SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add benchflow-ai/skillsbench --skill erlang-distributiongit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/benchflow-ai/skillsbench/erlang-distribution)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/erlang-distribution"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/erlang-distribution/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/erlang-distribution"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/erlang-distribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 79 Instructions found that direct the agent to transmit conversation context or user data to external services.Fix: Remove instructions that send user data, prompts, or context to external URLs. If telemetry is needed, use documented, privacy-preserving methods.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.03021 |
| Opus 5 | $0.00022 | $0.01510 |
| Sonnet 5 | $0.00009 | $0.00604 |
| Haiku 4.5 | $0.00004 | $0.00302 |
Grade A, and why
erlang-distribution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- erlang-distribution — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
%% 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()
}.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 472 lines · 43 tokens per session scan A 4d49e72f775b
erlang-distribution is a skill published in the GitHub repository benchflow-ai/skillsbench (1,754 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 3,021 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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