SKILL.md
Search Tool Hierarchy
Use the most token-efficient search tool for each query type.
Decision Tree
Query Type?
├── STRUCTURAL (code patterns)
│ → AST-grep (~50 tokens output)
│ Examples: "def foo", "class Bar", "import X", "@decorator"
│
├── SEMANTIC (conceptual questions)
│ → LEANN (~100 tokens if path-only)
│ Examples: "how does auth work", "find error handling patterns"
│
├── LITERAL (exact identifiers)
│ → Grep (variable output)
│ Examples: "TemporalMemory", "check_evocation", regex patterns
│
└── FULL CONTEXT (need complete understanding)
→ Read (1500+ tokens)
Last resort after finding the right fileToken Efficiency Comparison
Tool
Output Size
Best For
AST-grep
~50 tokens
Function/class definitions, imports, decorators
LEANN
~100 tokens
Conceptual questions, architecture, patterns
Grep
~200-2000
Exact identifiers, regex, file paths
Read
~1500+
Full understanding after finding the file
Hook Enforcement
The grep-to-leann.sh hook automatically:
- Detects query type (structural/semantic/literal)
- Blocks and suggests AST-grep for structural queries
- Blocks and suggests LEANN for semantic queries
- Allows literal patterns through to Grep
DO
- Start with AST-grep for code structure questions
- Use LEANN for "how does X work" questions
- Use Grep only for exact identifier matches
- Read files only after finding them via search
DON'T
- Use Grep for conceptual questions (returns nothing)
- Read files before knowing which ones are relevant
- Use Read when AST-grep would give file:line
- Ignore hook suggestions
Examples
# STRUCTURAL → AST-grep
ast-grep --pattern "async def $FUNC($$$):" --lang python
# SEMANTIC → LEANN
leann search opc-dev "how does authentication work" --top-k 3
# LITERAL → Grep
Grep pattern="check_evocation" path=opc/scripts
# FULL CONTEXT → Read (after finding file)
Read file_path=opc/scripts/z3_erotetic.pyOptimal 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