AGENT SKILL

search-first

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

License 许可
MIT
最近更新
2026-11-02
Stars
★ 0
Forks
0

01 前言 Front Matter

name
search-first
origin
ECC
description
Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.

03 SKILL.md 正文

/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

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
  • Over-customizing: Wrapping a library so heavily it loses its benefits
  • Dependency bloat: Installing a massive package for one small feature

04 同仓库更多 Skills

Prekzursil
Prekzursil/agent-skills-toolchain

clarify-requirements

Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.

★ 0
Prekzursil
Prekzursil/agent-skills-toolchain

openmm-md

Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein / protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.

Skill与智能体开发
★ 0
Prekzursil
Prekzursil/agent-skills-toolchain

visual-review

Take screenshots of web pages and UI using Playwright for visual review and iteration

演示文稿测试与质量
★ 0
Prekzursil
Prekzursil/agent-skills-toolchain

status

Diagnostic status report — shows metaswarm installation state, project setup, and potential issues

★ 0
Prekzursil
Prekzursil/agent-skills-toolchain

start

Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work

Skill与智能体开发
★ 0
Prekzursil
Prekzursil/agent-skills-toolchain

setup

Interactive project setup — detects your project, configures metaswarm, writes project-local files

★ 0