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 skills/twisker/claude-project-bootstrap/project-bootstrapnpx skills add twisker/claude-project-bootstrap --skill project-bootstrapgit clone --depth 1 https://github.com/twisker/claude-project-bootstrapWrote 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/skills/twisker/claude-project-bootstrap/project-bootstrap)<a href="https://agentmods.dev/skills/twisker/claude-project-bootstrap/project-bootstrap"><img src="https://agentmods.dev/badge/skills/twisker/claude-project-bootstrap/project-bootstrap.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 | $0.00108 | $0.01812 |
| Opus 5 | $0.00054 | $0.00906 |
| Sonnet 5 | $0.00022 | $0.00362 |
| Haiku 4.5 | $0.00011 | $0.00181 |
Grade A, and why
project-bootstrap 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.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Bootstrap
为任意新项目生成完整的 AI-Human 协作开发框架。通过对话理解用户意图,最终产出 README.md、CLAUDE.md 和 .claude/ 目录下的全套协作规范文件,并配置 git 版本自动管理钩子。
用户输入: $ARGUMENTS
Phase 1: 需求采集(对话式)
目标: 通过对话充分理解项目的真实意图和边界。根据用户已提供的信息量灵活调整——信息充足时可以快速确认,信息不足时多问几轮。避免重复询问用户已经说过的内容。
如果用户提供了文档路径($ARGUMENTS),先读取文档内容,从中提取信息,然后向用户确认并补充遗漏项。如果用户提供了描述文字,从中提取信息后同样确认补充。如果什么都没提供,直接进入提问流程。
需要采集的信息
以下是需要了解的信息清单,根据已知信息灵活组织提问,一次可以问多个相关问题:
基础信息:
- 项目名称与代号 — 项目的正式名称?英文代号?
- 一句话描述 — 用一句话描述这个项目做什么?
- 目标用户 — 谁会使用这个产品?
- 核心问题 — 它解决什么问题?
- 输入文档 — 是否有 PRD、需求文档、竞品分析等参考文档?
技术方向: 6. 技术栈偏好 — 前端框架?后端语言/框架?数据库?已定技术选型? 7. 部署环境 — 云平台?容器化?Serverless?本地? 8. 团队规模 — 几个人开发?AI 占多大比重? 9. 项目规模预估 — 大概多少个页面/API/模块?分几个阶段?
协作模式: 10. 角色分工 — 人工负责什么?AI 负责什么?审批流程? 11. 关键约束 — 时间限制?合规要求?性能指标? 12. 特殊要求 — 行业术语?文档语言偏好?
确认
将收集到的信息整理成简要摘要,展示给用户确认。用户可修正或补充。
进入 Phase 2 的条件: 项目名称 + 一句话描述 + 至少一个技术栈方向 + 角色分工明确 + 交付阶段有初步规划。
Phase 2: 文件生成
按以下顺序生成文件。全部生成完毕后统一 git add + git commit。
每个文件的内容必须根据 Phase 1 采集的信息完整填充,所有 {占位符} 都必须替换为实际内容,不得原样保留。如果某个章节不适用于当前项目,标注"不适用"而非删除。
生成文件清单
按以下顺序逐一生成。生成每个文件前,先读取对应的 reference 模板文件,理解其结构和意图,然后结合 Phase 1 采集的信息填充内容:
.claude/COLLABORATION.md— 读取references/collaboration-template.md,生成协作规范.claude/tech-spec-registry.md— 读取references/tech-spec-template.md,生成技术规格登记.claude/arch-spec-registry.md— 读取references/arch-spec-template.md,生成架构规格登记.claude/sprint-plan.md— 读取references/sprint-plan-template.md,生成迭代计划.claude/current-sprint.md— 读取references/current-sprint-template.md,生成当前迭代状态.claude/module-spec-registry.md— 读取references/module-spec-template.md,生成模块索引.claude/test-registry.md— 读取references/test-registry-template.md,生成测试登记.claude/validation-registry.md— 读取references/validation-template.md,生成验收标准.claude/archive/— 创建空目录(加一个.gitkeep)人工TODO事项.md— 读取references/human-todo-template.md,生成人工待办清单README.md— 面向人类阅读:项目名称、架构、模块、技术栈、目录结构、路线图。不放 AI 协作内部细节。CLAUDE.md— 读取references/claude-md-template.md,生成项目指令文件。精简扼要,细节全部引用.claude/子文件。
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/arch-spec-template.md 2.0 KB
- references/claude-md-template.md 3.9 KB
- references/collaboration-template.md 2.7 KB
- references/current-sprint-template.md 1.1 KB
- references/human-todo-template.md 473 B
- references/module-spec-template.md 1.0 KB
- references/scripts-template.md 2.4 KB
- references/sprint-plan-template.md 1.3 KB
- references/tech-spec-template.md 889 B
- references/test-registry-template.md 1.4 KB
- references/validation-template.md 1.1 KB
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 · 114 lines · 108 tokens per session scan A 8c9b13d3c1e3
project-bootstrap is a skill published in the GitHub repository twisker/claude-project-bootstrap (15 stars, last pushed 5mo ago), licensed MIT. It adds 108 tokens to every session and 1,812 once invoked, about $0.0005 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…