Systematize research-before-coding by searching existing tools, libraries, and patterns before writing custom code.
Works with
Provides a five-phase workflow: need analysis, parallel search across npm/PyPI/MCP/GitHub, evaluation, decision (adopt/extend/compose/build), and implementation
Includes a decision matrix to score candidates on functionality, maintenance, community, docs, license, and dependencies
Offers search shortcuts organized by category (development tooling, AI/LLM integration, da
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsearch-firstExecute the skills CLI command in your project's root directory to begin installation:
Fetches search-first from affaan-m/everything-claude-code and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate search-first. Access via /search-first in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Systematizes the "search for existing solutions before implementing" workflow.
Use this skill when:
┌─────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────┘
| 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 |
Before writing a utility or adding functionality, mentally run through:
rg through relevant modules/tests first~/.claude/settings.json and search~/.claude/skills/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
")
eslint, ruff, textlint, markdownlintprettier, black, gofmtjest, pytest, go testhusky, lint-staged, pre-commitunstructured, pdfplumber, mammothhttpx (Python), ky/got (Node)zod (TS), pydantic (Python)remark, unified, markdown-itsharp, imageminThe planner should invoke researcher before Phase 1 (Architecture Review):
The architect should consult researcher for:
Combine for progressive discovery:
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
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
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
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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parcadei/continuous-claude-v3
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I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
search-first has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in search-first — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in search-first — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
search-first reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend search-first for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
search-first reduced setup friction for our internal harness; good balance of opinion and flexibility.
search-first has been reliable in day-to-day use. Documentation quality is above average for community skills.
search-first fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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