Ralph TUI is a terminal interface that repeatedly sends tasks from a task tracker to AI coding agents, monitors their work, and moves through the list autonomously. Developers use it to run agents such as Claude Code, Codex, Cursor CLI, or Gemini CLI across ordered tasks, with persistence and progress visibility; catalogue entries add skills and instructions for that workflow.
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.
npx skills add subsy/ralph-tui --skill ralph-tui-prdgit clone --depth 1 https://github.com/subsy/ralph-tuiWrote 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.
[](https://agentmods.dev/skills/subsy/ralph-tui/ralph-tui-prd)<a href="https://agentmods.dev/skills/subsy/ralph-tui/ralph-tui-prd"><img src="https://agentmods.dev/badge/skills/subsy/ralph-tui/ralph-tui-prd/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.
<a href="https://agentmods.dev/skills/subsy/ralph-tui/ralph-tui-prd"><img src="https://agentmods.dev/badge/skills/subsy/ralph-tui/ralph-tui-prd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.02363 |
| Opus 5 | $0.00035 | $0.01182 |
| Sonnet 5 | $0.00014 | $0.00473 |
| Haiku 4.5 | $0.00007 | $0.00236 |
Grade A, and why
ralph-tui-prd 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph TUI PRD Generator
Create detailed Product Requirements Documents optimized for AI agent execution via ralph-tui.
The Job
- Receive a feature description from the user
- Ask 3-5 essential clarifying questions (with lettered options) - one set at a time
- Always ask about quality gates (what commands must pass)
- After each answer, ask follow-up questions if needed (adaptive exploration)
- Generate a structured PRD when you have enough context
- Output the PRD wrapped in
[PRD]...[/PRD]markers for TUI parsing
Important: Do NOT start implementing. Just create the PRD.
Step 1: Clarifying Questions (Iterative)
Ask questions one set at a time. Each answer should inform your next questions. Focus on:
- Problem/Goal: What problem does this solve?
- Core Functionality: What are the key actions?
- Scope/Boundaries: What should it NOT do?
- Success Criteria: How do we know it's done?
- Integration: How does it fit with existing features?
- Quality Gates: What commands must pass for each story? (REQUIRED)
Format Questions Like This:
1. What is the primary goal of this feature?
A. Improve user onboarding experience
B. Increase user retention
C. Reduce support burden
D. Other: [please specify]
2. Who is the target user?
A. New users only
B. Existing users only
C. All users
D. Admin users only
This lets users respond with "1A, 2C" for quick iteration.
If you are Claude Code in an interactive session, you may present these questions with the AskUserQuestion tool instead - same questions and options, still one set at a time. When this skill is driven by ralph-tui create-prd, the agent runs non-interactively and nothing can answer a tool call, so ask in plain text with lettered options as above.
Quality Gates Question (REQUIRED)
Always ask about quality gates - these are project-specific:
What quality commands must pass for each user story?
A. pnpm typecheck && pnpm lint
B. npm run typecheck && npm run lint
C. bun run typecheck && bun run lint
D. Other: [specify your commands]
For UI stories, should we include browser verification?
A. Yes, use dev-browser skill to verify visually
B. No, automated tests are sufficient
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.
- 5d ago Changed · +2 lines 8127004f3332
- 9d ago First seen · 328 lines · 70 tokens per session scan A 9534f7cdf173
ralph-tui-prd is a skill published in the GitHub repository subsy/ralph-tui (2,437 stars, last pushed 4d ago), licensed MIT. It adds 70 tokens to every session and 2,363 once invoked, about $0.0003 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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