firefrost-gaming/skill-seekers-reference
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style: Format all Python files with ruff

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- Formatted 103 files to comply with ruff format requirements
- No code logic changes, only formatting/whitespace
- Fixes CI formatting check failures
This commit is contained in:
yusyus
2026-02-08 14:42:27 +03:00
parent 6e4f623b9d
commit 0265de5816
103 changed files with 2241 additions and 2627 deletions
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101
tests/test_adaptor_benchmarks.py
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@@ -85,9 +85,17 @@ class TestAdaptorBenchmarks(unittest.TestCase):
# Platforms to benchmark
platforms = [
"claude", "gemini", "openai", "markdown", # IDE integrations
"langchain", "llama-index", "haystack", # RAG frameworks
"weaviate", "chroma", "faiss", "qdrant" # Vector DBs
"claude",
"gemini",
"openai",
"markdown", # IDE integrations
"langchain",
"llama-index",
"haystack", # RAG frameworks
"weaviate",
"chroma",
"faiss",
"qdrant", # Vector DBs
]
results = {}
@@ -115,20 +123,19 @@ class TestAdaptorBenchmarks(unittest.TestCase):
min_time = min(times)
max_time = max(times)
results[platform] = {
"avg": avg_time,
"min": min_time,
"max": max_time
}
results[platform] = {"avg": avg_time, "min": min_time, "max": max_time}
print(f"{platform:15} - Avg: {avg_time*1000:6.2f}ms | "
f"Min: {min_time*1000:6.2f}ms | Max: {max_time*1000:6.2f}ms")
print(
f"{platform:15} - Avg: {avg_time * 1000:6.2f}ms | "
f"Min: {min_time * 1000:6.2f}ms | Max: {max_time * 1000:6.2f}ms"
)
# Performance assertions (should complete in reasonable time)
for platform, metrics in results.items():
self.assertLess(
metrics["avg"], 0.5, # Should average < 500ms
f"{platform} format_skill_md too slow: {metrics['avg']*1000:.2f}ms"
metrics["avg"],
0.5, # Should average < 500ms
f"{platform} format_skill_md too slow: {metrics['avg'] * 1000:.2f}ms",
)
def test_benchmark_package_operations(self):
@@ -158,12 +165,9 @@ class TestAdaptorBenchmarks(unittest.TestCase):
# Get file size
file_size_kb = package_path.stat().st_size / 1024
results[platform] = {
"time": elapsed,
"size_kb": file_size_kb
}
results[platform] = {"time": elapsed, "size_kb": file_size_kb}
print(f"{platform:15} - Time: {elapsed*1000:7.2f}ms | Size: {file_size_kb:7.1f} KB")
print(f"{platform:15} - Time: {elapsed * 1000:7.2f}ms | Size: {file_size_kb:7.1f} KB")
# Validate output
self.assertTrue(package_path.exists())
@@ -171,12 +175,14 @@ class TestAdaptorBenchmarks(unittest.TestCase):
# Performance assertions
for platform, metrics in results.items():
self.assertLess(
metrics["time"], 1.0, # Should complete < 1 second
f"{platform} packaging too slow: {metrics['time']*1000:.2f}ms"
metrics["time"],
1.0, # Should complete < 1 second
f"{platform} packaging too slow: {metrics['time'] * 1000:.2f}ms",
)
self.assertLess(
metrics["size_kb"], 1000, # Should be < 1MB for 10 refs
f"{platform} package too large: {metrics['size_kb']:.1f}KB"
metrics["size_kb"],
1000, # Should be < 1MB for 10 refs
f"{platform} package too large: {metrics['size_kb']:.1f}KB",
)
def test_benchmark_scaling_with_reference_count(self):
@@ -210,14 +216,18 @@ class TestAdaptorBenchmarks(unittest.TestCase):
json.loads(formatted)
size_kb = len(formatted) / 1024
results.append({
"count": ref_count,
"time": elapsed,
"time_per_ref": time_per_ref,
"size_kb": size_kb
})
results.append(
{
"count": ref_count,
"time": elapsed,
"time_per_ref": time_per_ref,
"size_kb": size_kb,
}
)
print(f"{ref_count:4} | {elapsed*1000:10.2f} | {time_per_ref*1000:10.3f} | {size_kb:10.1f}")
print(
f"{ref_count:4} | {elapsed * 1000:10.2f} | {time_per_ref * 1000:10.3f} | {size_kb:10.1f}"
)
# Analyze scaling behavior
# Time per ref should not increase significantly (linear scaling)
@@ -230,10 +240,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
print(f"(Time per ref at 50 refs / Time per ref at 1 ref)")
# Assert linear or sub-linear scaling (not exponential)
self.assertLess(
scaling_factor, 3.0,
f"Non-linear scaling detected: {scaling_factor:.2f}x"
)
self.assertLess(scaling_factor, 3.0, f"Non-linear scaling detected: {scaling_factor:.2f}x")
def test_benchmark_json_vs_zip_size_comparison(self):
"""Compare output sizes: JSON vs ZIP/tar.gz"""
@@ -263,16 +270,15 @@ class TestAdaptorBenchmarks(unittest.TestCase):
size_kb = package_path.stat().st_size / 1024
results[platform] = {
"format": format_name,
"size_kb": size_kb
}
results[platform] = {"format": format_name, "size_kb": size_kb}
print(f"{platform:15} | {format_name:8} | {size_kb:10.1f}")
# Analyze results
json_sizes = [v["size_kb"] for k, v in results.items() if v["format"] == "JSON"]
compressed_sizes = [v["size_kb"] for k, v in results.items() if v["format"] in ["ZIP", "tar.gz"]]
compressed_sizes = [
v["size_kb"] for k, v in results.items() if v["format"] in ["ZIP", "tar.gz"]
]
if json_sizes and compressed_sizes:
avg_json = sum(json_sizes) / len(json_sizes)
@@ -280,7 +286,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
print(f"\nAverage JSON size: {avg_json:.1f} KB")
print(f"Average compressed size: {avg_compressed:.1f} KB")
print(f"Compression ratio: {avg_json/avg_compressed:.2f}x")
print(f"Compression ratio: {avg_json / avg_compressed:.2f}x")
def test_benchmark_metadata_overhead(self):
"""Measure metadata processing overhead"""
@@ -299,7 +305,7 @@ class TestAdaptorBenchmarks(unittest.TestCase):
description="A comprehensive test skill for benchmarking purposes",
version="2.5.0",
author="Benchmark Suite",
tags=["test", "benchmark", "performance", "validation", "quality"]
tags=["test", "benchmark", "performance", "validation", "quality"],
)
adaptor = get_adaptor("langchain")
@@ -326,15 +332,12 @@ class TestAdaptorBenchmarks(unittest.TestCase):
overhead = avg_rich - avg_minimal
overhead_pct = (overhead / avg_minimal) * 100
print(f"\nMinimal metadata: {avg_minimal*1000:.2f}ms")
print(f"Rich metadata: {avg_rich*1000:.2f}ms")
print(f"Overhead: {overhead*1000:.2f}ms ({overhead_pct:.1f}%)")
print(f"\nMinimal metadata: {avg_minimal * 1000:.2f}ms")
print(f"Rich metadata: {avg_rich * 1000:.2f}ms")
print(f"Overhead: {overhead * 1000:.2f}ms ({overhead_pct:.1f}%)")
# Overhead should be negligible (< 10%)
self.assertLess(
overhead_pct, 10.0,
f"Metadata overhead too high: {overhead_pct:.1f}%"
)
self.assertLess(overhead_pct, 10.0, f"Metadata overhead too high: {overhead_pct:.1f}%")
def test_benchmark_empty_vs_full_skill(self):
"""Compare performance: empty skill vs full skill"""
@@ -360,9 +363,9 @@ class TestAdaptorBenchmarks(unittest.TestCase):
adaptor.format_skill_md(full_dir, metadata)
full_time = time.perf_counter() - start
print(f"\nEmpty skill: {empty_time*1000:.2f}ms")
print(f"Full skill (50 refs): {full_time*1000:.2f}ms")
print(f"Ratio: {full_time/empty_time:.1f}x")
print(f"\nEmpty skill: {empty_time * 1000:.2f}ms")
print(f"Full skill (50 refs): {full_time * 1000:.2f}ms")
print(f"Ratio: {full_time / empty_time:.1f}x")
# Empty should be very fast
self.assertLess(empty_time, 0.01, "Empty skill processing too slow")