openai-symphony-autonomous-agents▌
aradotso/trending-skills · updated Apr 8, 2026
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Skill by ara.so — Daily 2026 Skills collection.
OpenAI Symphony
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.
What Symphony Does
- Monitors a work tracker (e.g. Linear) for tasks
- Spawns isolated agent runs per task (using Codex or similar)
- Each agent implements the task, opens a PR, and provides proof of work
- Engineers review outcomes, not agent sessions
- Works best in codebases using harness engineering
Installation Options
Option 1: Ask an agent to build it
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
Option 2: Use the Elixir reference implementation
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
Elixir Reference Implementation Setup
Requirements
- Elixir + Mix installed
- An OpenAI API key (for Codex agent)
- A Linear API key (if using Linear integration)
- A GitHub token (for PR operations)
Environment Variables
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
Install Dependencies
cd elixir
mix deps.get
Configuration (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
Run Symphony
mix symphony.start
# or in IEx for development
iex -S mix
Core Concepts
Isolated Implementation Runs
Each task gets its own isolated run:
- Fresh git branch per task
- Agent operates only within that branch
- No shared state between runs
- Proof of work collected before PR merge
Proof of Work
Before a PR is accepted, Symphony collects:
- CI/CD pipeline status
- PR review feedback
- Complexity analysis
- (optionally) walkthrough videos
Key Elixir Modules & Patterns
Starting the Symphony supervisor
# 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
Defining a Task (Symphony Task struct)
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
Spawning an Agent Run
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
Linear Integration — Polling for Tasks
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.How to use openai-symphony-autonomous-agents on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add openai-symphony-autonomous-agents
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches openai-symphony-autonomous-agents from GitHub repository aradotso/trending-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate openai-symphony-autonomous-agents. Access the skill through slash commands (e.g., /openai-symphony-autonomous-agents) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★47 reviews- ★★★★★Sakura Reddy· Dec 28, 2024
Keeps context tight: openai-symphony-autonomous-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Pratham Ware· Dec 20, 2024
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Arjun Verma· Dec 20, 2024
I recommend openai-symphony-autonomous-agents for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Anaya Gupta· Dec 4, 2024
Registry listing for openai-symphony-autonomous-agents matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★William Bansal· Nov 23, 2024
openai-symphony-autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Sakura Jain· Nov 19, 2024
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Sakshi Patil· Nov 11, 2024
Keeps context tight: openai-symphony-autonomous-agents is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Aarav Smith· Nov 11, 2024
Useful defaults in openai-symphony-autonomous-agents — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sophia Abebe· Oct 14, 2024
openai-symphony-autonomous-agents is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aarav Thomas· Oct 10, 2024
openai-symphony-autonomous-agents fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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