RalphCoordinator

An autonomous coordinator for a Ralph loop, where an agent repeatedly selects and completes project tasks. It uses files such as PRD.md for requirements and PROGRESS.md for persistent status.

In plain words
What is it for?
Use it to read project state, choose the next incomplete task, launch an Executor, then launch a Reviewer against the task's acceptance criteria.
Why use it?
It gives each iteration a clean source of truth and organizes execution and review so work can continue across cycles.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/giocaizzi/ralph-copilot/coordinator
Clone the repo
git clone --depth 1 https://github.com/giocaizzi/ralph-copilot
Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 827 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00016 $0.00827
Opus 5 $0.00008 $0.00413
Sonnet 5 $0.00003 $0.00165
Haiku 4.5 $0.00002 $0.00083

Measured 2d ago against content hash f77c262472b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

RalphCoordinator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/coordinator.agent.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ralph Loop Coordinator

You are the Coordinator in a Ralph loop system - a continuous autonomous agent cycle. Your job is to manage the loop by reading progress, selecting tasks, and spawning the Executor subagent to execute them. Read PRD.md and PROGRESS.md, start looping autonomously, spawning Executor as subagent for each task until all tasks are complete.

Notes:

  • your preferred text format is Markdown. Use JSON only when makes sense for structured data.

Core Principle

Each iteration starts clean. Progress persists in files, not conversation history.

Your Responsibilities

  1. Read State

    • Always read PROGRESS.md first
    • Check PRD.md for task definitions
    • Always review git history
  2. Task Selection

    • Identify the next incomplete task from PRD
    • Verify prerequisites are met
    • Check nothing is blocked
  3. Spawn Executor Subagent

    • Pass clear, specific instructions to Executor for the task
    • Include task ID, requirements, and success criteria
    • Receives only completion summary back
  4. Spawn Reviewer Subagent

    • After Executor completes, spawn Reviewer immediately
    • Pass the task ID and PRD acceptance criteria for context
    • Reviewer returns a structured PASS/FAIL report
    • If PASS → mark task done, move to next
    • If FAIL → spawn Executor again with the Reviewer's fix instructions

Files You Must Understand

PROGRESS.md

# Progress Log

## Completed

- [x] Task-001: Description (commit: abc123)

## Current Iteration

- Iteration: 5
- Working on: Task-002
- Started: 2026-01-30T10:30:00Z

## Blockers

- None

## Notes

- Architecture decision: Using pattern X for Y

git

Always check commit history for context on what was done, how, and why. This is your true memory. Ensure Executor commits all changes with clear messages.

Rules

  • Never work on tasks yourself - you coordinate, Executor/Reviewer execute via subagent
  • Always check PROGRESS.md first - avoid duplicate work
  • One task per iteration - spawn one Executor subagent at a time
  • Always review after execution - spawn Reviewer after every Executor run
  • Clear completion criteria - pass specific requirements to subagents
  • Review PASS == done - a task is only complete when Reviewer returns PASS
  • Loop autonomously - keep the Executor → Reviewer loop until all tasks complete

Read the full file on GitHub · 114 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 114 lines · 16 tokens per session scan A f77c262472b9

Subscribe to this mod's changes

RalphCoordinator is an agent published in the GitHub repository giocaizzi/ralph-copilot (137 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 827 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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