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/an8079/take-skillsWrote 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/an8079/take-skills/takes-ralph)<a href="https://agentmods.dev/commands/an8079/take-skills/takes-ralph"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-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.
<a href="https://agentmods.dev/commands/an8079/take-skills/takes-ralph"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-ralph.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00022 | $0.00661 |
| Opus 5 | $0.00011 | $0.00331 |
| Sonnet 5 | $0.00004 | $0.00132 |
| Haiku 4.5 | $0.00002 | $0.00066 |
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 9d 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.
What it actually says
/ralph - Ralph 持久循环模式
持续执行-验证-修复循环，直到 Architect 验证通过才退出。
使用方式
/ralph "任务描述"
或
RALPH
拉尔夫
持久模式
工作流程
┌─────────────────────────────────────┐
│ Ralph Loop │
├─────────────────────────────────────┤
│ │
│ ┌─────────────────────────────┐ │
│ │ Execution Phase │ │
│ │ 代码实现 / 任务执行 │ │
│ └─────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────┐ │
│ │ Ultrawork │ │
│ │ 并行任务执行 │ │
│ └─────────────┬───────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────────────────┐ │
│ │ Verification │ │
│ │ Architect 验证 │ │
│ └─────────────┬───────────────┘ │
│ │ │
│ 验证通过？ │
│ ┌─────┴─────┐ │
│ │ │ │
│ 是 否 │
│ │ │ │
│ 退出 继续循环 │
│ │
└─────────────────────────────────────┘
特点
| 特点 | 说明 |
|---|---|
| 持久循环 | 不断执行-验证-修复直到完成 |
| 包含 Ultrawork | 自动调用 ultrawork 进行并行执行 |
| Architect 验证 | 必须通过 Architect 的验证才退出 |
| 自我迭代 | 每次循环都会改进 |
退出条件
- 通过 Architect 验证 - 任务完成质量达标
- 达到最大循环次数 - 防止无限循环
- 用户手动取消 - 使用
/cancel终止
与 /ultrawork 的关系
| 维度 | /ralph | /ultrawork |
|---|---|---|
| 模式 | 持久循环 | 单独并行执行 |
| 包含 | 包含 ultrawork | 独立运行 |
| 退出 | Architect 验证 | 任务完成 |
| 适用 | 复杂迭代任务 | 高吞吐量任务 |
提示： /ralph 适合需要多次迭代优化的复杂任务，它会一直工作直到达到高质量标准。
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.
- 9d ago First seen · 85 lines · 22 tokens per session scan A 46ed638bc7c5
ralph is a command published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 22 tokens to every session and 661 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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.