search-first

name search-first description Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent. metadata {"origin":"ECC"} /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 ┌─────────────────────────────────────────────┐ │ 0. TOOL AVAILABILITY PREFLIGHT │ │ Check search channels before relying on │ │ them; report skipped channels honestly │ ├─────────────────────────────────────────────┤ │ 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 Step 0: Tool Availability Preflight This is agent guidance, not an executable setup script. Check only the channels that are relevant to the task and project in front of you. Channel Check If missing Repository search rg --files and targeted rg queries State that only visible files were inspected Package registry npm --version , python -m pip --version , or project package manager Use web/docs search and avoid claiming registry coverage GitHub CLI gh auth status Use public web or local git history only MCP/docs tools Available tool list or local MCP config Fall back to official docs/web search Skills directory ls ~/.claude/skills ~/.codex/skills where applicable Say no local skill catalog was available Quick Mode (inline) Before writing a utility or adding functionality, mentally run through: Does this already exist in the repo? → rg through relevant modules/tests first Is this a common problem? → Search npm/PyPI Is there an MCP for this? → Check ~/.claude/settings.json and search Is there a skill for this? → Check ~/.claude/skills/ 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: Agent(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 ") Older Claude Code docs may call this Task(...) ; use the current agent/subagent tool name exposed by the active harness. 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 / undici (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 Silent skipping : Reporting "nothing found" when a search channel was unavailable Over-customizing : Wrapping a library so heavily it loses its benefits Dependency bloat : Installing a massive package for one small feature