search-first

name	search-first
description	Research-before-coding workflow. Search for existing tools, libraries, research literature, and patterns before writing custom code or research workflows. Invokes the researcher agent and uses research-evidence for academic paper search when literature support is needed.

/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

Academic Literature Path

When the task needs research grounding, method positioning, related work, citation support, or top-venue precedent, use $research-evidence instead of ad hoc web search. Prefer CVPR, ICCV, ECCV, ICLR, NeurIPS/NIPS, and ICML for CV/ML work; include CoRL, ICRA, IROS, AAAI, IJCAI, T-ITS, and RA-L only when the user topic justifies autonomous-driving, robotics, or collaborative perception coverage.

Use the evidence result to decide Adopt, Extend, Compose, or Build. Do not claim literature coverage when the research-evidence tool or source channel was unavailable.

Quick Mode (inline)

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

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