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.claude/skills/search-tools/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -44% | 0% |
When searching code, use this decision tree:
Need CONCEPTUAL/SEMANTIC search?
(how does X work, find patterns, understand architecture)
→ Use LEANN (/leann-search) - embedding-based semantic search
→ PreToolUse hook auto-redirects semantic Grep queries
Need to understand code STRUCTURE?
(find function calls, class usages, refactor patterns)
→ Use AST-grep (/ast-grep-find)
Need to find TEXT in code?
→ Use Morph (/morph-search) - 20x faster
→ If no Morph API key: fall back to Grep tool
Simple one-off search?
→ Use built-in Grep tool directly| Tool | Best For | Requires | |------|----------|----------| | LEANN | Semantic search: "how does caching work", "error handling patterns", conceptual queries | Index built | | AST-grep | Structural patterns: "find all calls to foo()", refactoring, find usages by type | MCP server | | Morph | Fast text search: "find files mentioning error", grep across codebase | API key | | Grep | Literal patterns, class/function names, regex | Nothing (built-in) |
LEANN (semantic/conceptual):
AST-grep (structural):
Morph (text search):
Grep (literal):
class ProviderAdapterdef __init__bash# Search with semantic query leann search opc-dev "how does blackboard communication work" --top-k 5 # List available indexes leann list # Rebuild index (when code changes) leann build opc-dev --docs dir1 dir2 --no-recompute --no-compact --force
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 26,728 | 3,315 | -88% | 1 | 1 | 0% | 2,350 | 1,053 | -55% | 0 | 0 | — |
case-02 | fail→pass | 3,914 | 2,493 | -36% | 1 | 1 | 0% | 717 | 935 | +30% | 0 | 0 | — |
case-03 | pass→pass | 4,426 | 10,076 | +128% | 1 | 1 | 0% | 840 | 1,311 | +56% | 0 | 0 | — |
case-04 | pass→pass | 5,990 | 3,674 | -39% | 1 | 1 | 0% | 1,089 | 1,112 | +2% | 0 | 0 | — |
case-05 | pass→pass | 2,559 | 2,161 | -16% | 1 | 1 | 0% | 507 | 802 | +58% | 0 | 0 | — |
case-06 | pass→pass | 13,459 | 3,491 | -74% | 1 | 1 | 0% | 2,312 | 1,104 | -52% | 0 | 0 | — |
case-07 | pass→pass | 10,043 | 2,524 | -75% | 1 | 1 | 0% | 1,617 | 830 | -49% | 0 | 0 | — |
case-08 | fail→pass | 8,945 | 3,681 | -59% | 1 | 1 | 0% | 1,448 | 1,135 | -22% | 0 | 0 | — |
case-09 | fail→pass | 11,733 | 2,827 | -76% | 1 | 1 | 0% | 1,963 | 994 | -49% | 0 | 0 | — |
case-10 | pass→pass | 5,215 | 2,537 | -51% | 1 | 1 | 0% | 883 | 898 | +2% | 0 | 0 | — |
case-11 | pass→pass | 7,556 | 2,362 | -69% | 1 | 1 | 0% | 1,400 | 908 | -35% | 0 | 0 | — |
case-12 | fail→pass | 8,659 | 2,272 | -74% | 1 | 1 | 0% | 1,597 | 887 | -44% | 0 | 0 | — |
case-13 | fail→pass | 14,076 | 4,411 | -69% | 1 | 1 | 0% | 2,384 | 740 | -69% | 0 | 0 | — |
case-14 | fail→pass | 5,399 | 1,506 | -72% | 1 | 1 | 0% | 1,006 | 760 | -24% | 0 | 0 | — |
case-15 | pass→pass | 6,482 | 1,260 | -81% | 1 | 1 | 0% | 1,117 | 687 | -38% | 0 | 0 | — |
case-16 | fail→pass | 6,539 | 2,706 | -59% | 1 | 1 | 0% | 1,226 | 1,058 | -14% | 0 | 0 | — |
case-17 | fail→pass | 4,437 | 1,794 | -60% | 1 | 1 | 0% | 752 | 749 | -0% | 0 | 0 | — |
case-18 | pass→pass | 10,125 | 1,838 | -82% | 1 | 1 | 0% | 1,673 | 756 | -55% | 0 | 0 | — |
case-19 | fail→pass | 16,656 | 2,337 | -86% | 1 | 1 | 0% | 2,637 | 872 | -67% | 0 | 0 | — |
case-20 | pass→pass | 10,593 | 2,639 | -75% | 1 | 1 | 0% | 1,812 | 955 | -47% | 0 | 0 | — |
case-21 | pass→pass | 6,340 | 1,684 | -73% | 1 | 1 | 0% | 1,014 | 746 | -26% | 0 | 0 | — |
case-22 | pass→pass | 10,848 | 2,387 | -78% | 1 | 1 | 0% | 1,659 | 911 | -45% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +45 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 7/29/2026 | +41% |
Other measured skills in the registry, with their headline benchmark lift.