Ship PRD stories via dual-agent loop
Ralph runs a generator and evaluator in tandem until all user stories pass acceptance criteria and browser tests.
# Ralph Harness — Agent Instructions ## Overview Ralph Harness is an autonomous AI agent loop that runs AI coding tools (Amp or Claude Code) repeatedly until all PRD items are complete. Each iteration is a fresh instance with clean context. Ralph supports two modes: | Mode | Architecture | When to use | |------|-------------|-------------| | simple | Single agent (self-implement, self-check) | Quick tasks, backend-only stories, well-defined small changes | | harness | Generator + Evaluator (dual-agent with contract) | UI-heavy features, complex stories, when quality is critical | ## Architecture: Harness Mode ralph.sh orchestrator │ ├── Planner (prd.json) │ Defines user stories, acceptance criteria, dependencies │ ├── Generator (generator-prompt.md) │ Drafts sprint contracts → Implements stories → Fixes based on feedback │ └── Evaluator (evaluator-prompt.md) Reviews contracts → Signs/locks → Tests in browser → Scores → Writes feedback ### Per-Story Flow 1. Contract Negotiation : Generator drafts contract.json → Evaluator reviews → Back-and-forth until Evaluator signs → Contract locked (immutable) 2. Build : Generator reads
claude-code
More automation loops
Run iterative refactor tasks with RALPH
Run an agent repeatedly on a single refactoring task, persisting filesystem state between iterations until tests pass.
#!/usr/bin/env python3 """ RALPH Loop Runner for RefactorBench. Runs iterative agent retry loops with filesystem-based memory on a SINGLE task at a time. RALPH pattern: - Agent runs, does work, exits - Work persists on the filesystem (working directory retains all changes) - Fresh agent starts, reads progress.json from the working directory, continues - Repeats until tests pass or max iterations Usage: python3 ralph runner.py --repo django refactor --task add-log-parameter-get-resolver python3 ralph runner.py --repo django refactor --task add-log-parameter-get-resolver --chains 2 --iterations 3 python3 ralph runner.py --repo django refactor --task add-log-parameter-get-resolver --verbose """ import argparse import asyncio import json import os import re import shutil import sys import time from dataclasses import dataclass from datetime import datetime from pathlib import Path from refactor agent import get task info, run test, setup workdir from notebook import ( FileSnapshot, NotebookWriter, parse stream json, compute solution diff, compute diff stats, ) import ralph prompt builder BENCH ROOT = Path( file ).parent / ".refactorbench" # --------------
automationmedium risk
Draft weekly release notes
Every Friday at 2pm, draft release notes from merged PRs into releases/DRAFT.md and verify all links resolve.
/schedule every Friday at 2pm, draft release notes from the PRs merged since the last draft: group changes into features, fixes, and docs, write one plain-English line per change with the PR link, and save to releases/DRAFT.md. Draft only — a human edits and publishes; never post or send anywhere. Verify every PR link resolves and every merged PR since the last run is covered before saving. Stop after 1 pass per run.
automationlow risk
Ship stories with Ralph
Ralph executes one story per iteration, reading PRD state and committing work until the backlog clears.
# Ralph  Ralph is a minimal, file‑based agent loop for autonomous coding. Each iteration starts fresh, reads the same on‑disk state, and commits work for one story at a time. ## How it works Ralph treats files and git as memory, not the model context: - PRD (JSON) defines stories, gates, and status - Loop executes one story per iteration - State persists in .ralph/  ## Global CLI (recommended) Install and run Ralph from anywhere: bash npm i -g @iannuttall/ralph ralph prd # launches an interactive prompt ralph build 1 # one Ralph run ### Template hierarchy Ralph will look for templates in this order: 1. .agents/ralph/ in the current project (if present) 2. Bundled defaults shipped with this repo State and logs always go to .ralph/ in the project. ### Install templates into a project (optional overrides) bash ralph install This creates .agents/ralph/ in the current repo so you can customize prompts and loop behavior. During install, you’ll be asked if you want to add the required skills. ### Install required skills (optional) bash ralph install --skills You’ll be prom
automationmedium risk