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Search First

  • 1.5k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/ecc

This is a copy of search-first by affaan-m - installs and ranking accrue to the original listing.

search-first is a research-before-coding workflow skill that discovers existing libraries, MCP servers, and patterns for developers who want to avoid writing custom code when proven solutions already exist.

About

search-first is a research-before-coding workflow skill from affaan-m/ecc that systematizes discovery of existing tools, libraries, MCP servers, and patterns before writing custom code. Developers reach for search-first when starting new features, adding dependencies or integrations, or when a user asks to add functionality and the agent is about to implement from scratch. The skill triggers on new feature work where existing solutions likely exist and guides agents to search repositories, package ecosystems, and MCP catalogs first. search-first prevents duplicate utilities, helpers, and abstractions by making discovery a mandatory gate before implementation.

  • Systematizes research-before-coding workflow with 4-step process
  • Runs parallel searches across npm/PyPI, MCP/Skills, and GitHub/web
  • Evaluates candidates on functionality, maintenance, community, docs, license and dependencies
  • Always performs tool-availability preflight and reports skipped channels honestly
  • Prevents unnecessary custom code by surfacing existing solutions first

Search First by the numbers

  • 1,451 all-time installs (skills.sh)
  • +91 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/affaan-m/ecc --skill search-first

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Listed on Skillselion
Installs1.5k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/ecc

How do you find existing libraries before writing code?

Ensure they discover existing libraries, MCP servers, and patterns before writing new code from scratch.

Who is it for?

Developers starting new features or integrations who want agents to search package registries and MCP catalogs before writing custom utilities.

Skip if: Bug fixes in existing codepaths, trivial one-liner changes, or projects with mandated internal-only libraries and no external search.

When should I use this skill?

A user requests new functionality and the agent is about to write code without first searching for existing libraries, MCP servers, or patterns.

What you get

Evaluated existing libraries, MCP servers, and reusable patterns with a documented build-or-buy recommendation.

  • Library evaluation
  • MCP server shortlist

Files

SKILL.mdMarkdownGitHub ↗

/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

Scope and Approval Rules

Default to read-only research: inspect the repo, package metadata, docs, and public examples before recommending a dependency or integration. Do not install packages, configure MCP servers, publish artifacts, open PRs, or make external write actions from this skill unless the user has explicitly approved that action in the current task.

When a candidate requires credentials, paid services, network writes, or project-wide config changes, return a recommendation and approval checkpoint instead of applying it directly.

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. APPROVAL CHECKPOINT / IMPLEMENT         │
│     Recommend package / MCP / custom code   │
│     Apply only after explicit approval      │
└─────────────────────────────────────────────┘

Decision Matrix

SignalAction
Exact match, well-maintained, MIT/ApacheAdopt — recommend the package and request approval before install or config changes
Partial match, good foundationExtend — recommend the package plus a thin wrapper, then wait for approval before applying
Multiple weak matchesCompose — propose 2-3 small packages and the integration plan before installing anything
Nothing suitable foundBuild — explain why custom code is warranted, then implement only within the approved task scope

How to Use

Quick Mode (inline)

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

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

Full Mode (subagent)

For non-trivial functionality, delegate to a research-focused subagent:

Invoke subagent with prompt:
  "Research existing tools for: [DESCRIPTION]
   Language/framework: [LANG]
   Constraints: [ANY]

   Search: npm/PyPI, MCP servers, 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 → Check 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 — recommend `textlint-rule-no-dead-link` and ask before installing it
Result: Zero custom code if approved, 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 — recommend `got`/`httpx` directly with retry config and ask before changing dependencies
Result: Zero custom code if approved, 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 — recommend `ajv-cli` plus a project-specific schema, then wait for approval before install/write
Result: 1 package + 1 schema file if approved, 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

When to Use This Skill

  • Starting new features
  • Adding dependencies or integrations
  • Before writing utilities or helpers
  • When evaluating technology choices
  • Planning architecture decisions

Related skills

How it compares

Use search-first at feature kickoff for discovery rather than implementation skills that assume custom code is the default path.

FAQ

When does search-first trigger?

search-first triggers when starting new features, adding dependencies or integrations, or before creating utilities and abstractions. The skill runs before the agent writes custom code for functionality that may already exist.

What does search-first prevent?

search-first prevents coding agents from reinventing libraries, MCP integrations, and helper abstractions. Agents search existing tools and patterns first, then implement only when no suitable solution exists.

Productivity & Planningintegrationstestingdocs

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