Use the most token-efficient search tool for each query type.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsearch-routerExecute the skills CLI command in your project's root directory to begin installation:
Fetches search-router from parcadei/continuous-claude-v3 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-router. Access via /search-router 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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Use the most token-efficient search tool for each query type.
Query Type?
├── CODE EXPLORATION (symbols, call chains, data flow)
│ → TLDR Search - 95% token savings
│ DEFAULT FOR ALL CODE SEARCH - use instead of Grep
│ Examples: "spawn_agent", "DataPoller", "redis usage"
│ Command: tldr search "query" .
│
├── STRUCTURAL (AST patterns)
│ → AST-grep (/ast-grep-find) - ~50 tokens output
│ Examples: "def foo", "class Bar", "import X", "@decorator"
│
├── SEMANTIC (conceptual questions)
│ → TLDR Semantic - 5-layer embeddings (P6)
│ Examples: "how does auth work", "find error handling patterns"
│ Command: tldr semantic search "query"
│
├── LITERAL (exact text, regex)
│ → Grep tool - LAST RESORT
│ Only when TLDR/AST-grep don't apply
│ Examples: error messages, config values, non-code text
│
└── FULL CONTEXT (need complete understanding)
→ Read tool - 1500+ tokens
Last resort after finding the right file
| Tool | Output Size | Best For |
|---|---|---|
| TLDR | ~50-500 | DEFAULT: Code symbols, call graphs, data flow |
| TLDR Semantic | ~100-300 | Conceptual queries (P6, embedding-based) |
| AST-grep | ~50 tokens | Function/class definitions, imports, decorators |
| Grep | ~200-2000 | LAST RESORT: Non-code text, regex |
| Read | ~1500+ | Full understanding after finding the file |
# CODE EXPLORATION → TLDR (DEFAULT)
tldr search "spawn_agent" .
tldr search "redis" . --layer call_graph
# STRUCTURAL → AST-grep
/ast-grep-find "async def $FUNC($$$):" --lang python
# SEMANTIC → TLDR Semantic
tldr semantic search "how does authentication work"
# LITERAL → Grep (LAST RESORT - prefer TLDR)
Grep pattern="check_evocation" path=opc/scripts
# FULL CONTEXT → Read (after finding file)
Read file_path=opc/scripts/z3_erotetic.py
1. AST-grep: "Find async functions" → 3 file:line matches
2. Read: Top match only → Full understanding
3. Skip: 4 irrelevant files → 6000 tokens saved
/tldr-search - DEFAULT - Code exploration with 95% token savings/ast-grep-find - Structural code search/morph-search - Fast text searchMake 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.
parcadei/continuous-claude-v3
kostja94/marketing-skills
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: search-router is focused, and the summary matches what you get after install.
Registry listing for search-router matched our evaluation — installs cleanly and behaves as described in the markdown.
search-router reduced setup friction for our internal harness; good balance of opinion and flexibility.
search-router fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend search-router for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
search-router is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: search-router is focused, and the summary matches what you get after install.
We added search-router from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
search-router has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: search-router is the kind of skill you can hand to a new teammate without a long onboarding doc.
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