step-by-step-cli 0.1.0

LLM Development Pipeline UI - GitHub Actions-style visual dashboard for LLM-driven workflows

Step-by-Step

A terminal UI that runs your development tasks through a structured multi-agent pipeline. You describe what you want to build; a team of specialized Claude agents plans, implements, tests, reviews, and opens a pull request — autonomously.

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How it works

Step-by-Step models software delivery as a linear pipeline of specialized agents, each owning a single responsibility. Stages that can be parallelized fan out into independent worker agents that run concurrently, then merge their results before the next stage begins.

Plan ──● Decomp ──● Impl ⇶ ──● Tests ⇶ ──● Quality ──● Docs ──● PR

⇶ = parallel workers · ● = single agent

Pipeline stages

Stage	Mode	What it does
Planning	Single agent	Senior architect reads your codebase and produces a concrete, numbered implementation plan
Decomposition	Manager agent	Splits the plan into independent subtasks that can be worked on simultaneously
Implementation	Parallel workers	Each subtask is handed to a dedicated worker agent; all workers run concurrently
Tests & Validation	Parallel workers	QA agents write and run tests per subtask; surface failures via ## Issues Found
Code Quality	Single agent	Reviewer checks for code smells, security issues, and readability
Documentation	Single agent	Generates or updates README sections, docstrings, and API reference
Commit & PR	Single agent	Writes conventional commits and opens a GitHub Pull Request

Refinement loops

Claude drives two autonomous feedback loops — it decides when to stop by reporting ## Issues Found: None.

• Test loop — cycles through Implementation → Tests & Validation until no issues remain
• Quality loop — re-decomposes and re-implements until Code Quality is satisfied

RAM-based flow control

Worker concurrency is not capped by a fixed number. Instead, the pipeline uses TCP-style flow control: a new worker starts only when system RAM is below 75%. Starts are serialized and include a post-start delay so the OS can register each new process's footprint before the next candidate is evaluated. When RAM is high, new workers queue up and resume as running workers release memory.

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Requirements

• Python 3.13+
• uv (recommended) or pip
• Claude Code CLI — npm install -g @anthropic-ai/claude-code
• GitHub CLI — required for the Commit & PR stage (gh auth login)

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Installation

git clone https://github.com/ValentinDutra/step-by-step.git
cd step-by-step
uv sync

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Usage

# Run against the current directory
uv run pipeline

# Run against a specific repository
uv run pipeline /path/to/your/repo

# Load a prompt from a file and start immediately
uv run pipeline /path/to/your/repo -f prompt.txt

Type your task in the input area at the bottom and press Ctrl+Enter to start.

Keyboard shortcuts

Key	Action
Ctrl+Enter	Submit prompt and run the pipeline
Ctrl+L	Clear the activity log
Ctrl+E	Export log to pipeline_log_<timestamp>.txt
Ctrl+C	Quit

Re-running from a specific stage

Once a run completes, every stage pill in the header becomes clickable. Click any stage to re-run from that point forward, reusing all prior context — useful for retrying a failed stage or iterating on implementation without re-planning.

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UI layout

┌─────────────────────────────────────────────────────────────────┐
│  Plan  │  Decomp  │  Impl ⇶  │  Tests ⇶  │  Quality  │  PR    │  ← stage bar
├─────────────────────────────────────────────────────────────────┤
│  > Describe your task…                                          │  ← prompt input
├──────────────────────────────┬──────────────────────────────────┤
│  ● Planning                  │                                  │
│                              │         Activity log             │
│      Streaming pane          │   (full chronological history)   │
│  (live output from active    │                                  │
│   stage or worker)           │                                  │
├──────────────────────────────┴──────────────────────────────────┤
│  ^p palette  ^l Clear Log  ctrl+↵ Run  ^e Export Log  ^m Monitor        Calls: 4  |  Cost: $0.0234  |  Time: 1m 20s  │
└─────────────────────────────────────────────────────────────────┘

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Project structure

app/
├── __main__.py      Entry point
├── models.py        Shared data classes (Task, WorkerResult, PipelineStats)
├── agents.py        Claude CLI invocation, flow control, and worker coordination
├── stages.py        Stage definitions, prompt templates, and pipeline configuration
├── pipeline.py      Stage runners: run_stage() and run_stage_parallel()
├── runner.py        PipelineRunnerMixin: run_pipeline() and rerun_from_stage()
├── widgets.py       StagePill TUI widget and display constants
├── git.py           Git/gh subprocess helpers and Commit & PR stage runner
└── tui.py           PipelineApp (Textual App) and main() entry point

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Safety

The pipeline invokes claude --dangerously-skip-permissions so agents can read and write files autonomously. Only point it at repositories where you trust the output. Always review the diff before the PR stage commits.

Each subprocess is run with a 10-minute timeout and cleaned up unconditionally on exit — even on errors or cancellation — so stalled Claude processes do not accumulate.

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License

MIT © Valentin Dutra — see LICENSE