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.
git clone --depth 1 https://github.com/samqin123/Claude_skill_poolWrote 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/commands/samqin123/claude_skill_pool/ralph-yolo)<a href="https://agentmods.dev/commands/samqin123/claude_skill_pool/ralph-yolo"><img src="https://agentmods.dev/badge/commands/samqin123/claude_skill_pool/ralph-yolo.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00018 | $0.02603 |
| Opus 5 | $0.00009 | $0.01301 |
| Sonnet 5 | $0.00004 | $0.00521 |
| Haiku 4.5 | $0.00002 | $0.00260 |
Grade A, and why
ralph-yolo 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 8d 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph YOLO - 自主 Agent 循环(无 Amp 依赖)
Ralph YOLO 是 Ralph 的轻量版本,直接使用 Claude Code 的 Task tool 管理子 agent 完成任务,无需 Amp CLI。
使用方法
/ralph-yolo [path-to-prd.md]
示例:
/ralph-yolo tasks/prd-my-feature.md # 从 PRD 转换并执行
/ralph-yolo prd.json # 使用现有 prd.json 执行
/ralph-yolo # 自动查找 prd.json
Phase 1: PRD 转换(如果提供 markdown PRD)
Step 1: 检查输入
检查用户是否提供了 PRD 文件路径。如果没有,查找项目根目录的 prd.json。
用户输入格式:
/ralph-yolo tasks/prd-my-feature.md- 转换 PRD 并运行 Ralph YOLO/ralph-yolo prd.json- 使用现有 prd.json 运行/ralph-yolo- 运行(查找 prd.json)
Step 2: 归档旧运行(如需要)
检查是否存在 prd.json 且 branchName 不同。如果是:
- 读取当前
prd.json提取branchName - 比较新功能的分支名
- 如果不同且
prd-progress.txt有内容:- 创建归档文件夹:
.claude/archive/YYYY-MM-DD-[feature-name]/ - 复制当前
prd.json和prd-progress.txt到归档 - 重置
prd-progress.txt为新的头部信息
- 创建归档文件夹:
Step 3: 转换为 prd.json
解析 PRD 并生成项目根目录的 prd.json:
{
"project": "[从 PRD 提取或自动检测的项目名]",
"branchName": "ralph/[feature-name-kebab-case]",
"description": "[从 PRD 提取的功能描述]",
"userStories": [
{
"id": "US-001",
"title": "[故事标题]",
"description": "As a [用户], I want [功能] so that [收益]",
"acceptanceCriteria": [
"标准 1",
"标准 2",
"Typecheck passes"
],
"priority": 1,
"passes": false,
"notes": ""
}
]
}
转换规则
- 每个用户故事成为一个 JSON 条目
- ID:顺序编号(US-001, US-002 等)
- Priority:基于依赖顺序(schema → backend → UI)
- 所有故事:初始
passes: false,notes为空 - branchName:从功能名派生,kebab-case,前缀
ralph/ - 始终添加:"Typecheck passes" 到每个故事
- UI 故事:添加 "Verify in browser"
故事大小关键原则
每个故事必须在一个上下文窗口内完成。
合适大小:
- 添加数据库列和迁移
- 向现有页面添加 UI 组件
- 更新服务器操作
- 添加过滤下拉菜单
太大(需拆分):
- "构建整个仪表板" → 拆分为:schema, queries, UI 组件, filters
- "添加认证" → 拆分为:schema, middleware, 登录 UI, session handling
经验法则: 如果不能用 2-3 句话描述变更,就太大了。
故事排序
故事按 priority 顺序执行。前面的故事不能依赖后面的。
正确顺序:
- Schema/数据库变更(migrations)
- Server actions / 后端逻辑
- 使用后端的 UI 组件
- 汇总数据的 Dashboard/summary 视图
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.
- 8d ago First seen · 347 lines · 18 tokens per session scan A 2754e6b4c2b8
ralph-yolo is a command published in the GitHub repository samqin123/Claude_skill_pool (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 2,603 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.