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This commit is contained in:
Pablo Estevez
2026-01-17 17:29:21 +00:00
parent c89f059712
commit 5ed767ff9a
144 changed files with 14142 additions and 16488 deletions
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77
src/skill_seekers/cli/ai_enhancer.py
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@@ -17,9 +17,8 @@ Credits:
- Graceful degradation if API unavailable
"""
import os
import logging
from typing import List, Dict, Optional, Any
import os
from dataclasses import dataclass
logger = logging.getLogger(__name__)
@@ -28,18 +27,19 @@ logger = logging.getLogger(__name__)
@dataclass
class AIAnalysis:
"""AI analysis result for patterns or examples"""
explanation: str
issues: List[str]
recommendations: List[str]
related_items: List[str] # Related patterns or examples
best_practices: List[str]
issues: list[str]
recommendations: list[str]
related_items: list[str] # Related patterns or examples
best_practices: list[str]
confidence_boost: float # -0.2 to +0.2 adjustment to confidence
class AIEnhancer:
"""Base class for AI enhancement"""
def __init__(self, api_key: Optional[str] = None, enabled: bool = True, mode: str = "auto"):
def __init__(self, api_key: str | None = None, enabled: bool = True, mode: str = "auto"):
"""
Initialize AI enhancer.
@@ -53,7 +53,7 @@ class AIEnhancer:
"""
self.enabled = enabled
self.mode = mode
self.api_key = api_key or os.environ.get('ANTHROPIC_API_KEY')
self.api_key = api_key or os.environ.get("ANTHROPIC_API_KEY")
self.client = None
# Determine actual mode
@@ -72,6 +72,7 @@ class AIEnhancer:
if self.mode == "api" and self.enabled:
try:
import anthropic
self.client = anthropic.Anthropic(api_key=self.api_key)
logger.info("✅ AI enhancement enabled (using Claude API)")
except ImportError:
@@ -88,16 +89,14 @@ class AIEnhancer:
logger.info(" Use API mode (set ANTHROPIC_API_KEY) or 'skill-seekers enhance' for SKILL.md")
self.enabled = False
def _call_claude(self, prompt: str, max_tokens: int = 1000) -> Optional[str]:
def _call_claude(self, prompt: str, max_tokens: int = 1000) -> str | None:
"""Call Claude API with error handling"""
if not self.client:
return None
try:
response = self.client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=max_tokens,
messages=[{"role": "user", "content": prompt}]
model="claude-sonnet-4-20250514", max_tokens=max_tokens, messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
except Exception as e:
@@ -108,7 +107,7 @@ class AIEnhancer:
class PatternEnhancer(AIEnhancer):
"""Enhance design pattern detection with AI analysis"""
def enhance_patterns(self, patterns: List[Dict]) -> List[Dict]:
def enhance_patterns(self, patterns: list[dict]) -> list[dict]:
"""
Enhance detected patterns with AI analysis.
@@ -128,19 +127,19 @@ class PatternEnhancer(AIEnhancer):
enhanced = []
for i in range(0, len(patterns), batch_size):
batch = patterns[i:i+batch_size]
batch = patterns[i : i + batch_size]
batch_results = self._enhance_pattern_batch(batch)
enhanced.extend(batch_results)
logger.info(f"✅ Enhanced {len(enhanced)} patterns")
return enhanced
def _enhance_pattern_batch(self, patterns: List[Dict]) -> List[Dict]:
def _enhance_pattern_batch(self, patterns: list[dict]) -> list[dict]:
"""Enhance a batch of patterns"""
# Prepare prompt
pattern_descriptions = []
for idx, p in enumerate(patterns):
desc = f"{idx+1}. {p['pattern_type']} in {p.get('class_name', 'unknown')}"
desc = f"{idx + 1}. {p['pattern_type']} in {p.get('class_name', 'unknown')}"
desc += f"\n Evidence: {', '.join(p.get('evidence', []))}"
pattern_descriptions.append(desc)
@@ -166,24 +165,25 @@ Format as JSON array matching input order. Be concise and actionable.
try:
import json
analyses = json.loads(response)
# Merge AI analysis into patterns
for idx, pattern in enumerate(patterns):
if idx < len(analyses):
analysis = analyses[idx]
pattern['ai_analysis'] = {
'explanation': analysis.get('explanation', ''),
'issues': analysis.get('issues', []),
'recommendations': analysis.get('recommendations', []),
'related_patterns': analysis.get('related_patterns', []),
'confidence_boost': analysis.get('confidence_boost', 0.0)
pattern["ai_analysis"] = {
"explanation": analysis.get("explanation", ""),
"issues": analysis.get("issues", []),
"recommendations": analysis.get("recommendations", []),
"related_patterns": analysis.get("related_patterns", []),
"confidence_boost": analysis.get("confidence_boost", 0.0),
}
# Adjust confidence
boost = analysis.get('confidence_boost', 0.0)
boost = analysis.get("confidence_boost", 0.0)
if -0.2 <= boost <= 0.2:
pattern['confidence'] = min(1.0, max(0.0, pattern['confidence'] + boost))
pattern["confidence"] = min(1.0, max(0.0, pattern["confidence"] + boost))
return patterns
@@ -198,7 +198,7 @@ Format as JSON array matching input order. Be concise and actionable.
class TestExampleEnhancer(AIEnhancer):
"""Enhance test examples with AI analysis"""
def enhance_examples(self, examples: List[Dict]) -> List[Dict]:
def enhance_examples(self, examples: list[dict]) -> list[dict]:
"""
Enhance test examples with AI context and explanations.
@@ -218,21 +218,21 @@ class TestExampleEnhancer(AIEnhancer):
enhanced = []
for i in range(0, len(examples), batch_size):
batch = examples[i:i+batch_size]
batch = examples[i : i + batch_size]
batch_results = self._enhance_example_batch(batch)
enhanced.extend(batch_results)
logger.info(f"✅ Enhanced {len(enhanced)} examples")
return enhanced
def _enhance_example_batch(self, examples: List[Dict]) -> List[Dict]:
def _enhance_example_batch(self, examples: list[dict]) -> list[dict]:
"""Enhance a batch of examples"""
# Prepare prompt
example_descriptions = []
for idx, ex in enumerate(examples):
desc = f"{idx+1}. {ex.get('category', 'unknown')} - {ex.get('test_name', 'unknown')}"
desc = f"{idx + 1}. {ex.get('category', 'unknown')} - {ex.get('test_name', 'unknown')}"
desc += f"\n Code: {ex.get('code', '')[:100]}..."
if ex.get('expected_behavior'):
if ex.get("expected_behavior"):
desc += f"\n Expected: {ex['expected_behavior']}"
example_descriptions.append(desc)
@@ -257,18 +257,19 @@ Format as JSON array matching input order. Focus on educational value.
try:
import json
analyses = json.loads(response)
# Merge AI analysis into examples
for idx, example in enumerate(examples):
if idx < len(analyses):
analysis = analyses[idx]
example['ai_analysis'] = {
'explanation': analysis.get('explanation', ''),
'best_practices': analysis.get('best_practices', []),
'common_mistakes': analysis.get('common_mistakes', []),
'related_examples': analysis.get('related_examples', []),
'tutorial_group': analysis.get('tutorial_group', '')
example["ai_analysis"] = {
"explanation": analysis.get("explanation", ""),
"best_practices": analysis.get("best_practices", []),
"common_mistakes": analysis.get("common_mistakes", []),
"related_examples": analysis.get("related_examples", []),
"tutorial_group": analysis.get("tutorial_group", ""),
}
return examples
@@ -280,7 +281,7 @@ Format as JSON array matching input order. Focus on educational value.
logger.warning(f"⚠️ Error processing AI analysis: {e}")
return examples
def generate_tutorials(self, examples: List[Dict]) -> Dict[str, List[Dict]]:
def generate_tutorials(self, examples: list[dict]) -> dict[str, list[dict]]:
"""
Group enhanced examples into tutorial sections.
@@ -293,8 +294,8 @@ Format as JSON array matching input order. Focus on educational value.
tutorials = {}
for example in examples:
ai_analysis = example.get('ai_analysis', {})
group = ai_analysis.get('tutorial_group', 'Miscellaneous')
ai_analysis = example.get("ai_analysis", {})
group = ai_analysis.get("tutorial_group", "Miscellaneous")
if group not in tutorials:
tutorials[group] = []