# Search First

> Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.

- Skill: `marucie/search-first` (Agent Skill)
- Install (CLI): `npx skillmds@latest add marucie/search-first`
- Raw SKILL.md: https://api.skillmd.com/api/skills/marucie/search-first/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: MARUCIE (https://skillmd.com/u/marucie)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/marucie/search-first

---


## 是什么

在动手写代码之前，先把 npm / PyPI / MCP 服务器 / Claude 技能库 / GitHub 翻一遍，帮你在 15 分钟里判断"这事到底是装一个包、套个壳、还是真的得自己造"，把"重复造轮子"的概率压到最低。

## 怎么用

1. 先做需求拆解：把"我要的能力"用一句话写清楚，标注语言 / 框架 / 许可 / 维护活跃度的硬约束。
2. 并行检索四路：包管理器（npm / PyPI）+ MCP 服务器 + 本地 skills 目录 + GitHub 仓库 / 模板搜索。
3. 用打分矩阵评候选（功能匹配 / 维护活跃 / 社区规模 / 文档完整 / 许可 / 依赖体积）。
4. 做决策：完全匹配就直接 Adopt；部分匹配就 Extend 写薄壳；多个弱匹配就 Compose 组合；都不行才 Build。
5. 实施时优先 install + 最小配置，自己写的代码越少越好，最后回头把研究结论沉淀进项目文档。

## 架构图

```mermaid
flowchart LR
  A[需求拆解] --> B[并行检索 npm/PyPI/MCP/GitHub]
  B --> C[候选打分矩阵]
  C --> D{匹配程度}
  D -->|完全| E[Adopt 直接装]
  D -->|部分| F[Extend 薄壳]
  D -->|弱匹配| G[Compose 组合]
  D -->|无| H[Build 自己造]
```

# /search-first — Research Before You Code

Systematizes the "search for existing solutions before implementing" workflow.

## Trigger

Use this skill when:
- Starting a new feature that likely has existing solutions
- Adding a dependency or integration
- The user asks "add X functionality" and you're about to write code
- Before creating a new utility, helper, or abstraction

## Workflow

```
┌─────────────────────────────────────────────┐
│  1. NEED ANALYSIS                           │
│     Define what functionality is needed      │
│     Identify language/framework constraints  │
├─────────────────────────────────────────────┤
│  2. PARALLEL SEARCH (researcher agent)      │
│     ┌──────────┐ ┌──────────┐ ┌──────────┐  │
│     │  npm /   │ │  MCP /   │ │  GitHub / │  │
│     │  PyPI    │ │  Skills  │ │  Web      │  │
│     └──────────┘ └──────────┘ └──────────┘  │
├─────────────────────────────────────────────┤
│  3. EVALUATE                                │
│     Score candidates (functionality, maint, │
│     community, docs, license, deps)         │
├─────────────────────────────────────────────┤
│  4. DECIDE                                  │
│     ┌─────────┐  ┌──────────┐  ┌─────────┐  │
│     │  Adopt  │  │  Extend  │  │  Build   │  │
│     │ as-is   │  │  /Wrap   │  │  Custom  │  │
│     └─────────┘  └──────────┘  └─────────┘  │
├─────────────────────────────────────────────┤
│  5. IMPLEMENT                               │
│     Install package / Configure MCP /       │
│     Write minimal custom code               │
└─────────────────────────────────────────────┘
```

## Decision Matrix

| Signal | Action |
|--------|--------|
| Exact match, well-maintained, MIT/Apache | **Adopt** — install and use directly |
| Partial match, good foundation | **Extend** — install + write thin wrapper |
| Multiple weak matches | **Compose** — combine 2-3 small packages |
| Nothing suitable found | **Build** — write custom, but informed by research |

## How to Use

### Quick Mode (inline)

Before writing a utility or adding functionality, mentally run through:

0. Does this already exist in the repo? → `rg` through relevant modules/tests first
1. Is this a common problem? → Search npm/PyPI
2. Is there an MCP for this? → Check `~/.claude/settings.json` and search
3. Is there a skill for this? → Check `~/.claude/skills/`
4. Is there a GitHub implementation/template? → Run GitHub code search for maintained OSS before writing net-new code

### Full Mode (agent)

For non-trivial functionality, launch the researcher agent:

```
Task(subagent_type="general-purpose", prompt="
  Research existing tools for: [DESCRIPTION]
  Language/framework: [LANG]
  Constraints: [ANY]

  Search: npm/PyPI, MCP servers, Claude Code skills, GitHub
  Return: Structured comparison with recommendation
")
```

## Search Shortcuts by Category

### Development Tooling
- Linting → `eslint`, `ruff`, `textlint`, `markdownlint`
- Formatting → `prettier`, `black`, `gofmt`
- Testing → `jest`, `pytest`, `go test`
- Pre-commit → `husky`, `lint-staged`, `pre-commit`

### AI/LLM Integration
- Claude SDK → Context7 for latest docs
- Prompt management → Check MCP servers
- Document processing → `unstructured`, `pdfplumber`, `mammoth`

### Data & APIs
- HTTP clients → `httpx` (Python), `ky`/`got` (Node)
- Validation → `zod` (TS), `pydantic` (Python)
- Database → Check for MCP servers first

### Content & Publishing
- Markdown processing → `remark`, `unified`, `markdown-it`
- Image optimization → `sharp`, `imagemin`

## Integration Points

### With planner agent
The planner should invoke researcher before Phase 1 (Architecture Review):
- Researcher identifies available tools
- Planner incorporates them into the implementation plan
- Avoids "reinventing the wheel" in the plan

### With architect agent
The architect should consult researcher for:
- Technology stack decisions
- Integration pattern discovery
- Existing reference architectures

### With iterative-retrieval skill
Combine for progressive discovery:
- Cycle 1: Broad search (npm, PyPI, MCP)
- Cycle 2: Evaluate top candidates in detail
- Cycle 3: Test compatibility with project constraints

## Examples

### Example 1: "Add dead link checking"
```
Need: Check markdown files for broken links
Search: npm "markdown dead link checker"
Found: textlint-rule-no-dead-link (score: 9/10)
Action: ADOPT — npm install textlint-rule-no-dead-link
Result: Zero custom code, battle-tested solution
```

### Example 2: "Add HTTP client wrapper"
```
Need: Resilient HTTP client with retries and timeout handling
Search: npm "http client retry", PyPI "httpx retry"
Found: got (Node) with retry plugin, httpx (Python) with built-in retry
Action: ADOPT — use got/httpx directly with retry config
Result: Zero custom code, production-proven libraries
```

### Example 3: "Add config file linter"
```
Need: Validate project config files against a schema
Search: npm "config linter schema", "json schema validator cli"
Found: ajv-cli (score: 8/10)
Action: ADOPT + EXTEND — install ajv-cli, write project-specific schema
Result: 1 package + 1 schema file, no custom validation logic
```

## Anti-Patterns

- **Jumping to code**: Writing a utility without checking if one exists
- **Ignoring MCP**: Not checking if an MCP server already provides the capability
- **Over-customizing**: Wrapping a library so heavily it loses its benefits
- **Dependency bloat**: Installing a massive package for one small feature