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.
npx agentmods add commands/joe-rq/harness-lab/first-reqgit clone --depth 1 https://github.com/Joe-rq/harness-labWrote 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/joe-rq/harness-lab/first-req)<a href="https://agentmods.dev/commands/joe-rq/harness-lab/first-req"><img src="https://agentmods.dev/badge/commands/joe-rq/harness-lab/first-req.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 | $0.00040 | $0.00861 |
| Opus 5 | $0.00020 | $0.00430 |
| Sonnet 5 | $0.00008 | $0.00172 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
first-req 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 4d 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
/first-req
目标
引导新用户快速创建第一个 REQ，降低接入摩擦。
前置检查
- 确认
requirements/目录存在。不存在 → 提示先运行/harness-setup - 确认
package.json中有req:create脚本。不存在 → 提示先运行/harness-setup
执行步骤
Step 1: 项目类型识别
通过以下信号自动识别项目类型：
| 信号 | 项目类型 |
|---|---|
存在 package.json + react 依赖 |
React |
存在 package.json + next 依赖 |
Next.js |
存在 package.json + vue 依赖 |
Vue |
存在 pyproject.toml 或 setup.py |
Python |
存在 go.mod |
Go |
存在 Cargo.toml |
Rust |
| 以上都不匹配 | Generic |
Step 2: 用 AskUserQuestion 询问 REQ 主题
使用 AskUserQuestion 工具，提供 3 个推荐选项 + 自定义输入：
推荐选项根据项目类型生成：
- React/Next.js 项目：组件开发、状态管理、API 集成
- Python 项目：数据处理、API 开发、测试覆盖
- Go 项目：性能优化、并发处理、API 开发
- Generic：Bug 修复、功能开发、代码重构
Step 3: 创建 REQ
根据用户选择的主题，运行：
npm run req:create -- --title "[用户选择的主题]"
Step 4: 自动填充 REQ 内容
创建完成后，自动读取生成的 REQ 文件，填充以下内容：
背景：基于项目类型和主题生成一段简短描述。
目标：根据主题类型推荐 2-3 个目标。
验收标准：根据主题类型推荐 2-3 个可验证的标准。
主题与字段映射：
| 主题类型 | 推荐目标 | 推荐验收标准 |
|---|---|---|
| 组件开发 | 实现组件、编写测试、补充文档 | 组件可渲染、测试通过、Storybook 可用 |
| Bug 修复 | 定位根因、实现修复、回归测试 | Bug 不再复现、回归测试通过 |
| API 开发 | 实现端点、参数校验、错误处理 | API 可调通、4xx/5xx 处理正确 |
| 测试覆盖 | 识别未覆盖路径、编写测试 | 覆盖率提升至目标值 |
| 代码重构 | 消除重复、改善命名、保持行为 | 重构后测试仍通过 |
Step 5: 启动 REQ
提示用户确认 REQ 内容是否满意，然后运行：
npm run req:start -- --id [创建的 REQ ID] --phase implementation
输出
向导完成后，输出：
- 创建的 REQ ID 和文件路径
- REQ 当前状态
- 下一步建议：直接开始实施，或先补充更多细节
约束
- 不跳过
req:create和req:start的验证逻辑 - 不自动填充颗粒度自检（由用户自己判断）
- 如果
req:start因内容不足被拒绝，提示用户补充后重试 - 整个流程应在 5 分钟内完成（从运行命令到 REQ 进入 in-progress）
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.
- 4d ago First seen · 94 lines · 40 tokens per session scan A 3396d3665546
first-req is a command published in the GitHub repository Joe-rq/harness-lab (20 stars, last pushed 22d ago), licensed MIT. It adds 40 tokens to every session and 861 once invoked, about $0.0002 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
git
Git operations with intelligent commit messages and workflow optimization.
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.