search-first

Systematize research-before-coding by searching existing tools, libraries, and patterns before writing custom code.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/affaan-m/everything-claude-code --skill search-first

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0

this week

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What it does

  • Provides a five-phase workflow: need analysis, parallel search across npm/PyPI/MCP/GitHub, evaluation, decision (adopt/extend/compose/build), and implementation

  • Includes a decision matrix to score candidates on functionality, maintenance, community, docs, license, and dependencies

  • Offers search shortcuts organized by category (development tooling, AI/LLM integration, da

Category

Productivity

Last updated

Apr 8, 2026

Installation Guide

How to use search-first on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add search-first
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/affaan-m/everything-claude-code --skill search-first

Fetches search-first from affaan-m/everything-claude-code and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/search-first

Restart Cursor to activate search-first. Access via /search-first in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

/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:

  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

List & Monetize Your Skill

Submit your Claude Code skill and start earning

Get started →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Steps

  1. 1Install product management skill
  2. 2Start with user story generation for known feature
  3. 3Progress to competitive analysis: research 2-3 competitors
  4. 4Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5Draft stakeholder communications and refine based on feedback
  6. 6Build template library for recurring PM tasks
  7. 7Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use when

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid when

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Related Skills

Reviews

4.732 reviews
  • P
    Pratham WareDec 28, 2024

    I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • H
    Hana GarciaDec 16, 2024

    search-first has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • A
    Aarav SrinivasanDec 12, 2024

    Useful defaults in search-first — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • S
    Sakshi PatilNov 19, 2024

    Useful defaults in search-first — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • S
    Soo SethiNov 7, 2024

    search-first reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • S
    Sophia ParkNov 3, 2024

    I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • M
    Meera FarahOct 26, 2024

    I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • M
    Mateo ShahOct 22, 2024

    search-first reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • C
    Chaitanya PatilOct 10, 2024

    search-first has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • S
    Sofia LiSep 5, 2024

    search-first fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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