Cxxx); a difference between Fortran versions (F2023/F2018/F2008/F2003/F95/F90); or whether a feature is deleted/obsolescent. SKIP only for pure build/tooling questions (use the fobis skill) or when the user explicitly wants compiler-specific (gfortran/nvfortran/ifx) behavior rather than the standard.
iso-c-9899-2024
by szaghiAuthoritative knowledge base from the ISO/IEC 9899:2024 C standard (C23, working draft N3220). CONSULT THIS BEFORE ANSWERING — do not answer C-standard questions from memory; the standard's exact rules, constraints, undefined-behavior catalogue (Annex J), and C23 version deltas are easy to misremember. TRIGGER whenever a question concerns: what the C standard requires/permits/forbids; whether code is standard-conforming or has undefined/unspecified/implementation-defined behavior; any C23 feature (_BitInt, constexpr, typeof, auto type inference, nullptr, enum with fixed underlying type, [[attributes]], #embed, __VA_OPT__, _Generic, <stdbit.h>, <stdckdint.h>, decimal floats); integer promotion / usual arithmetic conversions / conversion rank; the C memory model (atomics, memory_order, data races); the floating-point model (Annex F / IEC 60559, <fenv.h>, fma, rounding); sequence points and evaluation order; the preprocessor; the standard library headers; or bounds-checking (Annex K _s functions). SKIP only for
hpc-numerics
by szaghiPractitioner knowledge base for the numerical and algorithmic theory of high-performance scientific computing — the science beneath the parallel-programming mechanics. Use when reasoning about numerical correctness, algorithm design, or performance modeling: floating-point arithmetic and round-off error (machine epsilon, catastrophic cancellation, non-associativity, Kahan summation); conditioning vs stability (condition number, backward stability); ODE/PDE discretization (finite differences, stencils, explicit vs implicit Euler, stiffness, CFL condition, method of lines); numerical linear algebra (LU factorization, pivoting, sparse matrices, fill-in, reordering); iterative and Krylov solvers (Jacobi/Gauss-Seidel, CG, GMRES, preconditioning, multigrid); performance programming (the memory wall, cache blocking/tiling, the roofline model, arithmetic intensity); high-performance linear algebra (BLAS levels, gemm, block algorithms); combinatorial algorithms (parallel sorting networks, graph algorithms as sparse li
hpc-index
by szaghiRouter and disambiguation map for the HPC skill fleet — decides WHICH high-performance-computing skill to consult when a query plausibly matches several. Use ONLY for cross-cutting routing/navigation: when the user asks which HPC skill covers a topic, says 'what HPC skills do I have', wants the map/relationships between them, or poses a parallel/numerical/HPC question that genuinely spans the reference-vs-applied-vs-theory boundary and the right skill is ambiguous (e.g. 'CG solver not converging' = theory vs PETSc API; 'optimize my CUDA kernel' = design playbook vs CUDA reference; an MPI question that could be the standard vs the C++/Python practical layer). SKIP this skill when the query already names one technology unambiguously — a pure MPI-semantics question goes straight to mpi-5.0, a C++23-standard question to iso-cpp-2023, a Numba-CUDA-Python question to python-hpc — those route directly without this router. This is a thin navigation layer over twelve HPC skills, not a knowledge base itself.
hpc-cluster-tooling
by szaghiPractitioner knowledge base for the practical workflow tooling of HPC clusters — the command-line skills around writing, building, debugging, profiling, and running scientific code at scale. Use when working on a cluster or HPC project's toolchain: Unix shell for HPC (pipes, grep/sed/awk, ssh/rsync, tmux, environment modules); build automation with Make (targets, rules, automatic variables, pattern rules, parallel make); the CMake build system (out-of-source builds, find_package, target-based commands, build types); git version control with HPC discipline (gitignore, LFS, reproducibility tagging); debugging with GDB (breakpoints, watchpoints, backtrace, core dumps); memory and parallel debugging (Valgrind, AddressSanitizer/ThreadSanitizer, MPI debugging, DDT/TotalView); profiling and benchmarking (gprof, perf, PAPI hardware counters, TAU parallel profiling/tracing); and SLURM batch job management (sbatch/squeue/scancel, #SBATCH directives, job arrays, dependencies, login vs compute nodes). Covers the cluster
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.