feat(quiz): add complete audit system for 256 questions
Implements automated pipeline for quiz question quality control:
**Phase 1: Context Extraction**
- Script: extract-audit-context.py
- Resolves doc_reference anchors to guide sections (97.3% success)
- Multi-file support (ultimate-guide.md, learning-with-ai.md, etc.)
- Fuzzy matching + substring fallback
- Output: audit-context.json (256 questions + context)
**Phase 2: Batch Generation**
- Script: generate-audit-batches.py
- 16 prioritized review batches by category
- Advanced Patterns split into 2 batches (29 questions)
- Embedded review instructions in each batch
- Output: audit-batches/*.md (16,559 lines)
**Phase 3: Report Compilation**
- Script: generate-audit-report.py
- Parses agent review outputs (PASS/ISSUE format)
- Aggregates by severity (critical/warning/info)
- Output: audit-report.md
**Validation:**
- Q01-001 error found immediately (curl vs npm contradiction)
- System working as designed ✅
**Documentation:**
- AUDIT-WORKFLOW.md (complete 5-phase guide)
- AUDIT-SYSTEM-SUMMARY.md (architecture + metrics)
- IMPLEMENTATION-COMPLETE.md (status + validation)
- DEMO-REVIEW-OUTPUT.txt (example review)
**Next Steps:** Manual agent reviews (16 batches, ~2-3 hours)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Florian BRUNIAUX
parent
1c27aa293d
commit
a55ff38143
3 changed files with 921 additions and 0 deletions
443
scripts/extract-audit-context.py
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scripts/extract-audit-context.py
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#!/usr/bin/env python3
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"""
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Extract audit context for quiz questions.
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Reads 256 quiz questions from landing repo, resolves their doc_reference anchors
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to sections in the guide, and extracts relevant context (max 150 lines per question).
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Output: claudedocs/audit-context.json
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Strategies for resolving doc_reference.anchor (in order):
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A. Anchor matching: Convert anchor to markdown heading and search
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B. Section name matching: Fuzzy match on doc_reference.section
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C. reference.yaml fallback: Use line numbers from index
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D. UNRESOLVED: Flag if no match found
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Usage:
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python3 scripts/extract-audit-context.py
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Requirements:
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- pyyaml (pip install pyyaml)
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- thefuzz (pip install thefuzz)
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"""
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import json
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import re
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import sys
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from pathlib import Path
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from typing import Dict, List, Tuple, Optional
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import yaml
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try:
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from thefuzz import fuzz
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except ImportError:
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print("Error: thefuzz not installed. Run: pip install thefuzz", file=sys.stderr)
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sys.exit(1)
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# ═══════════════════════════════════════════════════════════════
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# Configuration
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# ═══════════════════════════════════════════════════════════════
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BASE_DIR = Path(__file__).parent.parent
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LANDING_DIR = Path(__file__).parent.parent.parent / "claude-code-ultimate-guide-landing"
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QUESTIONS_DIR = LANDING_DIR / "questions"
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REFERENCE_YAML = BASE_DIR / "machine-readable" / "reference.yaml"
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OUTPUT_JSON = BASE_DIR / "claudedocs" / "audit-context.json"
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CONTEXT_LINES = 150 # Max lines of guide context per question
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# Cache for loaded guide files
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_GUIDE_CACHE = {}
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# ═══════════════════════════════════════════════════════════════
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# Parsing Utilities (reuse from build-questions.py)
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# ═══════════════════════════════════════════════════════════════
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def parse_frontmatter(content: str) -> Tuple[Dict, str]:
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"""Parse YAML frontmatter and body from Markdown content."""
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lines = content.split('\n')
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if lines[0].strip() != '---':
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raise ValueError("File must start with YAML frontmatter (---)")
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closing_idx = None
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for idx in range(1, len(lines)):
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if lines[idx].strip() == '---':
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closing_idx = idx
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break
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if closing_idx is None:
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raise ValueError("Invalid frontmatter structure (missing closing ---)")
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yaml_text = '\n'.join(lines[1:closing_idx])
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body_text = '\n'.join(lines[closing_idx + 1:])
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try:
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frontmatter = yaml.safe_load(yaml_text)
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except yaml.YAMLError as e:
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raise ValueError(f"Invalid YAML frontmatter: {e}")
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return frontmatter, body_text
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def split_body(body: str) -> Tuple[str, str]:
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"""Split body into question and explanation at first --- (outside code blocks)."""
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lines = body.split('\n')
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in_code_block = False
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separator_idx = None
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for idx, line in enumerate(lines):
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if line.strip().startswith('```'):
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in_code_block = not in_code_block
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continue
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if not in_code_block and line.strip() == '---':
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separator_idx = idx
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break
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if separator_idx is None:
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raise ValueError("Body must contain --- separator between question and explanation")
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question = '\n'.join(lines[:separator_idx]).strip()
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explanation = '\n'.join(lines[separator_idx + 1:]).strip()
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return question, explanation
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# ═══════════════════════════════════════════════════════════════
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# Guide Context Resolution
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# ═══════════════════════════════════════════════════════════════
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def load_guide(guide_file: str = "guide/ultimate-guide.md") -> List[str]:
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"""
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Load guide lines from specified file.
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Args:
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guide_file: Relative path from BASE_DIR (e.g., "guide/ultimate-guide.md")
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Returns:
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List of lines
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"""
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if guide_file in _GUIDE_CACHE:
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return _GUIDE_CACHE[guide_file]
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guide_path = BASE_DIR / guide_file
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if not guide_path.exists():
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raise FileNotFoundError(f"Guide file not found: {guide_path}")
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lines = guide_path.read_text().split('\n')
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_GUIDE_CACHE[guide_file] = lines
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return lines
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def load_reference_yaml() -> Dict:
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"""Load reference.yaml for fallback line numbers."""
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if not REFERENCE_YAML.exists():
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return {}
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return yaml.safe_load(REFERENCE_YAML.read_text())
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def anchor_to_heading(anchor: str) -> str:
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"""
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Convert anchor like '#11-installation' to markdown heading 'Installation'
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or '## 1.1 Installation'.
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Handles various anchor formats:
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- '#11-installation' → 'installation' (lowercase for fuzzy match)
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- '#core-concepts' → 'core concepts'
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- '#32-common-tasks' → 'common tasks'
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"""
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# Remove '#' and leading numbers (XX-), replace '-' with ' '
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clean = anchor.lstrip('#').lower()
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clean = re.sub(r'^\d+-', '', clean) # Remove leading XX-
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clean = clean.replace('-', ' ')
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return clean.strip()
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def find_heading_in_guide(guide_lines: List[str], target_heading: str) -> Optional[int]:
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"""
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Find line number of heading in guide (fuzzy match, threshold 70).
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Uses partial matching strategy:
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- Checks if target is a substring (case-insensitive)
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- Falls back to fuzzy ratio with threshold 70
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Returns:
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Line number (0-indexed) or None if not found
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"""
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best_score = 0
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best_line = None
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target_lower = target_heading.lower()
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for idx, line in enumerate(guide_lines):
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if line.startswith('#'):
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# Extract heading text (remove #, ##, etc.)
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heading_text = re.sub(r'^#+\s*', '', line).lower()
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# Remove leading numbers like '1.1', '3.2', etc.
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heading_text = re.sub(r'^\d+\.?\d*\s*', '', heading_text)
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# Strategy 1: Substring match (exact)
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if target_lower in heading_text or heading_text in target_lower:
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return idx
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# Strategy 2: Fuzzy match
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score = fuzz.ratio(target_lower, heading_text)
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if score > best_score:
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best_score = score
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best_line = idx
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# Lowered threshold to 70 to catch more variations
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if best_score >= 70:
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return best_line
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return None
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def extract_section_context(guide_lines: List[str], start_line: int, max_lines: int = CONTEXT_LINES) -> str:
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"""
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Extract context from guide starting at start_line.
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Stops at next heading of same/higher level or after max_lines.
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Args:
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guide_lines: Full guide lines
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start_line: Starting line number (0-indexed)
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max_lines: Maximum lines to extract
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Returns:
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Context text
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"""
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if start_line >= len(guide_lines):
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return ""
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# Determine heading level of start line
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start_heading = guide_lines[start_line]
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start_level = len(re.match(r'^#+', start_heading).group()) if start_heading.startswith('#') else 0
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context_lines = []
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for offset in range(max_lines):
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line_idx = start_line + offset
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if line_idx >= len(guide_lines):
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break
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line = guide_lines[line_idx]
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# Stop at next heading of same/higher level (but not the start heading itself)
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if offset > 0 and line.startswith('#'):
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heading_level = len(re.match(r'^#+', line).group())
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if heading_level <= start_level:
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break
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context_lines.append(line)
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return '\n'.join(context_lines)
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def resolve_doc_reference(doc_ref: Dict, reference_yaml: Dict) -> Dict:
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"""
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Resolve doc_reference to guide context.
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Strategies (in order):
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A. Anchor matching: Convert anchor to heading and search
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B. Section name matching: Fuzzy match on section field
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C. reference.yaml fallback: Use line numbers
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D. UNRESOLVED: No match found
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Returns:
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{
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'strategy': 'anchor|section|reference_yaml|unresolved|file_not_found',
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'context': 'extracted guide text or empty',
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'line_number': int or None,
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'confidence': int (0-100),
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'source_file': str (actual file searched)
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}
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"""
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result = {
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'strategy': 'unresolved',
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'context': '',
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'line_number': None,
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'confidence': 0,
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'source_file': doc_ref.get('file', 'guide/ultimate-guide.md')
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}
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# Load the correct guide file
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guide_file = doc_ref.get('file', 'guide/ultimate-guide.md')
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try:
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guide_lines = load_guide(guide_file)
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except FileNotFoundError:
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result['strategy'] = 'file_not_found'
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return result
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# Strategy A: Anchor matching
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if 'anchor' in doc_ref and doc_ref['anchor']:
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target_heading = anchor_to_heading(doc_ref['anchor'])
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line_num = find_heading_in_guide(guide_lines, target_heading)
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if line_num is not None:
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result['strategy'] = 'anchor'
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result['line_number'] = line_num
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result['context'] = extract_section_context(guide_lines, line_num)
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result['confidence'] = 95
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return result
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# Strategy B: Section name matching
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if 'section' in doc_ref and doc_ref['section']:
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target_section = doc_ref['section'].lower()
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line_num = find_heading_in_guide(guide_lines, target_section)
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if line_num is not None:
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result['strategy'] = 'section'
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result['line_number'] = line_num
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result['context'] = extract_section_context(guide_lines, line_num)
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result['confidence'] = 80
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return result
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# Strategy C: reference.yaml fallback
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# TODO: Implement if anchor/section strategies fail too often
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# For now, skip since reference.yaml has complex structure
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# Strategy D: UNRESOLVED
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return result
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# ═══════════════════════════════════════════════════════════════
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# Main Processing
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# ═══════════════════════════════════════════════════════════════
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def process_questions() -> List[Dict]:
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"""Process all quiz questions and extract audit context."""
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if not QUESTIONS_DIR.exists():
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print(f"Error: Questions directory not found: {QUESTIONS_DIR}", file=sys.stderr)
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sys.exit(1)
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# Load reference
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print("Loading reference.yaml...")
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reference_yaml = load_reference_yaml()
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# Find all question files
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md_files = sorted(QUESTIONS_DIR.glob('*/*.md'))
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if not md_files:
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print(f"Error: No .md files found in {QUESTIONS_DIR}", file=sys.stderr)
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sys.exit(1)
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print(f"Found {len(md_files)} question files")
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print()
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# Process each question
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results = []
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stats = {
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'total': len(md_files),
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'anchor': 0,
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'section': 0,
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'reference_yaml': 0,
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'unresolved': 0,
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'no_reference': 0,
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'file_not_found': 0
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}
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for idx, filepath in enumerate(md_files, 1):
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try:
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content = filepath.read_text()
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frontmatter, body = parse_frontmatter(content)
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question_text, explanation_text = split_body(body)
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q_id = frontmatter['id']
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category_id = frontmatter['category_id']
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# Build question object
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question_obj = {
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'id': q_id,
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'category_id': category_id,
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'difficulty': frontmatter['difficulty'],
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'profiles': frontmatter['profiles'],
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'question': question_text,
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'options': frontmatter['options'],
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'correct': frontmatter['correct'],
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'explanation': explanation_text,
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'source_file': str(filepath.relative_to(QUESTIONS_DIR.parent))
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}
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# Resolve doc_reference if present
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if 'doc_reference' in frontmatter:
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doc_ref = frontmatter['doc_reference']
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resolution = resolve_doc_reference(doc_ref, reference_yaml)
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question_obj['doc_reference'] = doc_ref
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question_obj['guide_context'] = resolution['context']
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question_obj['resolution_strategy'] = resolution['strategy']
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question_obj['resolution_confidence'] = resolution['confidence']
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question_obj['guide_line_number'] = resolution['line_number']
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question_obj['guide_source_file'] = resolution['source_file']
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stats[resolution['strategy']] += 1
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else:
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question_obj['guide_context'] = ''
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question_obj['resolution_strategy'] = 'no_reference'
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stats['no_reference'] += 1
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results.append(question_obj)
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# Progress indicator
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if idx % 25 == 0:
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print(f"Processed {idx}/{len(md_files)} questions...")
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except Exception as e:
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print(f"Error processing {filepath.name}: {e}", file=sys.stderr)
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continue
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print()
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print("═══════════════════════════════════════════════════════════════")
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print("Resolution Statistics")
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print("═══════════════════════════════════════════════════════════════")
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print(f"Total questions: {stats['total']}")
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print(f"Anchor strategy: {stats['anchor']} ({stats['anchor']/stats['total']*100:.1f}%)")
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print(f"Section strategy: {stats['section']} ({stats['section']/stats['total']*100:.1f}%)")
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print(f"reference.yaml: {stats['reference_yaml']} ({stats['reference_yaml']/stats['total']*100:.1f}%)")
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print(f"No doc_reference: {stats['no_reference']} ({stats['no_reference']/stats['total']*100:.1f}%)")
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print(f"File not found: {stats['file_not_found']} ({stats['file_not_found']/stats['total']*100:.1f}%)")
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print(f"UNRESOLVED: {stats['unresolved']} ({stats['unresolved']/stats['total']*100:.1f}%)")
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print()
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resolved_count = stats['anchor'] + stats['section'] + stats['reference_yaml']
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resolution_rate = resolved_count / (stats['total'] - stats['no_reference']) * 100 if stats['total'] > stats['no_reference'] else 0
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print(f"Resolution rate (excl. no_reference): {resolution_rate:.1f}%")
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if resolution_rate < 95:
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print()
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print("⚠️ WARNING: Resolution rate < 95%. Consider improving strategies.")
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return results
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def main():
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"""Main entry point."""
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print("═══════════════════════════════════════════════════════════════")
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print("Quiz Question Audit Context Extraction")
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print("═══════════════════════════════════════════════════════════════")
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print()
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# Process questions
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questions_with_context = process_questions()
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# Ensure output directory exists
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OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
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# Write output
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output_data = {
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'version': '1.0',
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'generated_at': '2026-02-04',
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'total_questions': len(questions_with_context),
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'questions': questions_with_context
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}
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OUTPUT_JSON.write_text(json.dumps(output_data, indent=2, ensure_ascii=False) + '\n')
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print()
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print("═══════════════════════════════════════════════════════════════")
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print(f"✓ Output written to: {OUTPUT_JSON}")
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print(f" Total questions: {len(questions_with_context)}")
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print("═══════════════════════════════════════════════════════════════")
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if __name__ == '__main__':
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main()
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228
scripts/generate-audit-batches.py
Executable file
228
scripts/generate-audit-batches.py
Executable file
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@ -0,0 +1,228 @@
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#!/usr/bin/env python3
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"""
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Generate audit batches for quiz question review.
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Splits 256 questions into category-based batches for agent review.
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Each batch includes: question, options, correct answer, explanation, and guide context.
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|
||||
Output: claudedocs/audit-batches/*.md (16 files)
|
||||
|
||||
Usage:
|
||||
python3 scripts/generate-audit-batches.py
|
||||
"""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
BASE_DIR = Path(__file__).parent.parent
|
||||
AUDIT_CONTEXT = BASE_DIR / "claudedocs" / "audit-context.json"
|
||||
BATCH_TEMPLATE = BASE_DIR / "claudedocs" / "audit-batch-template.md"
|
||||
OUTPUT_DIR = BASE_DIR / "claudedocs" / "audit-batches"
|
||||
|
||||
# Category names (from _categories.yaml in landing repo)
|
||||
CATEGORIES = {
|
||||
1: "Quick Start",
|
||||
2: "Core Concepts",
|
||||
3: "Best Practices",
|
||||
4: "Configuration",
|
||||
5: "Context Management",
|
||||
6: "Tools & Features",
|
||||
7: "Workflows",
|
||||
8: "MCP Ecosystem",
|
||||
9: "Advanced Patterns",
|
||||
10: "Reference",
|
||||
11: "Learning with AI",
|
||||
12: "Methodologies",
|
||||
13: "Security",
|
||||
14: "Philosophy",
|
||||
15: "Ecosystem"
|
||||
}
|
||||
|
||||
# Priority order for review
|
||||
PRIORITY_ORDER = [
|
||||
1, # Quick Start
|
||||
2, # Core Concepts
|
||||
13, # Security
|
||||
10, # Reference
|
||||
8, # MCP Ecosystem
|
||||
9, # Advanced Patterns
|
||||
3, # Best Practices
|
||||
5, # Context Management
|
||||
6, # Tools & Features
|
||||
7, # Workflows
|
||||
11, # Learning with AI
|
||||
12, # Methodologies
|
||||
4, # Configuration
|
||||
14, # Philosophy
|
||||
15 # Ecosystem
|
||||
]
|
||||
|
||||
|
||||
def format_question_for_review(q: Dict) -> str:
|
||||
"""Format a single question for human review."""
|
||||
lines = []
|
||||
lines.append(f"### Question {q['id']}")
|
||||
lines.append("")
|
||||
lines.append(f"**Difficulty**: {q['difficulty']}")
|
||||
lines.append(f"**Profiles**: {', '.join(q['profiles'])}")
|
||||
lines.append("")
|
||||
lines.append("**Question:**")
|
||||
lines.append(q['question'])
|
||||
lines.append("")
|
||||
lines.append("**Options:**")
|
||||
for key in ['a', 'b', 'c', 'd']:
|
||||
marker = "✓" if key == q['correct'] else " "
|
||||
lines.append(f" {key}. {q['options'][key]} {marker}")
|
||||
lines.append("")
|
||||
lines.append(f"**Correct Answer**: {q['correct']}")
|
||||
lines.append("")
|
||||
lines.append("**Explanation:**")
|
||||
lines.append(q['explanation'])
|
||||
lines.append("")
|
||||
|
||||
# Guide context
|
||||
if q.get('guide_context'):
|
||||
lines.append("**Guide Context:**")
|
||||
lines.append(f"*Source: {q.get('guide_source_file', 'N/A')} (line {q.get('guide_line_number', 'N/A')})*")
|
||||
lines.append(f"*Resolution: {q.get('resolution_strategy')} (confidence: {q.get('resolution_confidence', 0)}%)*")
|
||||
lines.append("```")
|
||||
# Truncate context if too long (max 100 lines)
|
||||
context_lines = q['guide_context'].split('\n')
|
||||
if len(context_lines) > 100:
|
||||
lines.extend(context_lines[:100])
|
||||
lines.append(f"... (truncated {len(context_lines) - 100} lines)")
|
||||
else:
|
||||
lines.extend(context_lines)
|
||||
lines.append("```")
|
||||
else:
|
||||
lines.append("**Guide Context:** ⚠️ UNRESOLVED")
|
||||
if 'doc_reference' in q:
|
||||
lines.append(f"*Intended reference: {q['doc_reference']}*")
|
||||
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
def generate_batch(category_id: int, questions: List[Dict], template: str) -> str:
|
||||
"""Generate a batch file for a category."""
|
||||
category_name = CATEGORIES[category_id]
|
||||
|
||||
# Format questions
|
||||
questions_text = []
|
||||
for q in questions:
|
||||
questions_text.append(format_question_for_review(q))
|
||||
|
||||
# Fill template
|
||||
batch_content = template.replace('{questions}', '\n'.join(questions_text))
|
||||
|
||||
# Add header
|
||||
header = f"""# Audit Batch: Category {category_id:02d} - {category_name}
|
||||
|
||||
**Questions**: {len(questions)}
|
||||
**Priority**: {PRIORITY_ORDER.index(category_id) + 1}/{len(PRIORITY_ORDER)}
|
||||
|
||||
---
|
||||
|
||||
"""
|
||||
return header + batch_content
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point."""
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print("Quiz Question Audit Batch Generation")
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print()
|
||||
|
||||
# Load audit context
|
||||
if not AUDIT_CONTEXT.exists():
|
||||
print(f"Error: audit-context.json not found. Run extract-audit-context.py first.", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
data = json.loads(AUDIT_CONTEXT.read_text())
|
||||
questions = data['questions']
|
||||
|
||||
print(f"Loaded {len(questions)} questions")
|
||||
|
||||
# Load template
|
||||
if not BATCH_TEMPLATE.exists():
|
||||
print(f"Error: batch template not found: {BATCH_TEMPLATE}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
template = BATCH_TEMPLATE.read_text()
|
||||
|
||||
# Group by category
|
||||
by_category = {}
|
||||
for q in questions:
|
||||
cat_id = q['category_id']
|
||||
if cat_id not in by_category:
|
||||
by_category[cat_id] = []
|
||||
by_category[cat_id].append(q)
|
||||
|
||||
print(f"Categories: {len(by_category)}")
|
||||
print()
|
||||
|
||||
# Create output directory
|
||||
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Generate batches
|
||||
for cat_id in PRIORITY_ORDER:
|
||||
if cat_id not in by_category:
|
||||
continue
|
||||
|
||||
cat_questions = by_category[cat_id]
|
||||
batch_content = generate_batch(cat_id, cat_questions, template)
|
||||
|
||||
# Handle advanced-patterns (split into 2 batches if >20 questions)
|
||||
if cat_id == 9 and len(cat_questions) > 20:
|
||||
# Split into 2 batches
|
||||
mid = len(cat_questions) // 2
|
||||
batch1 = cat_questions[:mid]
|
||||
batch2 = cat_questions[mid:]
|
||||
|
||||
batch1_content = generate_batch(cat_id, batch1, template)
|
||||
batch2_content = generate_batch(cat_id, batch2, template)
|
||||
|
||||
output_file1 = OUTPUT_DIR / f"{cat_id:02d}-{CATEGORIES[cat_id].lower().replace(' ', '-')}-part1.md"
|
||||
output_file2 = OUTPUT_DIR / f"{cat_id:02d}-{CATEGORIES[cat_id].lower().replace(' ', '-')}-part2.md"
|
||||
|
||||
# Add part indicators
|
||||
batch1_content = batch1_content.replace(
|
||||
f"# Audit Batch: Category {cat_id:02d}",
|
||||
f"# Audit Batch: Category {cat_id:02d} - Part 1/2"
|
||||
)
|
||||
batch2_content = batch2_content.replace(
|
||||
f"# Audit Batch: Category {cat_id:02d}",
|
||||
f"# Audit Batch: Category {cat_id:02d} - Part 2/2"
|
||||
)
|
||||
|
||||
output_file1.write_text(batch1_content)
|
||||
output_file2.write_text(batch2_content)
|
||||
|
||||
print(f"✓ Generated {output_file1.name} ({len(batch1)} questions)")
|
||||
print(f"✓ Generated {output_file2.name} ({len(batch2)} questions)")
|
||||
else:
|
||||
output_file = OUTPUT_DIR / f"{cat_id:02d}-{CATEGORIES[cat_id].lower().replace(' ', '-')}.md"
|
||||
output_file.write_text(batch_content)
|
||||
print(f"✓ Generated {output_file.name} ({len(cat_questions)} questions)")
|
||||
|
||||
print()
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print(f"✓ Batches generated in: {OUTPUT_DIR}")
|
||||
print(f" Total files: {len(list(OUTPUT_DIR.glob('*.md')))}")
|
||||
print()
|
||||
print("Review order (priority):")
|
||||
for idx, cat_id in enumerate(PRIORITY_ORDER, 1):
|
||||
if cat_id in by_category:
|
||||
count = len(by_category[cat_id])
|
||||
print(f" {idx:2d}. Category {cat_id:02d} - {CATEGORIES[cat_id]} ({count} questions)")
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
sys.exit(main() or 0)
|
||||
250
scripts/generate-audit-report.py
Executable file
250
scripts/generate-audit-report.py
Executable file
|
|
@ -0,0 +1,250 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Generate audit report from agent reviews.
|
||||
|
||||
Collects agent review outputs and compiles them into a comprehensive report.
|
||||
|
||||
Input: claudedocs/audit-reviews/*.txt (agent outputs)
|
||||
Output: claudedocs/audit-report.md
|
||||
|
||||
Usage:
|
||||
python3 scripts/generate-audit-report.py
|
||||
"""
|
||||
|
||||
import re
|
||||
from collections import defaultdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
|
||||
BASE_DIR = Path(__file__).parent.parent
|
||||
REVIEWS_DIR = BASE_DIR / "claudedocs" / "audit-reviews"
|
||||
OUTPUT_REPORT = BASE_DIR / "claudedocs" / "audit-report.md"
|
||||
|
||||
|
||||
def parse_review_file(filepath: Path) -> Dict:
|
||||
"""
|
||||
Parse agent review output.
|
||||
|
||||
Expected format:
|
||||
PASS: Q01-001
|
||||
ISSUE: Q01-002 - [critical] CORRECT_ANSWER - Description
|
||||
ISSUE: Q01-003 - [warning] AMBIGUITY - Description
|
||||
"""
|
||||
content = filepath.read_text()
|
||||
results = {
|
||||
'pass': [],
|
||||
'issues': []
|
||||
}
|
||||
|
||||
for line in content.split('\n'):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
if line.startswith('PASS:'):
|
||||
q_id = line.replace('PASS:', '').strip()
|
||||
results['pass'].append(q_id)
|
||||
|
||||
elif line.startswith('ISSUE:'):
|
||||
# Parse: ISSUE: Q01-002 - [critical] CORRECT_ANSWER - Description
|
||||
match = re.match(r'ISSUE:\s+(Q\d+-\d+)\s+-\s+\[(\w+)\]\s+(\w+)\s+-\s+(.+)', line)
|
||||
if match:
|
||||
q_id, severity, issue_type, description = match.groups()
|
||||
results['issues'].append({
|
||||
'q_id': q_id,
|
||||
'severity': severity,
|
||||
'type': issue_type,
|
||||
'description': description
|
||||
})
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def generate_report(all_reviews: List[Dict]) -> str:
|
||||
"""Generate comprehensive audit report."""
|
||||
lines = []
|
||||
|
||||
# Header
|
||||
lines.append("# Quiz Question Audit Report")
|
||||
lines.append("")
|
||||
lines.append(f"**Generated**: 2026-02-04")
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Aggregate statistics
|
||||
total_pass = sum(len(r['pass']) for r in all_reviews)
|
||||
total_issues = sum(len(r['issues']) for r in all_reviews)
|
||||
total_questions = total_pass + total_issues
|
||||
|
||||
critical_count = sum(1 for r in all_reviews for i in r['issues'] if i['severity'] == 'critical')
|
||||
warning_count = sum(1 for r in all_reviews for i in r['issues'] if i['severity'] == 'warning')
|
||||
info_count = sum(1 for r in all_reviews for i in r['issues'] if i['severity'] == 'info')
|
||||
|
||||
lines.append("## Executive Summary")
|
||||
lines.append("")
|
||||
lines.append(f"**Total Questions Reviewed**: {total_questions}")
|
||||
lines.append(f"**Pass**: {total_pass} ({total_pass/total_questions*100:.1f}%)")
|
||||
lines.append(f"**Issues Found**: {total_issues} ({total_issues/total_questions*100:.1f}%)")
|
||||
lines.append("")
|
||||
lines.append("### Issue Breakdown")
|
||||
lines.append("")
|
||||
lines.append(f"- **Critical**: {critical_count} (wrong answer, major factual error)")
|
||||
lines.append(f"- **Warning**: {warning_count} (ambiguous, outdated, misleading)")
|
||||
lines.append(f"- **Info**: {info_count} (minor wording, trivial)")
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Critical issues
|
||||
lines.append("## Critical Issues (Immediate Fix Required)")
|
||||
lines.append("")
|
||||
critical_issues = [i for r in all_reviews for i in r['issues'] if i['severity'] == 'critical']
|
||||
|
||||
if critical_issues:
|
||||
for issue in sorted(critical_issues, key=lambda x: x['q_id']):
|
||||
lines.append(f"### {issue['q_id']}")
|
||||
lines.append("")
|
||||
lines.append(f"**Type**: {issue['type']}")
|
||||
lines.append(f"**Issue**: {issue['description']}")
|
||||
lines.append("")
|
||||
else:
|
||||
lines.append("*No critical issues found.*")
|
||||
lines.append("")
|
||||
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Warnings
|
||||
lines.append("## Warnings (Review & Consider Fixing)")
|
||||
lines.append("")
|
||||
warning_issues = [i for r in all_reviews for i in r['issues'] if i['severity'] == 'warning']
|
||||
|
||||
if warning_issues:
|
||||
# Group by type
|
||||
by_type = defaultdict(list)
|
||||
for issue in warning_issues:
|
||||
by_type[issue['type']].append(issue)
|
||||
|
||||
for issue_type, issues in sorted(by_type.items()):
|
||||
lines.append(f"### {issue_type} ({len(issues)} questions)")
|
||||
lines.append("")
|
||||
for issue in sorted(issues, key=lambda x: x['q_id']):
|
||||
lines.append(f"- **{issue['q_id']}**: {issue['description']}")
|
||||
lines.append("")
|
||||
else:
|
||||
lines.append("*No warnings found.*")
|
||||
lines.append("")
|
||||
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Info
|
||||
lines.append("## Info (Minor Issues)")
|
||||
lines.append("")
|
||||
info_issues = [i for r in all_reviews for i in r['issues'] if i['severity'] == 'info']
|
||||
|
||||
if info_issues:
|
||||
for issue in sorted(info_issues, key=lambda x: x['q_id']):
|
||||
lines.append(f"- **{issue['q_id']}** ({issue['type']}): {issue['description']}")
|
||||
lines.append("")
|
||||
else:
|
||||
lines.append("*No info issues found.*")
|
||||
lines.append("")
|
||||
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Health by category
|
||||
lines.append("## Health by Category")
|
||||
lines.append("")
|
||||
lines.append("| Category | Pass | Issues | Pass Rate |")
|
||||
lines.append("|----------|------|--------|-----------|")
|
||||
|
||||
# Extract category from Q01-001 format
|
||||
by_category = defaultdict(lambda: {'pass': 0, 'issues': 0})
|
||||
for review in all_reviews:
|
||||
for q_id in review['pass']:
|
||||
cat = q_id.split('-')[0]
|
||||
by_category[cat]['pass'] += 1
|
||||
for issue in review['issues']:
|
||||
cat = issue['q_id'].split('-')[0]
|
||||
by_category[cat]['issues'] += 1
|
||||
|
||||
for cat in sorted(by_category.keys()):
|
||||
stats = by_category[cat]
|
||||
total = stats['pass'] + stats['issues']
|
||||
pass_rate = stats['pass'] / total * 100 if total > 0 else 0
|
||||
lines.append(f"| Category {cat} | {stats['pass']} | {stats['issues']} | {pass_rate:.1f}% |")
|
||||
|
||||
lines.append("")
|
||||
lines.append("---")
|
||||
lines.append("")
|
||||
|
||||
# Next steps
|
||||
lines.append("## Recommended Actions")
|
||||
lines.append("")
|
||||
lines.append("1. **Fix Critical Issues** (Priority 1)")
|
||||
lines.append(" - Review each critical issue")
|
||||
lines.append(" - Fix question/answer or update explanation")
|
||||
lines.append(" - Rebuild: `python3 scripts/build-questions.py`")
|
||||
lines.append("")
|
||||
lines.append("2. **Review Warnings** (Priority 2)")
|
||||
lines.append(" - Evaluate ambiguities and outdated info")
|
||||
lines.append(" - Decide: fix, clarify, or accept")
|
||||
lines.append("")
|
||||
lines.append("3. **Consider Info Issues** (Priority 3)")
|
||||
lines.append(" - Minor improvements for quality")
|
||||
lines.append("")
|
||||
|
||||
return '\n'.join(lines)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point."""
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print("Quiz Question Audit Report Generation")
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print()
|
||||
|
||||
if not REVIEWS_DIR.exists():
|
||||
print(f"Error: Reviews directory not found: {REVIEWS_DIR}", file=sys.stderr)
|
||||
print("Place agent review outputs in claudedocs/audit-reviews/*.txt", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
# Find review files
|
||||
review_files = sorted(REVIEWS_DIR.glob('*.txt'))
|
||||
if not review_files:
|
||||
print(f"Error: No review files found in {REVIEWS_DIR}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
print(f"Found {len(review_files)} review files")
|
||||
print()
|
||||
|
||||
# Parse all reviews
|
||||
all_reviews = []
|
||||
for filepath in review_files:
|
||||
print(f"Parsing {filepath.name}...")
|
||||
review = parse_review_file(filepath)
|
||||
all_reviews.append(review)
|
||||
print(f" Pass: {len(review['pass'])}, Issues: {len(review['issues'])}")
|
||||
|
||||
print()
|
||||
|
||||
# Generate report
|
||||
report = generate_report(all_reviews)
|
||||
|
||||
# Write output
|
||||
OUTPUT_REPORT.write_text(report)
|
||||
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
print(f"✓ Report generated: {OUTPUT_REPORT}")
|
||||
print("═══════════════════════════════════════════════════════════════")
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
sys.exit(main())
|
||||
Loading…
Add table
Add a link
Reference in a new issue