feat: Calculer automatiquement les moyennes après chaque saisie de notes

Les enseignants ont besoin de moyennes à jour immédiatement après la publication ou modification des notes, sans attendre un batch nocturne. Le système recalcule via Domain Events synchrones : statistiques d'évaluation (min/max/moyenne/médiane), moyennes matières pondérées (normalisation /20), et moyenne générale par élève. Les résultats sont stockés dans des tables dénormalisées avec cache Redis (TTL 5 min). Trois endpoints API exposent les données avec contrôle d'accès par rôle. Une commande console permet le backfill des données historiques au déploiement.
2026-03-30 06:22:03 +02:00
parent b70d5ec2ad
commit b7dc27f2a5
786 changed files with 118783 additions and 316 deletions
--- a/.agents/skills/bmad-agent-builder/scripts/prepass-prompt-metrics.py
+++ b/.agents/skills/bmad-agent-builder/scripts/prepass-prompt-metrics.py
@@ -0,0 +1,403 @@
+#!/usr/bin/env python3
+"""Deterministic pre-pass for prompt craft scanner (agent builder).
+
+Extracts metrics and flagged patterns from SKILL.md and prompt files
+so the LLM scanner can work from compact data instead of reading raw files.
+
+Covers:
+- SKILL.md line count and section inventory
+- Overview section size
+- Inline data detection (tables, fenced code blocks)
+- Defensive padding pattern grep
+- Meta-explanation pattern grep
+- Back-reference detection ("as described above")
+- Config header and progression condition presence per prompt
+- File-level token estimates (chars / 4 rough approximation)
+- Prompt frontmatter validation (name, description, menu-code)
+- Wall-of-text detection
+- Suggestive loading grep
+"""
+
+# /// script
+# requires-python = ">=3.9"
+# ///
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+
+# Defensive padding / filler patterns
+WASTE_PATTERNS = [
+    (r'\b[Mm]ake sure (?:to|you)\b', 'defensive-padding', 'Defensive: "make sure to/you"'),
+    (r"\b[Dd]on'?t forget (?:to|that)\b", 'defensive-padding', "Defensive: \"don't forget\""),
+    (r'\b[Rr]emember (?:to|that)\b', 'defensive-padding', 'Defensive: "remember to/that"'),
+    (r'\b[Bb]e sure to\b', 'defensive-padding', 'Defensive: "be sure to"'),
+    (r'\b[Pp]lease ensure\b', 'defensive-padding', 'Defensive: "please ensure"'),
+    (r'\b[Ii]t is important (?:to|that)\b', 'defensive-padding', 'Defensive: "it is important"'),
+    (r'\b[Yy]ou are an AI\b', 'meta-explanation', 'Meta: "you are an AI"'),
+    (r'\b[Aa]s a language model\b', 'meta-explanation', 'Meta: "as a language model"'),
+    (r'\b[Aa]s an AI assistant\b', 'meta-explanation', 'Meta: "as an AI assistant"'),
+    (r'\b[Tt]his (?:workflow|skill|process) is designed to\b', 'meta-explanation', 'Meta: "this workflow is designed to"'),
+    (r'\b[Tt]he purpose of this (?:section|step) is\b', 'meta-explanation', 'Meta: "the purpose of this section is"'),
+    (r"\b[Ll]et'?s (?:think about|begin|start)\b", 'filler', "Filler: \"let's think/begin\""),
+    (r'\b[Nn]ow we(?:\'ll| will)\b', 'filler', "Filler: \"now we'll\""),
+]
+
+# Back-reference patterns (self-containment risk)
+BACKREF_PATTERNS = [
+    (r'\bas described above\b', 'Back-reference: "as described above"'),
+    (r'\bper the overview\b', 'Back-reference: "per the overview"'),
+    (r'\bas mentioned (?:above|in|earlier)\b', 'Back-reference: "as mentioned above/in/earlier"'),
+    (r'\bsee (?:above|the overview)\b', 'Back-reference: "see above/the overview"'),
+    (r'\brefer to (?:the )?(?:above|overview|SKILL)\b', 'Back-reference: "refer to above/overview"'),
+]
+
+# Suggestive loading patterns
+SUGGESTIVE_LOADING_PATTERNS = [
+    (r'\b[Ll]oad (?:the |all )?(?:relevant|necessary|needed|required)\b', 'Suggestive loading: "load relevant/necessary"'),
+    (r'\b[Rr]ead (?:the |all )?(?:relevant|necessary|needed|required)\b', 'Suggestive loading: "read relevant/necessary"'),
+    (r'\b[Gg]ather (?:the |all )?(?:relevant|necessary|needed)\b', 'Suggestive loading: "gather relevant/necessary"'),
+]
+
+
+def count_tables(content: str) -> tuple[int, int]:
+    """Count markdown tables and their total lines."""
+    table_count = 0
+    table_lines = 0
+    in_table = False
+    for line in content.split('\n'):
+        if '|' in line and re.match(r'^\s*\|', line):
+            if not in_table:
+                table_count += 1
+                in_table = True
+            table_lines += 1
+        else:
+            in_table = False
+    return table_count, table_lines
+
+
+def count_fenced_blocks(content: str) -> tuple[int, int]:
+    """Count fenced code blocks and their total lines."""
+    block_count = 0
+    block_lines = 0
+    in_block = False
+    for line in content.split('\n'):
+        if line.strip().startswith('```'):
+            if in_block:
+                in_block = False
+            else:
+                in_block = True
+                block_count += 1
+        elif in_block:
+            block_lines += 1
+    return block_count, block_lines
+
+
+def extract_overview_size(content: str) -> int:
+    """Count lines in the ## Overview section."""
+    lines = content.split('\n')
+    in_overview = False
+    overview_lines = 0
+    for line in lines:
+        if re.match(r'^##\s+Overview\b', line):
+            in_overview = True
+            continue
+        elif in_overview and re.match(r'^##\s', line):
+            break
+        elif in_overview:
+            overview_lines += 1
+    return overview_lines
+
+
+def detect_wall_of_text(content: str) -> list[dict]:
+    """Detect long runs of text without headers or breaks."""
+    walls = []
+    lines = content.split('\n')
+    run_start = None
+    run_length = 0
+
+    for i, line in enumerate(lines, 1):
+        stripped = line.strip()
+        is_break = (
+            not stripped
+            or re.match(r'^#{1,6}\s', stripped)
+            or re.match(r'^[-*]\s', stripped)
+            or re.match(r'^\d+\.\s', stripped)
+            or stripped.startswith('```')
+            or stripped.startswith('|')
+        )
+
+        if is_break:
+            if run_length >= 15:
+                walls.append({
+                    'start_line': run_start,
+                    'length': run_length,
+                })
+            run_start = None
+            run_length = 0
+        else:
+            if run_start is None:
+                run_start = i
+            run_length += 1
+
+    if run_length >= 15:
+        walls.append({
+            'start_line': run_start,
+            'length': run_length,
+        })
+
+    return walls
+
+
+def parse_prompt_frontmatter(filepath: Path) -> dict:
+    """Parse YAML frontmatter from a prompt file and validate."""
+    content = filepath.read_text(encoding='utf-8')
+    result = {
+        'has_frontmatter': False,
+        'fields': {},
+        'missing_fields': [],
+    }
+
+    fm_match = re.match(r'^---\s*\n(.*?)\n---\s*\n', content, re.DOTALL)
+    if not fm_match:
+        result['missing_fields'] = ['name', 'description', 'menu-code']
+        return result
+
+    result['has_frontmatter'] = True
+
+    try:
+        import yaml
+        fm = yaml.safe_load(fm_match.group(1))
+    except Exception:
+        # Fallback: simple key-value parsing
+        fm = {}
+        for line in fm_match.group(1).split('\n'):
+            if ':' in line:
+                key, _, val = line.partition(':')
+                fm[key.strip()] = val.strip()
+
+    if not isinstance(fm, dict):
+        result['missing_fields'] = ['name', 'description', 'menu-code']
+        return result
+
+    expected_fields = ['name', 'description', 'menu-code']
+    for field in expected_fields:
+        if field in fm:
+            result['fields'][field] = fm[field]
+        else:
+            result['missing_fields'].append(field)
+
+    return result
+
+
+def scan_file_patterns(filepath: Path, rel_path: str) -> dict:
+    """Extract metrics and pattern matches from a single file."""
+    content = filepath.read_text(encoding='utf-8')
+    lines = content.split('\n')
+    line_count = len(lines)
+
+    # Token estimate (rough: chars / 4)
+    token_estimate = len(content) // 4
+
+    # Section inventory
+    sections = []
+    for i, line in enumerate(lines, 1):
+        m = re.match(r'^(#{2,3})\s+(.+)$', line)
+        if m:
+            sections.append({'level': len(m.group(1)), 'title': m.group(2).strip(), 'line': i})
+
+    # Tables and code blocks
+    table_count, table_lines = count_tables(content)
+    block_count, block_lines = count_fenced_blocks(content)
+
+    # Pattern matches
+    waste_matches = []
+    for pattern, category, label in WASTE_PATTERNS:
+        for m in re.finditer(pattern, content):
+            line_num = content[:m.start()].count('\n') + 1
+            waste_matches.append({
+                'line': line_num,
+                'category': category,
+                'pattern': label,
+                'context': lines[line_num - 1].strip()[:100],
+            })
+
+    backref_matches = []
+    for pattern, label in BACKREF_PATTERNS:
+        for m in re.finditer(pattern, content, re.IGNORECASE):
+            line_num = content[:m.start()].count('\n') + 1
+            backref_matches.append({
+                'line': line_num,
+                'pattern': label,
+                'context': lines[line_num - 1].strip()[:100],
+            })
+
+    # Suggestive loading
+    suggestive_loading = []
+    for pattern, label in SUGGESTIVE_LOADING_PATTERNS:
+        for m in re.finditer(pattern, content, re.IGNORECASE):
+            line_num = content[:m.start()].count('\n') + 1
+            suggestive_loading.append({
+                'line': line_num,
+                'pattern': label,
+                'context': lines[line_num - 1].strip()[:100],
+            })
+
+    # Config header
+    has_config_header = '{communication_language}' in content or '{document_output_language}' in content
+
+    # Progression condition
+    prog_keywords = ['progress', 'advance', 'move to', 'next stage',
+                     'when complete', 'proceed to', 'transition', 'completion criteria']
+    has_progression = any(kw in content.lower() for kw in prog_keywords)
+
+    # Wall-of-text detection
+    walls = detect_wall_of_text(content)
+
+    result = {
+        'file': rel_path,
+        'line_count': line_count,
+        'token_estimate': token_estimate,
+        'sections': sections,
+        'table_count': table_count,
+        'table_lines': table_lines,
+        'fenced_block_count': block_count,
+        'fenced_block_lines': block_lines,
+        'waste_patterns': waste_matches,
+        'back_references': backref_matches,
+        'suggestive_loading': suggestive_loading,
+        'has_config_header': has_config_header,
+        'has_progression': has_progression,
+        'wall_of_text': walls,
+    }
+
+    return result
+
+
+def scan_prompt_metrics(skill_path: Path) -> dict:
+    """Extract metrics from all prompt-relevant files."""
+    files_data = []
+
+    # SKILL.md
+    skill_md = skill_path / 'SKILL.md'
+    if skill_md.exists():
+        data = scan_file_patterns(skill_md, 'SKILL.md')
+        content = skill_md.read_text(encoding='utf-8')
+        data['overview_lines'] = extract_overview_size(content)
+        data['is_skill_md'] = True
+        files_data.append(data)
+
+    # Prompt files at skill root
+    skip_files = {'SKILL.md'}
+
+    for f in sorted(skill_path.iterdir()):
+        if f.is_file() and f.suffix == '.md' and f.name not in skip_files and f.name != 'SKILL.md':
+            data = scan_file_patterns(f, f.name)
+            data['is_skill_md'] = False
+
+            # Parse prompt frontmatter
+            pfm = parse_prompt_frontmatter(f)
+            data['prompt_frontmatter'] = pfm
+
+            files_data.append(data)
+
+    # Resources (just sizes, for progressive disclosure assessment)
+    resources_dir = skill_path / 'resources'
+    resource_sizes = {}
+    if resources_dir.exists():
+        for f in sorted(resources_dir.iterdir()):
+            if f.is_file() and f.suffix in ('.md', '.json', '.yaml', '.yml'):
+                content = f.read_text(encoding='utf-8')
+                resource_sizes[f.name] = {
+                    'lines': len(content.split('\n')),
+                    'tokens': len(content) // 4,
+                }
+
+    # Aggregate stats
+    total_waste = sum(len(f['waste_patterns']) for f in files_data)
+    total_backrefs = sum(len(f['back_references']) for f in files_data)
+    total_suggestive = sum(len(f.get('suggestive_loading', [])) for f in files_data)
+    total_tokens = sum(f['token_estimate'] for f in files_data)
+    total_walls = sum(len(f.get('wall_of_text', [])) for f in files_data)
+    prompts_with_config = sum(1 for f in files_data if not f.get('is_skill_md') and f['has_config_header'])
+    prompts_with_progression = sum(1 for f in files_data if not f.get('is_skill_md') and f['has_progression'])
+    total_prompts = sum(1 for f in files_data if not f.get('is_skill_md'))
+
+    skill_md_data = next((f for f in files_data if f.get('is_skill_md')), None)
+
+    return {
+        'scanner': 'prompt-craft-prepass',
+        'script': 'prepass-prompt-metrics.py',
+        'version': '1.0.0',
+        'skill_path': str(skill_path),
+        'timestamp': datetime.now(timezone.utc).isoformat(),
+        'status': 'info',
+        'skill_md_summary': {
+            'line_count': skill_md_data['line_count'] if skill_md_data else 0,
+            'token_estimate': skill_md_data['token_estimate'] if skill_md_data else 0,
+            'overview_lines': skill_md_data.get('overview_lines', 0) if skill_md_data else 0,
+            'table_count': skill_md_data['table_count'] if skill_md_data else 0,
+            'table_lines': skill_md_data['table_lines'] if skill_md_data else 0,
+            'fenced_block_count': skill_md_data['fenced_block_count'] if skill_md_data else 0,
+            'fenced_block_lines': skill_md_data['fenced_block_lines'] if skill_md_data else 0,
+            'section_count': len(skill_md_data['sections']) if skill_md_data else 0,
+        },
+        'prompt_health': {
+            'total_prompts': total_prompts,
+            'prompts_with_config_header': prompts_with_config,
+            'prompts_with_progression': prompts_with_progression,
+        },
+        'aggregate': {
+            'total_files_scanned': len(files_data),
+            'total_token_estimate': total_tokens,
+            'total_waste_patterns': total_waste,
+            'total_back_references': total_backrefs,
+            'total_suggestive_loading': total_suggestive,
+            'total_wall_of_text': total_walls,
+        },
+        'resource_sizes': resource_sizes,
+        'files': files_data,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Extract prompt craft metrics for LLM scanner pre-pass (agent builder)',
+    )
+    parser.add_argument(
+        'skill_path',
+        type=Path,
+        help='Path to the skill directory to scan',
+    )
+    parser.add_argument(
+        '--output', '-o',
+        type=Path,
+        help='Write JSON output to file instead of stdout',
+    )
+    args = parser.parse_args()
+
+    if not args.skill_path.is_dir():
+        print(f"Error: {args.skill_path} is not a directory", file=sys.stderr)
+        return 2
+
+    result = scan_prompt_metrics(args.skill_path)
+    output = json.dumps(result, indent=2)
+
+    if args.output:
+        args.output.parent.mkdir(parents=True, exist_ok=True)
+        args.output.write_text(output)
+        print(f"Results written to {args.output}", file=sys.stderr)
+    else:
+        print(output)
+
+    return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())