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affaan-m/search-first

affaan-m

search-first

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

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v1.1Saved May 11, 2026

/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

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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Overall Score

87/100

Grade

A

Excellent

Safety

92

Quality

85

Clarity

88

Completeness

80

Summary

A research-before-coding workflow skill that systematizes the practice of searching for existing tools, libraries, and patterns before implementing custom solutions. It guides agents through a structured 5-phase process: need analysis, parallel search (npm, PyPI, MCP, GitHub), evaluation, decision (adopt/extend/build), and implementation. The skill invokes a researcher agent for non-trivial functionality and provides category-specific search shortcuts and decision matrices to help agents choose between adopting existing solutions, extending them, composing multiple libraries, or building custom code.

Detected Capabilities

agent-invocation (researcher subagent)information-search (npm, PyPI, GitHub, MCP)decision-making frameworklibrary evaluation guidance

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

research before codingfind existing solutionavoid reinventing wheelevaluate librarieschoose package

Use Cases

  • Avoid reimplementing common development tools or utilities
  • Make informed decisions about adopting vs. building packages
  • Integrate external tools into development workflows before custom implementation
  • Systematize tech stack evaluation across npm, PyPI, MCP, and GitHub
  • Research and compare candidate libraries for specific functionality across multiple ecosystems

Quality Notes

  • Well-structured workflow with clear 5-phase process visualized in ASCII diagram
  • Decision matrix provides concrete scoring criteria (functionality, maintenance, community, docs, license, dependencies)
  • Category-specific search shortcuts reduce cognitive load for common patterns (linting, formatting, testing, AI/LLM, data/APIs, content)
  • Integration points clearly documented for collaboration with planner, architect, and iterative-retrieval skills
  • Practical examples (dead link checking, HTTP client wrapper, config linter) demonstrate realistic use cases with decision reasoning
  • Anti-patterns section prevents common mistakes (jumping to code, ignoring MCP, over-customizing)
  • Quick mode vs. full mode guidance allows flexible adoption paths depending on complexity
  • Clear trigger conditions help agents recognize when to activate the skill
Model: claude-haiku-4-5-20251001Analyzed: May 11, 2026

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Version History

v1.1

Content updated

2026-04-20

Latest
v1.0

Seeded from github.com/affaan-m/everything-claude-code

2026-03-16

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