ralph

ralph is a skill for Claude Code, Codex from konveyor-ecosystem/playpen-pf-mig-skills. It costs 64 tokens per session (1,811 once invoked), scanned A, a copy of ralph, Apache-2.0.

A converter that turns a product requirements document, or PRD, into the JSON file format used by the Ralph autonomous agent system. A PRD is a written description of a feature, its requirements, and how it should be accepted.

In plain words
What is it for?
Use it when you already have a PRD and need a `prd.json` containing the project details, branch name, user stories, criteria, priorities, and completion status.
Why use it?
Ralph needs work divided into small user stories with priorities and acceptance criteria. This converts an existing plan into that structure so Ralph can process it one iteration at a time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when you already have a PRD and need a prd.json containing the project details, branch name, user stories, criteria, priorities, and completion status.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph
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.

Any agent
npx skills add konveyor-ecosystem/playpen-pf-mig-skills --skill ralph
Clone the repo
git clone --depth 1 https://github.com/konveyor-ecosystem/playpen-pf-mig-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ralph

README.md
[![agentmods](https://agentmods.dev/badge/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph/github.svg)](https://agentmods.dev/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph)
Your own site
<a href="https://agentmods.dev/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph"><img src="https://agentmods.dev/badge/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ralph

Your own site · 80×15
<a href="https://agentmods.dev/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph"><img src="https://agentmods.dev/badge/skills/konveyor-ecosystem/playpen-pf-mig-skills/ralph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,811 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 92% copy Near-identical to another mod 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 5.1 $0.00064 $0.01811
Opus 5 $0.00032 $0.00905
Sonnet 5 $0.00013 $0.00362
Haiku 4.5 $0.00006 $0.00181

Measured 12d ago against content hash 0dac03f03bda, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ralph 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 12d 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.

Origin

This is a copy

92% identical to ralph — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

ralph/skills/ralph/SKILL.md · 258 lines

How it starts

The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ralph PRD Converter

Converts existing PRDs to the prd.json format that Ralph uses for autonomous execution.


The Job

Take a PRD (markdown file or text) and convert it to prd.json in your ralph directory.


Output Format

{
  "project": "[Project Name]",
  "branchName": "ralph/[feature-name-kebab-case]",
  "description": "[Feature description from PRD title/intro]",
  "userStories": [
    {
      "id": "US-001",
      "title": "[Story title]",
      "description": "As a [user], I want [feature] so that [benefit]",
      "acceptanceCriteria": [
        "Criterion 1",
        "Criterion 2",
        "Typecheck passes"
      ],
      "priority": 1,
      "passes": false,
      "notes": ""
    }
  ]
}

Story Size: The Number One Rule

Each story must be completable in ONE Ralph iteration (one context window).

Ralph spawns a fresh Amp instance per iteration with no memory of previous work. If a story is too big, the LLM runs out of context before finishing and produces broken code.

Right-sized stories:

  • Add a database column and migration
  • Add a UI component to an existing page
  • Update a server action with new logic
  • Add a filter dropdown to a list

Too big (split these):

  • "Build the entire dashboard" - Split into: schema, queries, UI components, filters
  • "Add authentication" - Split into: schema, middleware, login UI, session handling
  • "Refactor the API" - Split into one story per endpoint or pattern

Rule of thumb: If you cannot describe the change in 2-3 sentences, it is too big.


Story Ordering: Dependencies First

Stories execute in priority order. Earlier stories must not depend on later ones.

Correct order:

  1. Schema/database changes (migrations)
  2. Server actions / backend logic
  3. UI components that use the backend
  4. Dashboard/summary views that aggregate data

Wrong order:

  1. UI component (depends on schema that does not exist yet)
  2. Schema change

Acceptance Criteria: Must Be Verifiable

Read the full file on GitHub · 258 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. 12d ago First seen · 258 lines · 64 tokens per session scan A 0dac03f03bda

Subscribe to this mod's changes

ralph is a skill published in the GitHub repository konveyor-ecosystem/playpen-pf-mig-skills (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,811 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to ralph, differing in 1 line, and is treated as a copy.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens