Agent skill

Erlang Distribution

by benchflow-ai in benchflow-ai/skillsbench

A skill your agent uses when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building…

Apache-2.0Auto-check passed

Install Erlang Distribution

skills CLI
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .claude/skills/erlang-distribution && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
erlang-distribution
GitHub stars
1.8k
Used in
1 other repo
Token cost
~3.3k tokens
SKILL.md length
517 words
Files
1
Skills in repo
178
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building…

  • Works in 10 steps: Use short names for local clusters and… → Set same cookie on all nodes in trusted… → Monitor node connections to detect and… → …
  • Erlang distributed systems including node connectivity
  • SKILL.md covers Introduction, Node Connectivity, Distributed Message Passing and Global Name Registration, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Erlang Distribution is an agent skill from benchflow-ai/skillsbench. Use when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.

When your agent uses it

  • Erlang distributed systems including node connectivity
  • Distributed processes
  • Global name registration
  • Distributed supervision

Example prompts

  • “/erlang-distribution”

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Use short names for local clusters and long names for internet-wide distribution
  2. Set same cookie on all nodes in trusted cluster for security
  3. Monitor node connections to detect and handle network partitions
  4. Use global registration sparingly as it adds coordination overhead
  5. Implement partition detection and healing strategies for resilience
  6. Design for eventual consistency in distributed systems accepting CAP limitations
  7. Use RPC for simple calls but prefer message passing for complex protocols
  8. Test with network failures using tools like toxiproxy or chaos engineering
  9. Implement proper timeouts on distributed calls to handle slow networks
  10. Use distributed supervision to maintain fault tolerance across nodes

What it can do on your machine

Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are erlang).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • erlang.org
    • learnyousomeerlang.com
    • oreilly.com
    • infoq.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Erlang Distribution loads about 3.3k tokens when it runs. Until then it costs about 62 tokens; SKILL.md has 517 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 517 words, ~3,326 tokens.

Download SKILL.mdSave it as .claude/skills/erlang-distribution/SKILL.md (or your agent's skills folder).
name
erlang-distribution
description
Use when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.

Erlang Distribution

Introduction

Erlang's built-in distribution enables building clustered, fault-tolerant systems across multiple nodes. Processes on different nodes communicate transparently through the same message-passing primitives used locally. This location transparency makes distributed programming natural and straightforward.

The distribution layer handles network communication, serialization, and node connectivity automatically. Nodes discover each other through naming, with processes addressable globally via registered names or pid references. Understanding distribution patterns is essential for building scalable, resilient systems.

This skill covers node connectivity and clustering, distributed message passing, global name registration, distributed supervision, handling network partitions, RPC patterns, and building production distributed applications.

Node Connectivity

Nodes connect to form clusters for distributed computation and fault tolerance.

erlang
%% Starting named nodes
%% erl -name node1@hostname -setcookie secret
%% erl -sname node2 -setcookie secret

%% Connecting nodes
connect_nodes() ->
    Node1 = 'node1@host',
    Node2 = 'node2@host',
    net_kernel:connect_node(Node2).

%% Check connected nodes
list_nodes() ->
    Nodes = [node() | nodes()],
    io:format("Connected nodes: ~p~n", [Nodes]).

%% Monitor node connections
monitor_nodes() ->
    net_kernel:monitor_nodes(true),
    receive
        {nodeup, Node} ->
            io:format("Node up: ~p~n", [Node]);
        {nodedown, Node} ->
            io:format("Node down: ~p~n", [Node])
    end.

%% Node configuration
start_distributed() ->
    {ok, _} = net_kernel:start([mynode, shortnames]),
    erlang:set_cookie(node(), secret_cookie).

%% Hidden nodes (for monitoring)
connect_hidden(Node) ->
    net_kernel:connect_node(Node),
    erlang:disconnect_node(Node),
    net_kernel:hidden_connect_node(Node).

%% Get node information
node_info() ->
    #{
        name => node(),
        cookie => erlang:get_cookie(),
        nodes => nodes(),
        alive => is_alive()
    }.

Node connectivity enables building distributed clusters with automatic discovery.

Distributed Message Passing

Send messages to processes on remote nodes using same syntax as local messaging.

erlang
%% Send to registered process on remote node
send_remote(Node, Name, Message) ->
    {Name, Node} ! Message.

%% Spawn process on remote node
spawn_on_remote(Node, Fun) ->
    spawn(Node, Fun).

spawn_on_remote(Node, Module, Function, Args) ->
    spawn(Node, Module, Function, Args).

%% Distributed request-response
remote_call(Node, Module, Function, Args) ->
    Pid = spawn(Node, fun() ->
        Result = apply(Module, Function, Args),
        receive
            {From, Ref} -> From ! {Ref, Result}
        end
    end),
    Ref = make_ref(),
    Pid ! {self(), Ref},
    receive
        {Ref, Result} -> {ok, Result}
    after 5000 ->
        {error, timeout}
    end.

%% Distributed work distribution
-module(work_dispatcher).
-export([start/0, dispatch/1]).

start() ->
    register(?MODULE, spawn(fun() -> loop([]) end)).

dispatch(Work) ->
    ?MODULE ! {dispatch, Work}.

loop(Workers) ->
    receive
        {dispatch, Work} ->
            Node = select_node(nodes()),
            Pid = spawn(Node, fun() -> do_work(Work) end),
            loop([{Pid, Node} | Workers])
    end.

select_node(Nodes) ->
    lists:nth(rand:uniform(length(Nodes)), Nodes).

do_work(Work) ->
    Result = process_work(Work),
    io:format("Work done on ~p: ~p~n", [node(), Result]).

process_work(Work) -> Work * 2.

%% Remote group leader for output
remote_process_with_io(Node) ->
    spawn(Node, fun() ->
        group_leader(self(), self()),
        io:format("Output from ~p~n", [node()])
    end).

Location-transparent messaging enables seamless distributed communication.

Global Name Registration

Register process names globally across distributed clusters.

erlang
%% Global registration
register_global(Name) ->
    Pid = spawn(fun() -> global_loop() end),
    global:register_name(Name, Pid),
    Pid.

global_loop() ->
    receive
        {From, Message} ->
            From ! {reply, Message},
            global_loop();
        stop -> ok
    end.

%% Send to globally registered process
send_global(Name, Message) ->
    case global:whereis_name(Name) of
        undefined ->
            {error, not_found};
        Pid ->
            Pid ! Message,
            ok
    end.

%% Global name with conflict resolution
register_with_resolve(Name) ->
    Pid = spawn(fun() -> server_loop() end),
    ResolveFun = fun(Name, Pid1, Pid2) ->
        %% Keep process on node with lower name
        case node(Pid1) < node(Pid2) of
            true -> Pid1;
            false -> Pid2
        end
    end,
    global:register_name(Name, Pid, ResolveFun).

server_loop() ->
    receive
        Message ->
            io:format("Received: ~p on ~p~n", [Message, node()]),
            server_loop()
    end.

%% Global synchronization
sync_global() ->
    global:sync().

%% List globally registered names
list_global_names() ->
    global:registered_names().

%% Re-register after node reconnection
ensure_global_registration(Name, Fun) ->
    case global:whereis_name(Name) of
        undefined ->
            Pid = spawn(Fun),
            global:register_name(Name, Pid),
            Pid;
        Pid ->
            Pid
    end.

Global registration enables location-independent process discovery.

Distributed Supervision

Supervise processes across multiple nodes for cluster-wide fault tolerance.

erlang
-module(distributed_supervisor).
-behaviour(supervisor).

-export([start_link/0, start_worker/1]).
-export([init/1]).

start_link() ->
    supervisor:start_link({local, ?MODULE}, ?MODULE, []).

start_worker(Node) ->
    ChildSpec = #{
        id => make_ref(),
        start => {worker, start_link, [Node]},
        restart => permanent,
        type => worker
    },
    supervisor:start_child(?MODULE, ChildSpec).

init([]) ->
    SupFlags = #{
        strategy => one_for_one,
        intensity => 5,
        period => 60
    },
    {ok, {SupFlags, []}}.

%% Worker module spawning on specific node
-module(worker).
-export([start_link/1, loop/0]).

start_link(Node) ->
    Pid = spawn_link(Node, ?MODULE, loop, []),
    {ok, Pid}.

loop() ->
    receive
        stop -> ok;
        Msg ->
            io:format("Worker on ~p: ~p~n", [node(), Msg]),
            loop()
    end.

%% Distributed process groups
-module(pg_example).
-export([start/0, join/1, broadcast/1]).

start() ->
    pg:start_link().

join(Group) ->
    pg:join(Group, self()).

broadcast(Group, Message) ->
    Members = pg:get_members(Group),
    [Pid ! Message || Pid <- Members].

Distributed supervision maintains system health across node failures.

RPC and Remote Execution

Execute function calls on remote nodes with various invocation patterns.

erlang
%% Basic RPC
simple_rpc(Node, Module, Function, Args) ->
    rpc:call(Node, Module, Function, Args).

%% RPC with timeout
timed_rpc(Node, Module, Function, Args, Timeout) ->
    rpc:call(Node, Module, Function, Args, Timeout).

%% Async RPC
async_rpc(Node, Module, Function, Args) ->
    Key = rpc:async_call(Node, Module, Function, Args),
    %% Later retrieve result
    rpc:yield(Key).

%% Parallel RPC to multiple nodes
parallel_rpc(Nodes, Module, Function, Args) ->
    rpc:multicall(Nodes, Module, Function, Args).

%% Parallel call with results
parallel_rpc_results(Nodes, Module, Function, Args) ->
    rpc:multicall(Nodes, Module, Function, Args, 5000).

%% Cast (fire and forget)
cast_rpc(Node, Module, Function, Args) ->
    rpc:cast(Node, Module, Function, Args).

%% Broadcast to all nodes
broadcast_rpc(Module, Function, Args) ->
    Nodes = [node() | nodes()],
    rpc:multicall(Nodes, Module, Function, Args).

%% Parallel map over nodes
pmap_nodes(Fun, List) ->
    Nodes = nodes(),
    DistFun = fun(X) ->
        Node = lists:nth((X rem length(Nodes)) + 1, Nodes),
        rpc:call(Node, erlang, apply, [Fun, [X]])
    end,
    lists:map(DistFun, List).

RPC enables convenient remote execution with location transparency.

Network Partitions and CAP

Handle network partitions and understand CAP theorem trade-offs.

erlang
%% Detect network partition
detect_partition() ->
    ExpectedNodes = [node1@host, node2@host, node3@host],
    CurrentNodes = nodes(),
    Missing = ExpectedNodes -- CurrentNodes,
    case Missing of
        [] -> ok;
        Nodes -> {partition, Nodes}
    end.

%% Partition healing strategy
-module(partition_handler).
-export([monitor_cluster/1]).

monitor_cluster(ExpectedNodes) ->
    net_kernel:monitor_nodes(true),
    monitor_loop(ExpectedNodes, nodes()).

monitor_loop(Expected, Current) ->
    receive
        {nodeup, Node} ->
            NewCurrent = [Node | Current],
            case length(NewCurrent) == length(Expected) of
                true ->
                    io:format("Cluster fully connected~n"),
                    heal_partition();
                false ->
                    ok
            end,
            monitor_loop(Expected, NewCurrent);

        {nodedown, Node} ->
            NewCurrent = lists:delete(Node, Current),
            io:format("Partition detected: ~p~n", [Node]),
            monitor_loop(Expected, NewCurrent)
    end.

heal_partition() ->
    %% Synchronize state after partition heals
    global:sync(),
    ok.

%% Consensus with majority
-module(consensus).
-export([propose/2, vote/3]).

propose(Nodes, Value) ->
    Ref = make_ref(),
    [Node ! {vote, self(), Ref, Value} || Node <- Nodes],
    collect_votes(Ref, length(Nodes), 0).

collect_votes(_Ref, Total, Votes) when Votes > Total div 2 ->
    {ok, majority};
collect_votes(_Ref, Total, Total) ->
    {error, no_majority};
collect_votes(Ref, Total, Votes) ->
    receive
        {vote, Ref, accept} ->
            collect_votes(Ref, Total, Votes + 1);
        {vote, Ref, reject} ->
            collect_votes(Ref, Total, Votes)
    after 5000 ->
        {error, timeout}
    end.

vote(From, Ref, Value) ->
    Decision = evaluate_proposal(Value),
    From ! {vote, Ref, Decision}.

evaluate_proposal(_Value) -> accept.

Partition handling strategies maintain system availability during network failures.

Best Practices

  1. Use short names for local clusters and long names for internet-wide distribution

  2. Set same cookie on all nodes in trusted cluster for security

  3. Monitor node connections to detect and handle network partitions

  4. Use global registration sparingly as it adds coordination overhead

  5. Implement partition detection and healing strategies for resilience

  6. Design for eventual consistency in distributed systems accepting CAP limitations

  7. Use RPC for simple calls but prefer message passing for complex protocols

  8. Test with network failures using tools like toxiproxy or chaos engineering

  9. Implement proper timeouts on distributed calls to handle slow networks

  10. Use distributed supervision to maintain fault tolerance across nodes

Show full SKILL.md (184 more words)Show less

Common Pitfalls

  1. Not setting cookies prevents nodes from connecting causing silent failures

  2. Using global registry everywhere creates single point of failure and bottleneck

  3. Not handling node disconnection causes processes to hang indefinitely

  4. Assuming network reliability leads to incorrect behavior during partitions

  5. Using long timeouts in RPC calls causes cascading delays during failures

  6. Not testing network partitions misses critical failure modes

  7. Forgetting to synchronize global registry after partition heals

  8. Using same node name on multiple machines causes conflicts

  9. Not monitoring node health prevents detecting degraded cluster state

  10. Relying on strict consistency in distributed setting violates CAP theorem

When to Use This Skill

Apply distribution when building systems requiring high availability and fault tolerance.

Use distributed supervision for critical services needing automatic failover.

Leverage multiple nodes for horizontal scalability beyond single machine limits.

Implement distributed systems when geographic distribution provides latency benefits.

Use clustering for load distribution across multiple servers.

Apply distribution patterns for building resilient microservices architectures.

Resources

© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution of benchflow-ai/skillsbench.

Open the folder on GitHubat commit 9a1f4dd

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in benchflow-ai/skillsbench, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Erlang Distribution next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Erlang Distribution compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Erlang Distribution this skillbenchflow-ai/skillsbench1.8k1 repos~3.3kAutomated safety check: PassApache-2.0
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Node Connectopenclaw/openclaw392k—~1.6kAutomated safety check: PassMIT
Distributed Triagepytorch/pytorch104k—~2.8kAutomated safety check: PassCustom licence
Nutrient Document Processingaffaan-m/ECC275k4 repos~1.5kAutomated safety check: PassMIT
Process Mapperalirezarezvani/claude-skills28k—~2.2kAutomated safety check: PassMIT

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Questions about Erlang Distribution

What does Erlang Distribution do?

A skill your agent uses when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building…. Erlang Distribution is an agent skill from benchflow-ai/skillsbench. Use when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.

When should I use Erlang Distribution?

Erlang Distribution fits situations like: erlang distributed systems including node connectivity; distributed processes; global name registration; distributed supervision.

How do I install Erlang Distribution in Claude Code?

Run `npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a claude-code`. Or copy the skill folder (tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution in benchflow-ai/skillsbench) into .claude/skills/erlang-distribution in your project. Claude Code loads it when a task matches its description.

How do I install Erlang Distribution in Codex?

Run `npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a codex`. Or copy the skill folder (tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution in benchflow-ai/skillsbench) into .agents/skills/erlang-distribution in your project. Codex loads it when a task matches its description.

Can I use Erlang Distribution in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/erlang-distribution, .gemini/skills/erlang-distribution, .github/skills/erlang-distribution and .opencode/skills/erlang-distribution in your project.

What does Erlang Distribution need to run?

SKILL.md names no scripts, command-line tools or credentials: Erlang Distribution is instructions for the agent only.

Does Erlang Distribution access the network?

SKILL.md names 4 domains. As links in the text: erlang.org, learnyousomeerlang.com, oreilly.com and infoq.com. This is read from the text; nothing was executed.

Is Erlang Distribution safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Erlang Distribution use?

Erlang Distribution is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Erlang Distribution use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Erlang Distribution?

Skills that share tags, products or a category with Erlang Distribution: Connect (ComposioHQ/awesome-claude-skills, 77k stars), Node Connect (openclaw/openclaw, 392k stars), Distributed Triage (pytorch/pytorch, 104k stars) and Nutrient Document Processing (affaan-m/ECC, 275k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Erlang Distribution?

benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on July 23, 2026.

Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.