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/854771076/oh-my-claude-rolesWrote 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/854771076/oh-my-claude-roles/develop-new-feature)<a href="https://agentmods.dev/commands/854771076/oh-my-claude-roles/develop-new-feature"><img src="https://agentmods.dev/badge/commands/854771076/oh-my-claude-roles/develop-new-feature/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/854771076/oh-my-claude-roles/develop-new-feature"><img src="https://agentmods.dev/badge/commands/854771076/oh-my-claude-roles/develop-new-feature.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 | $0.00009 | $0.00428 |
| Opus 5 | $0.00005 | $0.00214 |
| Sonnet 5 | $0.00002 | $0.00086 |
| Haiku 4.5 | $0.00001 | $0.00043 |
Grade A, and why
develop-new-feature 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
按照企业级规范开发新功能
需求描述： $ARGUMENTS
执行步骤：
- 分析功能需求，确定功能类型（页面、API、异步任务等）
- 根据规范定义类型：
- 所有类型优先自动推导，用 Zod 定义运行时 Schema，再通过 z.infer 得到 TS 类型
- 公共类型放在 @/types 目录，禁止重复定义
- 所有函数参数和返回值显式声明类型
- 按照 Next.js 规范创建文件和目录：
- 默认使用服务端组件，仅交互部分抽离为客户端组件
- 服务端写操作使用 Server Actions，禁止客户端直接调用数据库
- 客户端数据获取使用 TanStack Query，合理配置缓存策略
- 表单必须使用 React Hook Form + Zod 校验
- 异步任务使用 BullMQ，禁止在请求链路同步执行
- 开发完成后，编写对应测试：
- 核心业务逻辑编写单元/集成测试，覆盖率不低于 80%
- 核心用户流程编写 E2E 测试
- 本地运行 ESLint 和测试用例，确保全部通过
- 提交代码遵循 Conventional Commits 规范
检查要点：
- 所有代码强类型安全，没有隐式 any，没有非必要类型断言
- 组件分类正确，客户端组件最小化
- 权限控制在服务端实现，禁止仅在客户端做权限判断
- 数据库操作使用 Prisma 参数化查询，避免安全问题
- 测试用例独立可重复，不依赖外部服务
- 命名和文件结构完全符合规范要求
---
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 · 41 lines · 9 tokens per session scan A 63d001dfaef7
develop-new-feature is a command published in the GitHub repository 854771076/oh-my-claude-roles (22 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 428 once invoked, about $0.0000 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.
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.