Skill by ara.so — Daily 2026 Skills collection.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionopenai-symphony-autonomous-agentsExecute the skills CLI command in your project's root directory to begin installation:
Fetches openai-symphony-autonomous-agents from aradotso/trending-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate openai-symphony-autonomous-agents. Access via /openai-symphony-autonomous-agents in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Skill by ara.so — Daily 2026 Skills collection.
Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents. Instead of watching an agent code, you define tasks (e.g. in Linear), and Symphony spawns agents that complete them, provide proof of work (CI status, PR reviews, walkthrough videos), and land PRs autonomously.
Paste this prompt into Claude Code, Cursor, or Codex:
Implement Symphony according to the following spec:
https://github.com/openai/symphony/blob/main/SPEC.md
git clone https://github.com/openai/symphony.git
cd symphony/elixir
Follow elixir/README.md, or ask an agent:
Set up Symphony for my repository based on
https://github.com/openai/symphony/blob/main/elixir/README.md
export OPENAI_API_KEY="sk-..." # OpenAI API key for Codex
export LINEAR_API_KEY="lin_api_..." # Linear integration
export GITHUB_TOKEN="ghp_..." # GitHub PR operations
export SYMPHONY_REPO_PATH="/path/to/repo" # Target repository
cd elixir
mix deps.get
elixir/config/config.exs)import Config
config :symphony,
openai_api_key: System.get_env("OPENAI_API_KEY"),
linear_api_key: System.get_env("LINEAR_API_KEY"),
github_token: System.get_env("GITHUB_TOKEN"),
repo_path: System.get_env("SYMPHONY_REPO_PATH", "./"),
poll_interval_ms: 30_000,
max_concurrent_agents: 3
mix symphony.start
# or in IEx for development
iex -S mix
Each task gets its own isolated run:
Before a PR is accepted, Symphony collects:
# In your application.ex or directly
defmodule MyApp.Application do
use Application
def start(_type, _args) do
children = [
Symphony.Supervisor
]
Supervisor.start_link(children, strategy: :one_for_one)
end
end
defmodule Symphony.Task do
@type t :: %__MODULE__{
id: String.t(),
title: String.t(),
description: String.t(),
source: :linear | :manual,
status: :pending | :running | :completed | :failed,
branch: String.t() | nil,
pr_url: String.t() | nil,
proof_of_work: map() | nil
}
defstruct [:id, :title, :description, :source,
status: :pending, branch: nil,
pr_url: nil, proof_of_work: nil]
end
defmodule Symphony.AgentRunner do
@doc """
Spawns an isolated agent run for a given task.
Each run gets its own branch and Codex session.
"""
def run(task) do
branch = "symphony/#{task.id}-#{slugify(task.title)}"
with :ok <- Git.create_branch(branch),
{:ok, result} <- Codex.implement(task, branch),
{:ok, pr_url} <- GitHub.open_pr(branch, task),
{:ok, proof} <- ProofOfWork.collect(pr_url) do
{:ok, %{task | status: :completed, pr_url: pr_url, proof_of_work: proof}}
else
{:error, reason} -> {:error, reason}
end
end
defp slugify(title) do
title
|> String.downcase()
|> String.replace(~r/[^a-z0-9]+/, "-")
|> String.trim("-")
end
end
defmodule Symphony.Linear.Poller do
use GenServer
@poll_interval Application.compile_env(:symphony, :poll_interval_ms, 30_000)
def start_link(opts \\ []) do
GenServer.start_link(__MODULE__, opts, name: __MODULE__)
end
def init(_opts) do
schedule_poll()
{:ok, %{processed_ids: MapSet.new()}}
end
def handle_info(:poll, state) do
case Symphony.Linear.Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
aradotso/trending-skills
aradotso/trending-skills
aradotso/trending-skills
davila7/claude-code-templates
intellectronica/agent-skills
am-will/codex-skills
Keeps context tight: openai-symphony-autonomous-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend openai-symphony-autonomous-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for openai-symphony-autonomous-agents matched our evaluation — installs cleanly and behaves as described in the markdown.
openai-symphony-autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: openai-symphony-autonomous-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in openai-symphony-autonomous-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
openai-symphony-autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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