Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Converts existing PRDs to the prd.json format that Ralph uses for autonomous execution.
Take a PRD (markdown file or text) and convert it to prd.json in your ralph directory.
{
"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": ""
}
]
}
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.
Rule of thumb: If you cannot describe the change in 2-3 sentences, it is too big.
Stories execute in priority order. Earlier stories must not depend on later ones.
Correct order:
Wrong order:
Each criterion must be something Ralph can CHECK, not something vague.
status column to tasks table with default 'pending'""Typecheck passes"
For stories with testable logic, also include:
"Tests pass"
"Verify in browser using dev-browser skill"
Frontend stories are NOT complete until visually verified. Ralph will use the dev-browser skill to navigate to the page, interact with the UI, and confirm changes work.
passes: false and empty notesralph/If a PRD has big features, split them:
Original:
"Add user notification system"
Split into:
Each is one focused change that can be completed and verified independently.
Input PRD:
# Task Status Feature
Add ability to mark tasks with different statuses.
## Requirements
- Toggle between pending/in-progress/done on task list
- Filter list by status
- Show status badge on each task
- Persist status in database
Output prd.json:
{
"project": "TaskApp",
"branchName": "ralph/task-status",
"description": "Task Status Feature - Track task progress with status indicators",
"userStories": [
{
"id": "US-001",
"title": "Add status field to tasks table",
"description": "As a developer, I need to store task status in the database.",
"acceptanceCriteria": [
"Add status column: 'pending' | 'in_progress' | 'done' (default 'pending')",
"Generate and run migration successfully",
"Typecheck passes"
],
"priority": 1,
"passes": false,
"notes": ""
},
{
"id": "US-002",
"title": "Display status badge on task cards",
"description": "As a user, I want to see task status at a glance.",
"acceptanceCriteria": [
"Each task card shows colored status badge",
"Badge colors: gray=pending, blue=in_progress, green=done",
"Typecheck passes",
"Verify in browser using dev-browser skill"
],
"priority": 2,
"passes": false,
"notes": ""
},
{
"id": "US-003",
"title": "Add status toggle to task list rows",
"description": "As a user, I want to change task status directly from the list.",
"acceptanceCriteria": [
"Each row has status dropdown or toggle",
"Changing status saves immediately",
"UI updates without page refresh",
"Typecheck passes",
"Verify in browser using dev-browser skill"
],
"priority": 3,
"passes": false,
"notes": ""
},
{
"id": "US-004",
"title": "Filter tasks by status",
"description": "As a user, I want to filter the list to see only certain statuses.",
"acceptanceCriteria": [
"Filter dropdown: All | Pending | In Progress | Done",
"Filter persists in URL params",
"Typecheck passes",
"Verify in browser using dev-browser skill"
],
"priority": 4,
"passes": false,
"notes": ""
}
]
}
Before writing a new prd.json, check if there is an existing one from a different feature:
prd.json if it existsbranchName differs from the new feature's branch nameprogress.txt has content beyond the header:
archive/YYYY-MM-DD-feature-name/prd.json and progress.txt to archiveprogress.txt with fresh headerThe ralph.sh script handles this automatically when you run it, but if you are manually updating prd.json between runs, archive first.
Before writing prd.json, verify:
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
日本語の概要は準備中です。原文の説明を表示しています。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
日本語の概要は準備中です。原文の説明を表示しています。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。