This skill enables automatic extraction of valuable patterns, solutions, and best practices from construction automation sessions to build institutional knowledge.
Activate this skill:
class ConstructionSessionAnalyzer:
"""Extract learnings from automation sessions"""
# Categories of learnable patterns
PATTERN_CATEGORIES = [
'data_processing', # Data transformation patterns
'estimation', # Cost estimation techniques
'scheduling', # Schedule optimization patterns
'integration', # API/system integration patterns
'document_processing', # Document handling patterns
'quality_assurance', # Validation and QA patterns
'error_handling', # Error resolution patterns
'optimization' # Performance optimization patterns
]
def analyze_session(self, session_log: list) -> dict:
"""Extract patterns from session history"""
patterns = {
'successful_solutions': [],
'error_resolutions': [],
'optimization_discoveries': [],
'integration_patterns': [],
'reusable_code': [],
'decision_rationales': []
}
for entry in session_log:
if self._is_solution(entry):
patterns['successful_solutions'].append(
self._extract_solution_pattern(entry)
)
if self._is_error_resolution(entry):
patterns['error_resolutions'].append(
self._extract_error_pattern(entry)
)
if self._is_optimization(entry):
patterns['optimization_discoveries'].append(
self._extract_optimization(entry)
)
return patterns
# Example learned pattern
pattern:
name: "electrical_cost_adjustment_pattern"
category: "estimation"
context: "When estimating electrical work for high-rise buildings"
problem: "Standard rates don't account for vertical transportation costs"
solution: |
Apply height factor multiplier:
- Floors 1-5: 1.0x base rate
- Floors 6-15: 1.15x base rate
- Floors 16-30: 1.25x base rate
- Floors 30+: 1.35x base rate
confidence: 0.85
source_sessions: ["session_2026_01_15", "session_2026_01_20"]
validations: 3
pattern:
name: "revit_level_extraction"
category: "data_processing"
context: "Extracting elements by level from Revit exports"
problem: "Elements sometimes missing level association"
solution: |
1. First check 'Level' parameter
2. If missing, check 'Reference Level' parameter
3. If still missing, derive from bounding box Z coordinate
4. Map Z ranges to known level elevations
code_snippet: |
def get_element_level(element: dict, levels: list) -> str:
# Direct level parameter
if level := element.get('Level'):
return level
# Reference level fallback
if ref_level := element.get('Reference Level'):
return ref_level
# Derive from geometry
z_coord = element['BoundingBox']['Min']['Z']
return find_nearest_level(z_coord, levels)
confidence: 0.92
pattern:
name: "procore_rate_limit_handling"
category: "integration"
context: "Syncing data with Procore API"
problem: "API returns 429 Too Many Requests during bulk operations"
solution: |
Implement exponential backoff with jitter:
1. Initial delay: 1 second
2. Multiply by 2 on each retry
3. Add random jitter (0-500ms)
4. Max retries: 5
5. Max delay: 32 seconds
code_snippet: |
async def procore_request_with_retry(url, data):
delay = 1
for attempt in range(5):
try:
response = await procore_api.post(url, data)
return response
except RateLimitError:
jitter = random.uniform(0, 0.5)
await asyncio.sleep(delay + jitter)
delay *= 2
raise MaxRetriesExceeded()
confidence: 0.95
pattern:
name: "cwicr_no_match_resolution"
category: "error_handling"
context: "CWICR semantic search returns no relevant matches"
problem: "Query too specific or uses non-standard terminology"
solution: |
Resolution steps:
1. Simplify query to core concepts
2. Remove brand names and specifications
3. Try alternative terminology (US vs UK terms)
4. Expand search to parent category
5. If still no match, flag for manual mapping
examples:
- original: "Kohler K-4519 wall-mounted water closet"
simplified: "wall mounted toilet"
- original: "Lutron Caseta wireless dimmer switch"
simplified: "dimmer switch"
confidence: 0.88
class ConstructionLearningPipeline:
"""Continuous learning pipeline for construction automation"""
def __init__(self, knowledge_base_path: str):
self.kb_path = knowledge_base_path
self.patterns = self._load_patterns()
def learn_from_session(self, session: dict) -> list:
"""Extract and store learnings from session"""
# Analyze session
analyzer = ConstructionSessionAnalyzer()
new_patterns = analyzer.analyze_session(session['log'])
# Validate patterns
validated = []
for pattern in new_patterns['successful_solutions']:
if self._validate_pattern(pattern):
# Check if similar pattern exists
existing = self._find_similar_pattern(pattern)
if existing:
# Reinforce existing pattern
self._reinforce_pattern(existing, pattern)
else:
# Add new pattern
self._add_pattern(pattern)
validated.append(pattern)
# Persist to knowledge base
self._save_patterns()
return validated
def apply_learnings(self, context: dict) -> list:
"""Retrieve relevant patterns for current context"""
relevant_patterns = []
for pattern in self.patterns:
similarity = self._calculate_similarity(pattern['context'], context)
if similarity > 0.7:
relevant_patterns.append({
'pattern': pattern,
'relevance': similarity
})
return sorted(relevant_patterns, key=lambda x: x['relevance'], reverse=True)
def _validate_pattern(self, pattern: dict) -> bool:
"""Validate pattern before adding to knowledge base"""
# Check minimum confidence
if pattern.get('confidence', 0) < 0.6:
return False
# Check for code quality (if code snippet)
if code := pattern.get('code_snippet'):
if not self._is_valid_code(code):
return False
# Check for completeness
required_fields = ['name', 'category', 'context', 'solution']
if not all(f in pattern for f in required_fields):
return False
return True
knowledge_base/
├── patterns/
│ ├── estimation/
│ │ ├── height_factors.yaml
│ │ ├── material_adjustments.yaml
│ │ └── labor_productivity.yaml
│ ├── data_processing/
│ │ ├── revit_extraction.yaml
│ │ ├── ifc_parsing.yaml
│ │ └── excel_transformations.yaml
│ ├── integration/
│ │ ├── procore_patterns.yaml
│ │ ├── plangrid_patterns.yaml
│ │ └── webhook_handlers.yaml
│ └── error_handling/
│ ├── cwicr_resolutions.yaml
│ ├── api_errors.yaml
│ └── data_validation.yaml
├── code_snippets/
│ ├── python/
│ ├── javascript/
│ └── sql/
├── decision_trees/
│ ├── estimate_type_selection.yaml
│ ├── schedule_method_selection.yaml
│ └── integration_approach.yaml
└── metrics/
├── pattern_usage.json
└── success_rates.json
At the end of each construction automation session:
## Session Learning Review
### What Worked Well
- [Successful approaches discovered]
- [Efficient patterns used]
- [Integrations that worked smoothly]
### Challenges Overcome
- [Errors encountered and how resolved]
- [Workarounds developed]
- [Edge cases handled]
### New Patterns Discovered
- [Novel approaches to problems]
- [Optimization techniques found]
- [Reusable code created]
### Knowledge to Preserve
- [Key learnings to remember]
- [Context-specific solutions]
- [Client/project-specific adaptations]
### Recommendations for Future
- [Improvements to suggest]
- [Patterns to apply elsewhere]
- [Automation opportunities identified]
When starting new construction tasks:
def suggest_approaches(task_context: dict) -> list:
"""Suggest learned approaches for new tasks"""
pipeline = ConstructionLearningPipeline('knowledge_base/')
relevant = pipeline.apply_learnings(task_context)
suggestions = []
for item in relevant[:5]: # Top 5 suggestions
pattern = item['pattern']
suggestions.append({
'name': pattern['name'],
'relevance': f"{item['relevance']*100:.0f}%",
'summary': pattern['solution'][:200],
'confidence': pattern['confidence'],
'previous_uses': pattern.get('usage_count', 0)
})
return suggestions
This skill works with:
# Extract learnings from current session
/learn
# View patterns for current context
/suggest-patterns
# Add manual pattern
/add-pattern --category estimation --name "my_pattern"
# Export knowledge base
/export-kb --format yaml
Every session is an opportunity to learn. Capture knowledge to compound expertise over time.
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