ralph-loop-yylo
Has your agent perform a specific Ralph system task. Use only if the user explicitly asks to perform this task.
What it does
This tool executes one specific, already assigned task within the 'Ralph' system. It ensures only that task is done, without deviations, and prepares it for review and approval.
When to use it
- ✓When you need to execute a single, already defined task in the 'Ralph' system.
- ✓If you want to ensure only the designated task is completed, without adding or modifying others.
- ✓To prepare a task for review and approval within the established workflow.
Example of what to ask your AI
“Execute task `TASK_ID_XYZ` in the Ralph loop.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool focuses on a single task you've already indicated. It follows the exact instructions to complete that task and leaves it ready for the next step in the process, such as a review.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill ralph-loop-yyloOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/ralph-loop-yylo/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Execute one assigned task in the Ralph loop
Read references/implement.md completely and follow it.
Stay within the assigned task. Do not select unrelated work, edit tasks.md, auto-tag releases, push, deploy, mutate production, or broaden scope because another issue is noticed. Record a bounded related Kanban follow-up when necessary.
Keep durable instructions concise and evidence-backed. Status belongs in the task response and runtime receipts, not AGENTS.md.
Controller checkpoints are best-effort local durability warnings after terminal metadata is durable. They never gate yy pi, yy task, yy merge, product commits, candidates, or releases.
Complete assigned request
Treat the following as the complete user-assigned request. Preserve task references and directives literally; resolve them only through the normal agent workflow.
$ARGUMENTS
When to Use
- The user explicitly requests
ralph-loop-yylofor one already-assigned YYLO Ledger task. - You need to implement exactly that task through the validated loop to a queued, review-ready commit.
Limitations
- Exactly one assigned task per run: never select unrelated work, broaden scope, push, deploy, merge, release, or mutate production.
- Requires
yy task start TASK_IDadmission andyy task finish TASK_IDclosure; stop after queueing - only the target owner runsyy merge land. - Docs-only import: the upstream
scripts/kanban.shwrapper is intentionally not bundled;references/holds the worker contract. - Controller checkpoints are best-effort durability warnings, never lifecycle gates.
Example
yy task start TASK_ID
yy task preflight TASK_ID
yy task finish TASK_ID
Adapted from yylo-dev/yylo-skills (MIT) - v2.0.1; frontmatter, When to Use/Limitations, and safety boundaries added for upstream compliance. Docs-only import:
scripts/kanban.shruntime not bundled.
FAQ
What is the 'Ralph loop'?
It's an internal system used to manage and execute development tasks in an organized and controlled manner.
Can I ask it to do multiple tasks at once?
No, this tool is designed to execute exactly one task at a time to ensure focus and control.
What happens after the task is executed?
The task is prepared and placed in a queue for review and approval by another person or system.
Details
Original description by the author
Execute exactly one explicitly assigned YYLO Ledger task through the Ralph loop to a validated queued commit. Use only when the user explicitly requests ralph-loop-yylo.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3954
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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