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search-first — what is search-first MCP skill what is search-first MCP skill, how to use search-first researcher agent, search-first vs manual code research, search-first install Claude developer tool, search-first setup guide for AI agents, research-before-coding workflow automation, Anthropic Claude Code productivity tools, MCP agent skills for developers

Verified
v1.0.0
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About this Skill

Essential for Code Generation Agents that prioritize efficiency and avoid reinventing the wheel. search-first is an AI agent skill that automates the research-before-coding workflow. It triggers a researcher agent to search for existing tools, libraries, and patterns, preventing redundant custom code development within MCP-compatible environments like Claude Code.

Features

Automatically invokes a dedicated researcher agent to find existing solutions
Triggers based on specific development intents like 'add X functionality'
Systematizes the workflow before creating new utilities, helpers, or abstractions
Integrates with MCP (Model Context Protocol) for AI agent tooling
Designed for use within Anthropic's Claude and Claude Code environments

# Core Topics

affaan-m affaan-m
[62.0k]
[7678]
Updated: 3/6/2026

Quality Score

Top 5%
95
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add affaan-m/everything-claude-code/search-first

Agent Capability Analysis

The search-first MCP Server by affaan-m is an open-source Categories.official integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for what is search-first MCP skill, how to use search-first researcher agent, search-first vs manual code research.

Ideal Agent Persona

Essential for Code Generation Agents that prioritize efficiency and avoid reinventing the wheel.

Core Value

Systematizes a research-before-coding workflow by invoking researcher agents to find existing tools, libraries, and patterns before writing custom code. This prevents redundant development and ensures optimal dependency integration for new features, utilities, or abstractions.

Capabilities Granted for search-first MCP Server

Starting new feature development with existing solutions
Adding optimized dependencies and integrations
Creating utilities by leveraging established patterns
Avoiding custom code when libraries exist

! Prerequisites & Limits

  • Requires integration with a researcher agent
  • Depends on quality of available search tools/libraries
  • May slow initial development phase for simple tasks
Project
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/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

SignalAction
Exact match, well-maintained, MIT/ApacheAdopt — install and use directly
Partial match, good foundationExtend — install + write thin wrapper
Multiple weak matchesCompose — combine 2-3 small packages
Nothing suitable foundBuild — write custom, but informed by research

How to Use

Quick Mode (inline)

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

  1. Does this already exist in the repo? → rg through relevant modules/tests first
  2. Is this a common problem? → Search npm/PyPI
  3. Is there an MCP for this? → Check ~/.claude/settings.json and search
  4. Is there a skill for this? → Check ~/.claude/skills/
  5. 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

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