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/miniidealab/openlogos/project-initnpx skills add miniidealab/openlogos --skill project-initgit clone --depth 1 https://github.com/miniidealab/openlogosWhat 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 | $0.00000 | $0.01407 |
| Opus 5 | $0.00000 | $0.00704 |
| Sonnet 5 | $0.00000 | $0.00281 |
| Haiku 4.5 | $0.00000 | $0.00141 |
Grade A, and why
project-init 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 2d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Project Init
初始化一个遵循 OpenLogos 方法论的项目结构,生成配置文件、AI 指令文件和标准目录。
触发条件
- 用户要求创建新项目或初始化项目结构
- 用户提到 "openlogos init" 或 "初始化项目"
- 当前目录下没有
logos/logos.config.json
核心能力
- 创建
logos/目录及其标准子结构 - 生成
logos/logos.config.json配置文件 - 生成
logos/logos-project.yamlAI 协作索引 - 生成
AGENTS.md/CLAUDE.mdAI 指令文件(根目录) - 创建
logos/changes/变更管理目录
执行步骤
Step 1: 收集项目信息
向用户确认以下信息:
- 项目名称:用于
logos/logos.config.json的name字段 - 项目描述:一句话描述
- 技术栈:主框架、语言、数据库、部署平台
- 文档模块:除默认的 prd/api/scenario/database 外,是否需要额外模块
如果用户没有提供,使用合理的默认值。
Step 2: 创建目录结构
project-root/
└── logos/
├── resources/
│ ├── prd/
│ │ ├── 1-product-requirements/
│ │ ├── 2-product-design/
│ │ │ ├── 1-feature-specs/
│ │ │ └── 2-page-design/
│ │ └── 3-technical-plan/
│ │ ├── 1-architecture/
│ │ └── 2-scenario-implementation/
│ ├── api/
│ ├── database/
│ └── scenario/
└── changes/
Step 3: 生成 logos/logos.config.json
{
"name": "{项目名称}",
"description": "{项目描述}",
"documents": {
"prd": {
"label": { "en": "Product Docs", "zh": "产品文档" },
"path": "./resources/prd",
"pattern": "**/*.{md,html,htm,pdf}"
},
"api": {
"label": { "en": "API Docs", "zh": "API 文档" },
"path": "./resources/api",
"pattern": "**/*.{yaml,yml,json}"
},
"scenario": {
"label": { "en": "Scenarios", "zh": "业务场景" },
"path": "./resources/scenario",
"pattern": "**/*.json"
},
"database": {
"label": { "en": "Database", "zh": "数据库" },
"path": "./resources/database",
"pattern": "**/*.sql"
}
}
}
path字段相对于logos.config.json自身所在目录(即logos/),因此./resources/prd指向logos/resources/prd。
Step 4: 生成 logos/logos-project.yaml
project:
name: "{项目名称}"
description: "{项目描述}"
methodology: "OpenLogos"
tech_stack:
framework: "{用户提供的框架}"
language: "{用户提供的语言}"
# ... 根据用户提供的信息填充
resource_index: []
# 初始为空,随着文档产出逐步添加
conventions:
- "遵循 OpenLogos 三层推进模型(Why → What → How)"
- "每次变更必须先创建 logos/changes/ 变更提案"
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.
- 2d ago First seen · 164 lines · 0 tokens per session scan A 34da9ee49fd9
project-init is a skill published in the GitHub repository miniidealab/openlogos (71 stars, last pushed 5d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,407 tokens. 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.
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…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
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.