Symphony workflow automation tool works with Claude Code

Symphony workflow automation with Claude Code
A developer successfully implemented the Symphony specification using Claude Code, creating an automated workflow that handles tickets from issue tracking through to pull requests.
How the workflow works
The Symphony tool follows this sequence:
- Raise a ticket in Linear (can be swapped for JIRA or GitHub Issues)
- Move the ticket from backlog to TODO
- Symphony picks up the ticket, checks out a branch, codes the work, and creates a PR
- You review and merge
- Symphony GUI shows status as the job runs
Implementation details
The developer used Node and TypeScript for the initial implementation but noted: "I quickly realized why OpenAI reference implementation used Elixir. This needs a language like Elixir." They plan to switch to Elixir, seeing it as "a good excuse to learn."
Setup requirements
Key setup considerations:
- You need to set up API keys and token billing for this to work
- This does not use your Claude Code Max or Pro subscription (or the developer didn't find a way to make it work)
- This billing setup might be a reason not to use it liberally
Tool preferences
The developer prefers GitHub Issues over other issue tracking systems, stating: "I will probably use Github issues (not keen on another issue tracking system)."
The code is available at: https://github.com/obelix74/symphony
📖 Read the full source: r/ClaudeAI
👀 See Also

Blackwell LLM Toolkit: NVFP4 Configs, Wheels, and Benchmarks for TensorRT-LLM on RTX Pro 6000
A community repo provides TensorRT-LLM configs, prebuilt LMCache wheels with sm_120 support, and benchmarks for Blackwell GPUs. Nemotron-3-Nano-Omni V3 hits 270 tok/s at 8k context on a single RTX Pro 6000.

Improving Claude Code Sessions with claude-self-improve
Claude-self-improve is a CLI tool that enhances Claude Code's AI performance by analyzing session data and updating memory files automatically.

Parallel Sub-Agents in Claude Code: When They Save vs. Burn Tokens
Anthropic reports multi-agent systems use ~15× more tokens than a single chat, but prompt caching offers 90% discount on tokens. Whether sub-agents save or burn money depends on cache hit rates.

Community-voted Model Leaderboard for OpenClaw Released
A new community-voted leaderboard for models compatible with OpenClaw is now available, with Opus 4.5 currently leading.