erlang-concurrency

0
0
Source

Use when erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.

Install

mkdir -p .claude/skills/erlang-concurrency && curl -L -o skill.zip "https://mcp.directory/api/skills/download/6501" && unzip -o skill.zip -d .claude/skills/erlang-concurrency && rm skill.zip

Installs to .claude/skills/erlang-concurrency

About erlang-concurrency

erlang-concurrency is a specialized agent skill created by benchflow-ai that extends AI coding assistants with enhanced capabilities. Agent skills provide context, workflows, and specialized knowledge that help AI assistants perform specific tasks more effectively than general-purpose AI alone.

Use when erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.

How to use erlang-concurrency

Once installed, erlang-concurrency becomes available in your AI coding environment automatically. Your AI assistant will use the skill's instructions and knowledge whenever relevant tasks arise in your workflow. You can install it at the project level (available only in a specific project) or globally (available across all your projects).

Use the install panel on this page to copy a one-line command for your preferred AI client. The skill files are downloaded and placed in the appropriate directory — no additional configuration is required.

Compatible AI clients

erlang-concurrency works with multiple AI coding assistants including Cursor, Claude Code, Codex, Copilot, and other agents that support the skills format. Each client stores skills in a slightly different directory, but the installation command handles this automatically.

What are agent skills?

Agent skills are reusable instruction sets that give AI coding assistants new capabilities. Unlike MCP servers that provide tools and API connections, skills provide context, workflows, and domain-specific knowledge. Think of skills as specialized training for your AI assistant — they help it understand particular frameworks, coding patterns, or development workflows.

Browse more skills in the skills directory or check out the leaderboard to see the most popular skills.

About the author

benchflow-ai has published 7 skills on MCP.Directory. Browse their full catalog to discover complementary skills for your workflow.

latex-writing

benchflow-ai

Guide LaTeX document authoring following best practices and proper semantic markup. Use proactively when: (1) writing or editing .tex files, (2) writing or editing .nw literate programming files, (3) literate-programming skill is active and working with .nw files, (4) user mentions LaTeX, BibTeX, or document formatting, (5) reviewing LaTeX code quality. Ensures proper use of semantic environments (description vs itemize), csquotes (\enquote{} not ``...''), and cleveref (\cref{} not \S\ref{}).

4935

geospatial-analysis

benchflow-ai

Analyze geospatial data using geopandas with proper coordinate projections. Use when calculating distances between geographic features, performing spatial filtering, or working with plate boundaries and earthquake data.

287

pytorch

benchflow-ai

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.

305

search-flights

benchflow-ai

Search flights by origin, destination, and departure date using the bundled flights dataset. Use this skill when proposing flight options or checking whether a route/date combination exists.

214

d3js-visualization

benchflow-ai

Build deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.

174

deep-learning

benchflow-ai

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

83

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