parcadei/continuous-claude-v3

search-router

Choose the right search tool for each query type

First seen Jan 22, 2026

Installation

$ npx skills add parcadei/continuous-claude-v3 --skill search-router

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 3.9K
License LICENSE
Default branch main
Open issues 30
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,624 B
  • docs SUMMARY.md 66 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 480 installs

SKILL.md

Search Tool Router

Use the most token-efficient search tool for each query type.

When to Use

  • Searching for code patterns
  • Finding where something is implemented
  • Looking for specific identifiers
  • Understanding how code works

Decision Tree

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

Token Efficiency Comparison

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

Examples

# 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

Optimal Flow

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

Related Skills

  • /tldr-search - DEFAULT - Code exploration with 95% token savings
  • /ast-grep-find - Structural code search
  • /morph-search - Fast text search