kickoff

kickoff is a command for coding agents from dhslegen/digital-delivery-team. It costs 37 tokens per session (1,083 once invoked), scanned A, original, MIT.

A project-start command that runs product requirements, work breakdown, and design steps in sequence. It can ask decisions at each step or run automatically with --auto.

In plain words
What is it for?
Use it to turn an initial project description into requirements, a task structure, and design inputs, including a selected technology stack.
Why use it?
It gives a new project a defined starting process and keeps early planning steps in order.

Command

Part of the digital-delivery-team plugin — 13 skills, 21 commands, 9 agents, 8 hooks shipped together

Install

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.

agentmods
npx agentmods add commands/dhslegen/digital-delivery-team/kickoff
Clone the repo
git clone --depth 1 https://github.com/dhslegen/digital-delivery-team

Or install digital-delivery-team, the plugin that ships this one along with the rest of its 13 skills, 21 commands, 9 agents, 8 hooks.

Wrote 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.

agentmods badge for kickoff

README.md
[![agentmods](https://agentmods.dev/badge/commands/dhslegen/digital-delivery-team/kickoff.svg)](https://agentmods.dev/commands/dhslegen/digital-delivery-team/kickoff)
Your own site
<a href="https://agentmods.dev/commands/dhslegen/digital-delivery-team/kickoff"><img src="https://agentmods.dev/badge/commands/dhslegen/digital-delivery-team/kickoff.svg" alt="Measured on agentmods" height="20"></a>
Per session 37 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,083 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.01083
Opus 5 $0.00018 $0.00541
Sonnet 5 $0.00007 $0.00217
Haiku 4.5 $0.00004 $0.00108

Measured 5d ago against content hash 5243b4b39406, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

kickoff 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.

commands/kickoff.md · 81 lines

How it starts

The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/kickoff

输入:$ARGUMENTS(补充需求描述,透传给 /prd


执行步骤

Step 0:技术栈预选(M6.3 新增)

LLM 必须执行:在跑 /prd 之前,先检查 project-brief.md 中的 "技术栈预设" 字段。

字段值 行为
具体 preset(java-modern / node-modern ...) 跳过问卷,按 preset 解析
interactive 或缺失 必须AskUserQuestion 工具发起 4 步问卷(Spring Initializr 等价:语言 → 数据库 → 前端 → UI)
custom 且 brief 已填详细字段 按详细字段解析,不再问
custom 但 brief 字段不全 用 AskUserQuestion 补全缺失字段

具体 4 步问卷模板见 commands/design.md::Phase 2b,或直接读取 $DDT_PLUGIN_ROOT/templates/tech-stack-options.yaml::askuserquestion_flow插件根——v0.9.3 D19)。

收集到答案后,把结果写入 .ddt/components.json.tmp(项目本地路径,多项目并行不冲突,跑完即删),必须使用嵌套对象 schema

{
  "preset": "<step1 推断的 preset 名>",
  "backend":  { "language": "...", "framework": "...", "database": { "primary": "..." } },
  "frontend": { "framework": "...", "ui": { "components": "..." } },
  "ai_design": { "type": "claude-design | figma | v0" }
}

严禁写成扁平字符串(如 "backend": "java-spring-boot")—— resolve-tech-stack.mjs 会拒绝并退出 2,污染源默认 preset 的字段。后续 /design 阶段会 merge。

M6.2 执行模式

  • 默认 interactive 模式:每个内部 phase(prd / wbs / design)跑完后必须暂停走决策门(skills/decision-gate/SKILL.md),未确认前禁止进入下一步
  • --auto 模式:传 --auto 时跳过所有决策门,按旧串行 chain 跑(兼容 v0.5.x 老用户的"一键起手"体验)

Step 1:跑 /prd $ARGUMENTS

  • project-brief.md 缺失 → 停止,提示用户填写
  • 若存在阻塞项(docs/blockers.md 非空)→ 停止,提示处理阻塞
  • /prd 命令内部已含决策门(除非 --auto);用户接受后再继续 Step 2
  • 未传 --auto 时:LLM 必须等待 /prd 决策门返回 accept 后才推进到 /wbs

Step 2:跑 /wbs

  • 若失败 → 停止
  • /wbs 内部决策门同上
  • 未传 --auto 时:LLM 必须等待 /wbs 决策门返回 accept 后才推进到 /design

Step 3:跑 /design $ARGUMENTS(透传 --preset / --ai-design

  • /design 内部 Phase 2b 会基于 Step 0 收集的 components JSON 写入 .ddt/tech-stack.json
  • 若契约 lint 未通过 → 停止,返回退出码 4
  • 若 OpenAPI lint 工具缺失 → 停止,返回退出码 5
  • 若产出文件缺失 → 停止
  • /design 内部决策门同上
  • 未传 --auto 时:LLM 必须等待 /design 决策门返回 accept 后才输出 kickoff 汇总
  1. 汇总输出:
/kickoff 完成

PRD 用户故事数: <n> 条
WBS 任务数:     <n> 个
Endpoint 数:    <n> 个
Top 3 风险:
  1. <risk-1>
  2. <risk-2>
  3. <risk-3>

建议下一步:/impl

Read the full file on GitHub · 81 lines

Changes

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.

  1. 5d ago First seen · 81 lines · 37 tokens per session scan A 5243b4b39406

Subscribe to this mod's changes

kickoff is a command published in the GitHub repository dhslegen/digital-delivery-team (1 stars, last pushed 3mo ago), licensed MIT. It adds 37 tokens to every session and 1,083 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 2026-08-31.