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search-first

Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Systematizes the "search for existing solutions before implementing" approach. Use when starting new features or adding functionality.

DeepseekModel キュレーション済みスキル 品質 優秀 · 90 v1.0.0

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name search-first description Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Systematizes the "search for existing solutions before implementing" approach. Use when starting new features or adding functionality. 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 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 Signal Action Exact match, well-maintained, MIT/Apache Adopt — recommend the package and request approval before install or config changes Partial match, good foundation Extend — recommend the package plus a thin wrapper, then wait for approval before applying Multiple weak matches Compose — propose 2-3 small packages and the integration plan before installing anything Nothing suitable found Build — 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: Does this already exist in the repo? → Search through relevant modules/tests first Is this a common problem? → Search npm/PyPI Is there an MCP for this? → Check MCP configuration and search Is there a skill for this? → Check available skills 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
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formatフォーマット識別子(skill/v1)
skill_idスキル固有 ID
nameスキル名
versionバージョン
description説明
categoryカテゴリ(配列)
trigger_wordsトリガーワード
tagsタグ
sourceソース
source_urlソース URL(本ページ)
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system_promptシステムプロンプト本文
model_configモデル設定:provider / model / temperature / max_tokens / top_p
examplesサンプル
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.skill 標準形式。system_prompt と model_config を収録し、任意の Agent で利用可能 ダウンロード
.skillpro 拡張形式。scripts / tools / dependencies / hooks を含む ダウンロード
.json 純粋な JSON 出力。system_prompt とモデル設定のみ ダウンロード
Coze frontmatter 付き Markdown。Coze へのインポート用 ダウンロード
Dify Dify DSL。アプリ作成後にそのままインポート ダウンロード

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