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/featuregit 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/feature)<a href="https://agentmods.dev/commands/joe-rq/harness-lab/feature"><img src="https://agentmods.dev/badge/commands/joe-rq/harness-lab/feature.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 | $0.00020 | $0.00523 |
| Opus 5 | $0.00010 | $0.00262 |
| Sonnet 5 | $0.00004 | $0.00105 |
| Haiku 4.5 | $0.00002 | $0.00052 |
Grade A, and why
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 5d 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
/feature
目标
引导用户创建 feature 型 REQ。
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 - 确认当前无活跃 REQ（
requirements/INDEX.md中## 当前活跃 REQ下为"无"）。有活跃 REQ → 提示先完成或搁置当前 REQ
执行步骤
Step 1: 收集功能信息
使用 AskUserQuestion 询问：
- 功能名称（必填）：简短描述要实现的功能，如"用户邮箱验证"
- 用户痛点（必填）：用户当前遇到什么问题？
- 业务背景（可选）：为什么现在要做？
Step 2: 创建 feature REQ
运行：
npm run req:create -- --title "feat: [功能名称]" --type feature
Step 3: 补充功能详情
用 Edit 将 REQ 中的占位符替换为用户提供的实际信息：
[请描述]（背景-用户痛点）→ 用户的痛点[请描述]（背景-业务背景）→ 用户的业务背景[功能名称]（目标）→ 用户的实际功能名称
Step 4: 提示用户确认
重点提醒：
- Scope Control 的 CAN/CANNOT 必须填写（feature 型最易功能蔓延）
- 考虑是否需要创建设计文档（
docs/plans/{reqId}-design.md） - 补充"颗粒度自检"
- 确认后运行
npm run req:start -- --id {reqId} --phase implementation
输出
- 创建的 REQ ID 和文件路径
- REQ 类型：feature
- 已预填充：用户痛点/业务背景占位、Scope Control 必填提示、建议创建设计文档
- 下一步：补充 Scope Control CAN/CANNOT，运行
req:start
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.
- 5d ago First seen · 57 lines · 20 tokens per session scan A 10d317dd30a8
feature is a command published in the GitHub repository Joe-rq/harness-lab (20 stars, last pushed 23d ago), licensed MIT. It adds 20 tokens to every session and 523 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 2026-08-30.
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.