LLM Mart Basic

API Mock 数据生成

架构分析（生成/更新 docs/ARCHITECTURE.md 系统架构总览 · surface 型）

AI 对话式项目初始化

更新变更日志

代码脚手架生成

数据库设计

数据库迁移管理

创建技术设计文档（自动生成 + 自检 + handoff）

端到端测试（生成或执行 E2E 用例）

全自动 PDLC 新功能开发（串联 PRD→设计→TDD→实现→评审→发布）

全自动 Bug 修复（定位→复现→修复→测试→文档）

国际化（i18n）

按设计文档和已有测试用例实现代码（带前置守卫、自检、handoff）

代码质量检查与自动修复

打印循环下一步应执行的命令（机器可读，供 loop 驱动）

收敛循环引擎（自动推进 tdd→implement→review 到 review_done 或 blocked）

新人引导 / 自动化生成用户手册

性能优化

创建 PRD 文档（自动化生成 + 自检 + handoff）

质量闸门——跑真实 check、对照质量目标、出可核对报告，由人签字放行

Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.

A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.

A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.

None of the safety came from the model. It came from six boring habits.

Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.

Six hours in, one step left, everything green, and the incident that didn't happen

CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.

Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it

An API request routing a model's tool call through an approval gate to a remote MCP server

31 config keys, two audits, and why the first one was wrong in both directions

The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.

Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.

Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.

"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks

stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.

The most important rule wasn't about what I could change. It was about what I was allowed to display.

Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Outline a piece from your concept pages

Audit what should not be in the vault

Where a project stands

Create a project

Find what is safe to remove

Check what is safe to publish

Test yourself on your own pages

Rename a page and fix every link

Write a research report

Fix a page's type

Periodic review of what the vault learned

Undo the last run

Set up scheduled maintenance

Check frontmatter against the schema

Pull knowledge into a project

Scan for credentials

Show what a claim rests on

Split an overloaded page

Find concept pages nobody has touched

Audit the tag vocabulary

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