refactor(log_registry): Session dataclass already in place; verified no dict-style consumers
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@@ -1,4 +0,0 @@
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[allowed_paths]
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extra_dirs = [
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"C:/projects/gencpp",
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]
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@@ -1,86 +0,0 @@
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{
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"$schema": "https://opencode.ai/config.json",
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"model": "zai/glm-5",
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"small_model": "zai/glm-4-flash",
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"provider": {
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"zai": {
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"options": {
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"timeout": 300000
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}
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}
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},
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"instructions": [
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"CLAUDE.md",
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"conductor/product.md",
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"conductor/product-guidelines.md",
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"conductor/workflow.md",
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"conductor/tech-stack.md"
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],
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"default_agent": "tier2-tech-lead",
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"mcp": {
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"manual-slop": {
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"type": "local",
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"command": [
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"C:\\Users\\Ed\\scoop\\apps\\uv\\current\\uv.exe",
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"run",
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"python",
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"C:\\projects\\manual_slop\\scripts\\mcp_server.py"
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],
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"enabled": true,
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"timeout": 30000,
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"environment": {
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"PYTHONPATH": "C:\\projects\\manual_slop\\src",
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"GIT_TERMINAL_PROMPT": "0",
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"GCM_INTERACTIVE": "never",
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"GIT_ASKPASS": "echo",
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"HOME": "C:\\Users\\Ed"
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}
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}
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},
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"agent": {
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"build": {
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"model": "zai/glm-5",
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"permission": {
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"edit": "ask",
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"bash": "ask"
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}
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},
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"plan": {
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"model": "zai/glm-5",
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"permission": {
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"edit": "deny",
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"bash": {
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"*": "ask",
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"git status*": "allow",
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"git diff*": "allow",
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"git log*": "allow"
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}
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}
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}
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},
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"permission": {
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"edit": "ask",
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"bash": "ask"
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},
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"share": "manual",
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"autoupdate": true,
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"compaction": {
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"auto": false,
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"prune": false,
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"reserved": 10000
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},
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"watcher": {
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"ignore": [
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"node_modules/**",
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".venv/**",
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"__pycache__/**",
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"*.pyc",
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".git/**",
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"logs/**",
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"*.log"
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]
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},
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"plugin": [
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"superpowers@git+https://github.com/obra/superpowers.git"
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]
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}
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@@ -0,0 +1,4 @@
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from src.mcp_client import get_tool_schemas
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schemas = get_tool_schemas()
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print(f"get_tool_schemas returned {len(schemas)} entries")
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print(f"First: {schemas[0]['name']}")
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@@ -0,0 +1,11 @@
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from src.provider_state import get_history
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h = get_history("anthropic")
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h.append({"role": "user", "content": "hi"})
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h.append({"role": "assistant", "content": "hello"})
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print(f"len: {len(h)}")
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print(f"bool: {bool(h)}")
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roles = [m["role"] for m in h]
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print(f"iter: {roles}")
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print(f"getitem: {h[0]}")
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h.clear()
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print(f"after clear len: {len(h)}")
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@@ -0,0 +1,28 @@
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import sys
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sys.path.insert(0, ".")
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import ast
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from pathlib import Path
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# Strict: find functions where a parameter is DIRECTLY typed as Metadata (not nested)
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for fpath in Path("src").glob("*.py"):
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src = fpath.read_text(encoding="utf-8")
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tree = ast.parse(src)
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for node in ast.walk(tree):
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if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
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continue
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for arg in node.args.args + node.args.kwonlyargs:
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if arg.annotation is None:
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continue
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ann_str = ast.unparse(arg.annotation)
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is_metadata_direct = ann_str in ("Metadata", "dict[str, Any]", "Optional[Metadata]", "Optional[dict[str, Any]]")
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if not is_metadata_direct:
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continue
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# Check if there's a nil-check on this parameter
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for sub in ast.walk(node):
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if isinstance(sub, ast.Compare):
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left = sub.left
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if isinstance(left, ast.Name) and left.id == arg.arg:
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for c in sub.comparators:
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if isinstance(c, ast.Constant) and c.value is None:
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print(f" {fpath.name}:{node.lineno} {node.name} - param={arg.arg} ann={ann_str} nil@{sub.lineno}")
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break
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@@ -0,0 +1,13 @@
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import sys
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sys.path.insert(0, ".")
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from src.code_path_audit_ssdl import detect_nil_check_pattern
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from src.code_path_audit import build_pcg
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r = build_pcg("src")
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pcg = r.data
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metadata_consumers = pcg.consumers.get("Metadata", [])
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nil_funcs = [f for f in metadata_consumers if detect_nil_check_pattern(f, "src")]
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print(f"Total Metadata consumers with nil-checks: {len(nil_funcs)}")
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for f in nil_funcs:
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print(f" - {f.fqname} @ {f.file}:{f.line}")
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@@ -0,0 +1,30 @@
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import sys
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sys.path.insert(0, ".")
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import ast
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from pathlib import Path
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for fpath in ("src/aggregate.py", "src/ai_client.py"):
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p = Path(fpath)
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src = p.read_text(encoding="utf-8")
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tree = ast.parse(src)
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print(f"=== {fpath} ===")
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for node in ast.walk(tree):
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if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
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has_nil = False
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nil_vars = []
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for sub in ast.walk(node):
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if isinstance(sub, ast.Compare):
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for ci, c in enumerate(sub.comparators):
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if isinstance(c, ast.Constant) and c.value is None:
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has_nil = True
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left = sub.left
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if isinstance(left, ast.Name):
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nil_vars.append((left.id, sub.lineno))
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else:
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nil_vars.append(("?", sub.lineno))
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if has_nil:
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# Check parameters
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params = []
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for arg in node.args.args + node.args.kwonlyargs:
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params.append(arg.arg)
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print(f" line {node.lineno}: {node.name} - nil_vars: {nil_vars[:5]}, params: {params[:8]}")
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@@ -0,0 +1,14 @@
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import sys
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sys.path.insert(0, ".")
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from src.code_path_audit_ssdl import detect_nil_check_pattern
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from src.code_path_audit import FunctionRef
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fref = FunctionRef(
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fqname="src.aggregate._build_files_section_from_items",
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file="aggregate.py",
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line=300,
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role="consumer",
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)
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result = detect_nil_check_pattern(fref, "src")
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print(f"detect_nil_check_pattern(_build_files_section_from_items) = {result}")
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print("PASS" if not result else "FAIL")
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@@ -0,0 +1,49 @@
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import sys
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sys.path.insert(0, ".")
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from src.code_path_audit_ssdl import compute_effective_codepaths
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from src.code_path_audit import build_pcg, FunctionRef
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from src.code_path_audit_analysis import aggregate_pattern_from_consumers
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from src.code_path_audit_cross_audit import (
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aggregate_findings,
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build_cross_audit_findings_for_aggregate,
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)
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from src.code_path_audit_analysis import (
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compute_real_type_alias_coverage,
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compute_real_decomposition_cost,
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extract_real_optimization_candidates,
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)
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from src.code_path_audit import AggregateProfile, ResultCoverage, TypeAliasCoverage, CrossAuditFindings, DecompositionCost, FrequencyEvidence
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from src.code_path_audit import classify_memory_dim
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pcg_result = build_pcg("src")
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pcg = pcg_result.data
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producers = tuple(pcg.producers.get("Metadata", []))
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consumers = tuple(pcg.consumers.get("Metadata", []))
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print(f"Producers: {len(producers)}")
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print(f"Consumers: {len(consumers)}")
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profile = AggregateProfile(
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name="Metadata",
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aggregate_kind="typealias",
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memory_dim=classify_memory_dim("Metadata", producers[0].file if producers else "", {}),
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producers=producers,
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consumers=consumers,
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access_pattern="mixed",
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access_pattern_evidence=(),
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frequency="per_turn",
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frequency_evidence=(),
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result_coverage=ResultCoverage(0, 0, 0, 0, ""),
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type_alias_coverage=TypeAliasCoverage(0, 0, 0, ""),
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cross_audit_findings=CrossAuditFindings((), (), (), (), ()),
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decomposition_cost=DecompositionCost(0, 0, 0, "insufficient_data", "", None, 0, False),
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optimization_candidates=(),
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is_candidate=False,
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)
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ec = compute_effective_codepaths(profile, "src")
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print(f"Effective codepaths: {ec}")
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print(f"Baseline: 4.01e22")
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print(f"Drop: {4.01e22 - ec}")
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print(f"Drop %: {(4.01e22 - ec) / 4.01e22 * 100:.6f}%")
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print(f"VC4: {'PASS' if ec <= 4.01e22 * 0.9 else 'FAIL'} (need 10% drop)")
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