Connect
ComposioHQ/awesome-claude-skills
Connect Claude to any app. An agent skill from ComposioHQ/awesome-claude-skills.
A skill your agent uses when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building…
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .claude/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distributionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .agents/skills/erlang-distribution && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .agents/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .cursor/skills/erlang-distribution && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .cursor/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .gemini/skills/erlang-distribution && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .gemini/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench erlang-distributionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .github/skills/erlang-distribution && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .github/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill erlang-distribution -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench erlang-distribution --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution .opencode/skills/erlang-distribution && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "erlang-distribution" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution into .opencode/skills/erlang-distribution/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "erlang-distribution", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
erlang-distributionA 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.
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.
10 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
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.
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.
Links to these hosts (documentation or services it may open):
erlang.orglearnyousomeerlang.comoreilly.cominfoq.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 517 words, ~3,326 tokens.
.claude/skills/erlang-distribution/SKILL.md (or your agent's skills folder).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.
Nodes connect to form clusters for distributed computation and fault tolerance.
%% 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.
Send messages to processes on remote nodes using same syntax as local messaging.
%% 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.
Register process names globally across distributed clusters.
%% 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.
Supervise processes across multiple nodes for cluster-wide fault tolerance.
-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.
Execute function calls on remote nodes with various invocation patterns.
%% 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.
Handle network partitions and understand CAP theorem trade-offs.
%% 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.
Use short names for local clusters and long names for internet-wide distribution
Set same cookie on all nodes in trusted cluster for security
Monitor node connections to detect and handle network partitions
Use global registration sparingly as it adds coordination overhead
Implement partition detection and healing strategies for resilience
Design for eventual consistency in distributed systems accepting CAP limitations
Use RPC for simple calls but prefer message passing for complex protocols
Test with network failures using tools like toxiproxy or chaos engineering
Implement proper timeouts on distributed calls to handle slow networks
Use distributed supervision to maintain fault tolerance across nodes
Not setting cookies prevents nodes from connecting causing silent failures
Using global registry everywhere creates single point of failure and bottleneck
Not handling node disconnection causes processes to hang indefinitely
Assuming network reliability leads to incorrect behavior during partitions
Using long timeouts in RPC calls causes cascading delays during failures
Not testing network partitions misses critical failure modes
Forgetting to synchronize global registry after partition heals
Using same node name on multiple machines causes conflicts
Not monitoring node health prevents detecting degraded cluster state
Relying on strict consistency in distributed setting violates CAP theorem
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.
© 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
Just SKILL.md in tasks/fix-erlang-ssh-cve/environment/skills/erlang-distribution of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Erlang Distribution this skillbenchflow-ai/skillsbench | 1.8k | 1 repos | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| ConnectComposioHQ/awesome-claude-skills | 77k | 3 repos | ~987 | Automated safety check: Pass | None | |
| Node Connectopenclaw/openclaw | 392k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Distributed Triagepytorch/pytorch | 104k | — | ~2.8k | Automated safety check: Pass | Custom licence | |
| Nutrient Document Processingaffaan-m/ECC | 275k | 4 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Process Mapperalirezarezvani/claude-skills | 28k | — | ~2.2k | Automated safety check: Pass | MIT |
ComposioHQ/awesome-claude-skills
Connect Claude to any app. An agent skill from ComposioHQ/awesome-claude-skills.
openclaw/openclaw
Diagnose OpenClaw Control UI browser and native Android, iOS, or macOS node connection failures across route, auth, pairing, QR/setup-code, and reconnect states.
pytorch/pytorch
Sub-triages issues in the oncall:distributed queue by assigning distributed module labels, routing to sub-oncalls, and marking triaged.
affaan-m/ECC
Process, convert, OCR, extract, redact, sign, and fill documents using the Nutrient DWS API.
alirezarezvani/claude-skills
A skill your agent uses when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff…
different-ai/openwork
Search and use the skills, MCP connections, and connected services available through the user's OpenWork organization.
benchflow-ai/skillsbench
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences…
benchflow-ai/skillsbench
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure.
benchflow-ai/skillsbench
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching acopf-math-model.md and MATPOWER branch fields.
benchflow-ai/skillsbench
Civilization 6 district mechanics library. An agent skill from benchflow-ai/skillsbench.
benchflow-ai/skillsbench
Build deterministic, verifiable data visualizations with D3.js (v6).
benchflow-ai/skillsbench
DC power flow analysis for power systems. An agent skill from 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…. 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.
Erlang Distribution fits situations like: erlang distributed systems including node connectivity; distributed processes; global name registration; distributed supervision.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Erlang Distribution is instructions for the agent only.
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.
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.
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.
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.
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.
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.