Token导航 LogoToken导航TokenDH.com
研究检索external-servicegithub未标认证来源可访问许可证需确认审计通过

bazdmegbazdmeg 搜索

Agent Skill

bazdmeg 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

724

周安装

29

GitHub Stars

1

下载量

234
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:bazdmeg(bazdmeg 搜索)
来源仓库:https://github.com/zerdos/spike-land-nextjs
仓库路径:skills/bazdmeg
安装命令:
npx skills add https://github.com/zerdos/spike-land-nextjs --skill bazdmeg
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/zerdos/spike-land-nextjs --skill bazdmeg

简介

用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过安装命令添加,需结合来源仓库和 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态及是否触发联网或文件读写操作。
  • bazdmeg 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

The BAZDMEG Method

Eight principles for AI-assisted development. Born from pain. Tested in production.

Quick Reference

#PrincipleOne-LinerDeep Dive
1Requirements Are The ProductThe code is just the outputreferences/01-requirements.md
2Discipline Before AutomationYou cannot automate chaosreferences/02-discipline.md
3Context Is ArchitectureWhat the model knows when you askreferences/03-context.md
4Test The LiesUnit tests, E2E tests, agent-based testsreferences/04-testing.md
5Orchestrate, Do Not OperateCoordinate agents, not keystrokesreferences/05-orchestration.md
6Trust Is Earned In PRsNot in promises, not in demosreferences/06-trust.md
7Own What You ShipIf you cannot explain it at 3am, do not ship itreferences/07-ownership.md
8Sources Have RankCanonical spec > audit > chatreferences/08-sources-have-rank.md

Effort Split

ActivityTimeWhy
Planning30%Understanding the problem, planning interview, verifying understanding
Testing50%Writing tests, running agent-based tests, verifying everything works
Quality20%Edge cases, maintainability, polish
Coding~0%AI writes the code; you make sure the code is right

Workflow: Planning Interview

Run this interview BEFORE any code is written. The agent asks the developer these questions and does not proceed until all are answered.

  1. What problem are we solving? -- State the problem in your own words, not the ticket's words.
  2. What data already exists? -- What is the server-side source of truth? What APIs exist? What state is already managed?
  3. What is the user flow? -- Walk through every step the user takes, including edge cases and error states.
  4. What should NOT change? -- Identify existing behavior, contracts, or interfaces that must be preserved.
  5. What happens on failure? -- Network errors, invalid input, race conditions, missing data.
  6. How will we verify it works? -- Name the specific tests: unit, E2E, agent-based. What constitutes "done"?
  7. Can I explain this to a teammate? -- If you cannot explain the approach to someone else, stop and learn more.

Stopping rules:

  • If any answer is "I don't know" -- stop and research before proceeding.
  • If the developer defers to "the AI will figure it out" -- stop. The requirement IS the product.
  • If no test plan exists -- stop. Untested code is unshippable code.

Checkpoint 0: Session Bootstrap

Run BEFORE anything else in a new session.

  • Read project status doc (STATUS_WALKTHROUGH, README, or equivalent)
  • Check task list / mailbox for pending work
  • Confirm current branch, latest commit, CI status
  • Identify what changed since last session (git log, diff)
  • Read agent-specific notes file (if multi-agent)
  • Execute the "NOW" section — do not ask questions first

If you cannot confirm the current state, stop. You are operating on stale context.


Checkpoint 1: Pre-Code Checklist

Run this BEFORE the AI writes any code.

  • Can I explain the problem in my own words?
  • Has the AI interviewed me about the requirements?
  • Do I understand why the current code exists?
  • Have I checked my documentation for relevant context?
  • Is my CLAUDE.md current?
  • Are my tests green and non-flaky?
  • Is CI running in under 10 minutes?

If any box is unchecked, do not proceed to implementation.


Checkpoint 2: Post-Code Checklist

Run this AFTER the AI writes code, BEFORE creating a PR.

  • Can I explain every line to a teammate?
  • Have I verified the AI's assumptions against the architecture?
  • Do I know why the AI chose this approach over alternatives?
  • Have the agents tested it like a human would?
  • Do MCP tool tests cover the business logic at 100%?

If any box is unchecked, go back and understand before proceeding.


Checkpoint 3: Pre-PR Checklist

Run this BEFORE submitting the pull request.

  • Do my unit tests prove the code works?
  • Do my E2E tests prove the feature works?
  • Does TypeScript pass with no errors in strict mode?
  • Can I answer "why" for every decision in the diff?
  • Would I be comfortable debugging this at 3am?
  • Does the PR description explain the thinking, not just the change?

If any answer is "no," stop. Go back. Learn more.


Automation-Ready Audit

Before adding AI agents to a workflow, verify these six gates pass.

GateRequirementCurrent (Feb 2026)Why
CI SpeedUnder 10 min (under 10s = branchless)~3 min tests, build OOM intermittentFast CI = fast agent iterations. If CI completes in under 10 seconds, skip branches entirely — commit to main (trunk-based dev).
Flaky TestsZeroZero knownFlaky tests gaslight the AI into chasing phantom bugs
Coverage100% on business logic80% lines (CI-enforced), 96% MCP file coverage (94/98)Untested code is invisible to agents; they will refactor through it
TypeScriptStrict mode enabledStrict, zero any, zero eslint-disableClaude Code integrates with the TS Language Server; strict mode = level zero
CLAUDE.mdCurrent and completeUpdated Feb 16, 2026 (7 pkgs, 98 tools, ~170 routes)Stops the AI from guessing; it follows the playbook instead
Domain GatesProject-specific executable quality gates exist(project-specific)Generic checklists miss domain invariants; executable gates catch what generic gates cannot

See references/02-discipline.md for the full breakdown.


The 10-Second Rule: Trunk-Based Development

If your CI pipeline (lint + typecheck + tests on changed files) consistently completes in under 10 seconds:

Skip feature branches. Commit directly to main. The feedback loop is fast enough that broken commits are caught and fixed immediately.

This is trunk-based development — the same pattern used by Google, Meta, and other high-velocity engineering orgs. Prerequisites:

  • Fast, reliable CI (under 10 seconds for affected tests)
  • Zero flaky tests
  • vitest --changed COMMIT_HASH to only run affected tests
  • file_guard MCP tool to pre-validate changes

When to still use branches:

  • CI takes more than 10 seconds
  • Multiple agents work on the same codebase simultaneously
  • Regulatory/compliance requirements mandate review

The math: 50 commits/day at 5s CI each = 4 minutes waiting. Branching overhead at 5 min/change = 250 minutes of ceremony. The choice is clear.


Hourglass Testing Model

     +---------------------+
     |   E2E Specs (heavy)  |  <-- Humans write these
     |   User flows as       |
     |   Given/When/Then     |
     +----------+-----------+
                |
        +-------v-------+
        |  UI Code       |  <-- AI generates this
        |  (thin,        |    Disposable.
        |   disposable)  |    Regenerate, don't fix.
        +-------+-------+
                |
+---------------v---------------+
|  Business Logic Tests (heavy)  |  <-- MCP tools + unit tests
|  Validation, contracts, state   |    Bulletproof.
|  transitions, edge cases        |    Never skip.
+-------------------------------+
LayerShareWhat to test
MCP tool tests70%Business logic, validation, contracts, state transitions
E2E specs20%Full user flows (Given/When/Then), wiring verification only
UI component tests10%Accessibility, responsive layout, keyboard navigation

See references/04-testing.md for the Three Lies Framework and test type decision guide.


Bayesian Bugbook

Bug appears twice → mandatory Bugbook entry. Bugs earn their record through recurrence and lose it through irrelevance.

EventConfidenceStatus
First observed0.5CANDIDATE — log conditions
Second occurrence0.6+ACTIVE — full entry + regression test
Fix prevents recurrence+0.1 per preventionACTIVE — confidence grows
Irrelevant 5+ sessions-0.1 decayDecaying → DEPRECATED below 0.3

Every ACTIVE entry requires a regression test matched to scope: unit test for single-function bugs, E2E test for cross-component bugs, agent-based test for usability bugs.

See references/04-testing.md for the full Bugbook entry format and Three Lies integration.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.92%
按下载量换算79

Claude

30.53%
按下载量换算71

Cursor

16.53%
按下载量换算39

Gemini CLI

9.4%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills