Initial commit: The Ultimate Antigravity Skills Collection (58 Skills)
This commit is contained in:
sck_0
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#!/usr/bin/env python3
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"""
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Customer Interview Analyzer
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Extracts insights, patterns, and opportunities from user interviews
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"""
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import re
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from typing import Dict, List, Tuple, Set
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from collections import Counter, defaultdict
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import json
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class InterviewAnalyzer:
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"""Analyze customer interviews for insights and patterns"""
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def __init__(self):
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# Pain point indicators
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self.pain_indicators = [
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'frustrat', 'annoy', 'difficult', 'hard', 'confus', 'slow',
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'problem', 'issue', 'struggle', 'challeng', 'pain', 'waste',
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'manual', 'repetitive', 'tedious', 'boring', 'time-consuming',
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'complicated', 'complex', 'unclear', 'wish', 'need', 'want'
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]
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# Positive indicators
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self.delight_indicators = [
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'love', 'great', 'awesome', 'amazing', 'perfect', 'easy',
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'simple', 'quick', 'fast', 'helpful', 'useful', 'valuable',
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'save', 'efficient', 'convenient', 'intuitive', 'clear'
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]
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# Feature request indicators
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self.request_indicators = [
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'would be nice', 'wish', 'hope', 'want', 'need', 'should',
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'could', 'would love', 'if only', 'it would help', 'suggest',
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'recommend', 'idea', 'what if', 'have you considered'
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]
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# Jobs to be done patterns
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self.jtbd_patterns = [
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r'when i\s+(.+?),\s+i want to\s+(.+?)\s+so that\s+(.+)',
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r'i need to\s+(.+?)\s+because\s+(.+)',
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r'my goal is to\s+(.+)',
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r'i\'m trying to\s+(.+)',
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r'i use \w+ to\s+(.+)',
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r'helps me\s+(.+)',
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]
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def analyze_interview(self, text: str) -> Dict:
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"""Analyze a single interview transcript"""
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text_lower = text.lower()
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sentences = self._split_sentences(text)
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analysis = {
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'pain_points': self._extract_pain_points(sentences),
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'delights': self._extract_delights(sentences),
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'feature_requests': self._extract_requests(sentences),
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'jobs_to_be_done': self._extract_jtbd(text_lower),
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'sentiment_score': self._calculate_sentiment(text_lower),
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'key_themes': self._extract_themes(text_lower),
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'quotes': self._extract_key_quotes(sentences),
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'metrics_mentioned': self._extract_metrics(text),
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'competitors_mentioned': self._extract_competitors(text)
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}
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return analysis
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def _split_sentences(self, text: str) -> List[str]:
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"""Split text into sentences"""
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# Simple sentence splitting
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sentences = re.split(r'[.!?]+', text)
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return [s.strip() for s in sentences if s.strip()]
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def _extract_pain_points(self, sentences: List[str]) -> List[Dict]:
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"""Extract pain points from sentences"""
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pain_points = []
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for sentence in sentences:
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sentence_lower = sentence.lower()
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for indicator in self.pain_indicators:
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if indicator in sentence_lower:
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# Extract context around the pain point
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pain_points.append({
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'quote': sentence,
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'indicator': indicator,
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'severity': self._assess_severity(sentence_lower)
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})
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break
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return pain_points[:10] # Return top 10
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def _extract_delights(self, sentences: List[str]) -> List[Dict]:
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"""Extract positive feedback"""
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delights = []
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for sentence in sentences:
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sentence_lower = sentence.lower()
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for indicator in self.delight_indicators:
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if indicator in sentence_lower:
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delights.append({
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'quote': sentence,
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'indicator': indicator,
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'strength': self._assess_strength(sentence_lower)
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})
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break
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return delights[:10]
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def _extract_requests(self, sentences: List[str]) -> List[Dict]:
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"""Extract feature requests and suggestions"""
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requests = []
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for sentence in sentences:
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sentence_lower = sentence.lower()
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for indicator in self.request_indicators:
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if indicator in sentence_lower:
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requests.append({
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'quote': sentence,
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'type': self._classify_request(sentence_lower),
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'priority': self._assess_request_priority(sentence_lower)
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})
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break
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return requests[:10]
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def _extract_jtbd(self, text: str) -> List[Dict]:
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"""Extract Jobs to Be Done patterns"""
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jobs = []
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for pattern in self.jtbd_patterns:
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matches = re.findall(pattern, text, re.IGNORECASE)
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for match in matches:
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if isinstance(match, tuple):
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job = ' → '.join(match)
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else:
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job = match
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jobs.append({
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'job': job,
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'pattern': pattern.pattern if hasattr(pattern, 'pattern') else pattern
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})
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return jobs[:5]
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def _calculate_sentiment(self, text: str) -> Dict:
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"""Calculate overall sentiment of the interview"""
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positive_count = sum(1 for ind in self.delight_indicators if ind in text)
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negative_count = sum(1 for ind in self.pain_indicators if ind in text)
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total = positive_count + negative_count
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if total == 0:
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sentiment_score = 0
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else:
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sentiment_score = (positive_count - negative_count) / total
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if sentiment_score > 0.3:
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sentiment_label = 'positive'
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elif sentiment_score < -0.3:
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sentiment_label = 'negative'
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else:
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sentiment_label = 'neutral'
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return {
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'score': round(sentiment_score, 2),
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'label': sentiment_label,
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'positive_signals': positive_count,
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'negative_signals': negative_count
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}
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def _extract_themes(self, text: str) -> List[str]:
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"""Extract key themes using word frequency"""
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# Remove common words
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stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at',
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'to', 'for', 'of', 'with', 'by', 'from', 'as', 'is',
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'was', 'are', 'were', 'been', 'be', 'have', 'has',
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'had', 'do', 'does', 'did', 'will', 'would', 'could',
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'should', 'may', 'might', 'must', 'can', 'shall',
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'it', 'i', 'you', 'we', 'they', 'them', 'their'}
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# Extract meaningful words
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words = re.findall(r'\b[a-z]{4,}\b', text)
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meaningful_words = [w for w in words if w not in stop_words]
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# Count frequency
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word_freq = Counter(meaningful_words)
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# Extract themes (top frequent meaningful words)
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themes = [word for word, count in word_freq.most_common(10) if count >= 3]
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return themes
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def _extract_key_quotes(self, sentences: List[str]) -> List[str]:
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"""Extract the most insightful quotes"""
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scored_sentences = []
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for sentence in sentences:
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if len(sentence) < 20 or len(sentence) > 200:
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continue
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score = 0
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sentence_lower = sentence.lower()
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# Score based on insight indicators
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if any(ind in sentence_lower for ind in self.pain_indicators):
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score += 2
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if any(ind in sentence_lower for ind in self.request_indicators):
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score += 2
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if 'because' in sentence_lower:
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score += 1
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if 'but' in sentence_lower:
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score += 1
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if '?' in sentence:
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score += 1
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if score > 0:
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scored_sentences.append((score, sentence))
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# Sort by score and return top quotes
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scored_sentences.sort(reverse=True)
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return [s[1] for s in scored_sentences[:5]]
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def _extract_metrics(self, text: str) -> List[str]:
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"""Extract any metrics or numbers mentioned"""
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metrics = []
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# Find percentages
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percentages = re.findall(r'\d+%', text)
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metrics.extend(percentages)
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# Find time metrics
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time_metrics = re.findall(r'\d+\s*(?:hours?|minutes?|days?|weeks?|months?)', text, re.IGNORECASE)
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metrics.extend(time_metrics)
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# Find money metrics
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money_metrics = re.findall(r'\$[\d,]+', text)
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metrics.extend(money_metrics)
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# Find general numbers with context
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number_contexts = re.findall(r'(\d+)\s+(\w+)', text)
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for num, context in number_contexts:
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if context.lower() not in ['the', 'a', 'an', 'and', 'or', 'of']:
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metrics.append(f"{num} {context}")
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return list(set(metrics))[:10]
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def _extract_competitors(self, text: str) -> List[str]:
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"""Extract competitor mentions"""
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# Common competitor indicators
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competitor_patterns = [
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r'(?:use|used|using|tried|trying|switch from|switched from|instead of)\s+(\w+)',
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r'(\w+)\s+(?:is better|works better|is easier)',
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r'compared to\s+(\w+)',
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r'like\s+(\w+)',
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r'similar to\s+(\w+)',
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]
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competitors = set()
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for pattern in competitor_patterns:
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matches = re.findall(pattern, text, re.IGNORECASE)
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competitors.update(matches)
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# Filter out common words
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common_words = {'this', 'that', 'it', 'them', 'other', 'another', 'something'}
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competitors = [c for c in competitors if c.lower() not in common_words and len(c) > 2]
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return list(competitors)[:5]
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def _assess_severity(self, text: str) -> str:
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"""Assess severity of pain point"""
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if any(word in text for word in ['very', 'extremely', 'really', 'totally', 'completely']):
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return 'high'
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elif any(word in text for word in ['somewhat', 'bit', 'little', 'slightly']):
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return 'low'
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return 'medium'
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def _assess_strength(self, text: str) -> str:
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"""Assess strength of positive feedback"""
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if any(word in text for word in ['absolutely', 'definitely', 'really', 'very']):
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return 'strong'
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return 'moderate'
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def _classify_request(self, text: str) -> str:
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"""Classify the type of request"""
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if any(word in text for word in ['ui', 'design', 'look', 'color', 'layout']):
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return 'ui_improvement'
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elif any(word in text for word in ['feature', 'add', 'new', 'build']):
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return 'new_feature'
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elif any(word in text for word in ['fix', 'bug', 'broken', 'work']):
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return 'bug_fix'
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elif any(word in text for word in ['faster', 'slow', 'performance', 'speed']):
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return 'performance'
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return 'general'
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def _assess_request_priority(self, text: str) -> str:
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"""Assess priority of request"""
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if any(word in text for word in ['critical', 'urgent', 'asap', 'immediately', 'blocking']):
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return 'critical'
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elif any(word in text for word in ['need', 'important', 'should', 'must']):
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return 'high'
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elif any(word in text for word in ['nice', 'would', 'could', 'maybe']):
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return 'low'
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return 'medium'
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def aggregate_interviews(interviews: List[Dict]) -> Dict:
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"""Aggregate insights from multiple interviews"""
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aggregated = {
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'total_interviews': len(interviews),
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'common_pain_points': defaultdict(list),
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'common_requests': defaultdict(list),
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'jobs_to_be_done': [],
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'overall_sentiment': {
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'positive': 0,
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'negative': 0,
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'neutral': 0
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},
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'top_themes': Counter(),
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'metrics_summary': set(),
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'competitors_mentioned': Counter()
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}
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for interview in interviews:
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# Aggregate pain points
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for pain in interview.get('pain_points', []):
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indicator = pain.get('indicator', 'unknown')
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aggregated['common_pain_points'][indicator].append(pain['quote'])
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# Aggregate requests
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for request in interview.get('feature_requests', []):
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req_type = request.get('type', 'general')
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aggregated['common_requests'][req_type].append(request['quote'])
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# Aggregate JTBD
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aggregated['jobs_to_be_done'].extend(interview.get('jobs_to_be_done', []))
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# Aggregate sentiment
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sentiment = interview.get('sentiment_score', {}).get('label', 'neutral')
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aggregated['overall_sentiment'][sentiment] += 1
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# Aggregate themes
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for theme in interview.get('key_themes', []):
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aggregated['top_themes'][theme] += 1
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# Aggregate metrics
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aggregated['metrics_summary'].update(interview.get('metrics_mentioned', []))
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# Aggregate competitors
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for competitor in interview.get('competitors_mentioned', []):
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aggregated['competitors_mentioned'][competitor] += 1
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# Process aggregated data
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aggregated['common_pain_points'] = dict(aggregated['common_pain_points'])
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aggregated['common_requests'] = dict(aggregated['common_requests'])
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aggregated['top_themes'] = dict(aggregated['top_themes'].most_common(10))
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aggregated['metrics_summary'] = list(aggregated['metrics_summary'])
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aggregated['competitors_mentioned'] = dict(aggregated['competitors_mentioned'])
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return aggregated
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def format_single_interview(analysis: Dict) -> str:
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"""Format single interview analysis"""
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output = ["=" * 60]
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output.append("CUSTOMER INTERVIEW ANALYSIS")
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output.append("=" * 60)
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# Sentiment
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sentiment = analysis['sentiment_score']
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output.append(f"\n📊 Overall Sentiment: {sentiment['label'].upper()}")
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output.append(f" Score: {sentiment['score']}")
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output.append(f" Positive signals: {sentiment['positive_signals']}")
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output.append(f" Negative signals: {sentiment['negative_signals']}")
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# Pain Points
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if analysis['pain_points']:
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output.append("\n🔥 Pain Points Identified:")
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for i, pain in enumerate(analysis['pain_points'][:5], 1):
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output.append(f"\n{i}. [{pain['severity'].upper()}] {pain['quote'][:100]}...")
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# Feature Requests
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if analysis['feature_requests']:
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output.append("\n💡 Feature Requests:")
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for i, req in enumerate(analysis['feature_requests'][:5], 1):
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output.append(f"\n{i}. [{req['type']}] Priority: {req['priority']}")
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output.append(f" \"{req['quote'][:100]}...\"")
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# Jobs to Be Done
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if analysis['jobs_to_be_done']:
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output.append("\n🎯 Jobs to Be Done:")
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for i, job in enumerate(analysis['jobs_to_be_done'], 1):
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output.append(f"{i}. {job['job']}")
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# Key Themes
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if analysis['key_themes']:
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output.append("\n🏷️ Key Themes:")
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output.append(", ".join(analysis['key_themes']))
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# Key Quotes
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if analysis['quotes']:
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output.append("\n💬 Key Quotes:")
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for i, quote in enumerate(analysis['quotes'][:3], 1):
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output.append(f'{i}. "{quote}"')
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# Metrics
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if analysis['metrics_mentioned']:
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output.append("\n📈 Metrics Mentioned:")
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output.append(", ".join(analysis['metrics_mentioned']))
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# Competitors
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if analysis['competitors_mentioned']:
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output.append("\n🏢 Competitors Mentioned:")
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output.append(", ".join(analysis['competitors_mentioned']))
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return "\n".join(output)
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def main():
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import sys
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if len(sys.argv) < 2:
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print("Usage: python customer_interview_analyzer.py <interview_file.txt>")
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print("\nThis tool analyzes customer interview transcripts to extract:")
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print(" - Pain points and frustrations")
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print(" - Feature requests and suggestions")
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print(" - Jobs to be done")
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print(" - Sentiment analysis")
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print(" - Key themes and quotes")
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sys.exit(1)
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# Read interview transcript
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with open(sys.argv[1], 'r') as f:
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interview_text = f.read()
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# Analyze
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analyzer = InterviewAnalyzer()
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analysis = analyzer.analyze_interview(interview_text)
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# Output
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if len(sys.argv) > 2 and sys.argv[2] == 'json':
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print(json.dumps(analysis, indent=2))
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else:
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print(format_single_interview(analysis))
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if __name__ == "__main__":
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main()
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296
skills/product-manager-toolkit/scripts/rice_prioritizer.py
Normal file
296
skills/product-manager-toolkit/scripts/rice_prioritizer.py
Normal file
@@ -0,0 +1,296 @@
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#!/usr/bin/env python3
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"""
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RICE Prioritization Framework
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Calculates RICE scores for feature prioritization
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RICE = (Reach x Impact x Confidence) / Effort
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"""
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import json
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import csv
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from typing import List, Dict, Tuple
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import argparse
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class RICECalculator:
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"""Calculate RICE scores for feature prioritization"""
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def __init__(self):
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self.impact_map = {
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'massive': 3.0,
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'high': 2.0,
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'medium': 1.0,
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'low': 0.5,
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'minimal': 0.25
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}
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self.confidence_map = {
|
||||
'high': 100,
|
||||
'medium': 80,
|
||||
'low': 50
|
||||
}
|
||||
|
||||
self.effort_map = {
|
||||
'xl': 13,
|
||||
'l': 8,
|
||||
'm': 5,
|
||||
's': 3,
|
||||
'xs': 1
|
||||
}
|
||||
|
||||
def calculate_rice(self, reach: int, impact: str, confidence: str, effort: str) -> float:
|
||||
"""
|
||||
Calculate RICE score
|
||||
|
||||
Args:
|
||||
reach: Number of users/customers affected per quarter
|
||||
impact: massive/high/medium/low/minimal
|
||||
confidence: high/medium/low (percentage)
|
||||
effort: xl/l/m/s/xs (person-months)
|
||||
"""
|
||||
impact_score = self.impact_map.get(impact.lower(), 1.0)
|
||||
confidence_score = self.confidence_map.get(confidence.lower(), 50) / 100
|
||||
effort_score = self.effort_map.get(effort.lower(), 5)
|
||||
|
||||
if effort_score == 0:
|
||||
return 0
|
||||
|
||||
rice_score = (reach * impact_score * confidence_score) / effort_score
|
||||
return round(rice_score, 2)
|
||||
|
||||
def prioritize_features(self, features: List[Dict]) -> List[Dict]:
|
||||
"""
|
||||
Calculate RICE scores and rank features
|
||||
|
||||
Args:
|
||||
features: List of feature dictionaries with RICE components
|
||||
"""
|
||||
for feature in features:
|
||||
feature['rice_score'] = self.calculate_rice(
|
||||
feature.get('reach', 0),
|
||||
feature.get('impact', 'medium'),
|
||||
feature.get('confidence', 'medium'),
|
||||
feature.get('effort', 'm')
|
||||
)
|
||||
|
||||
# Sort by RICE score descending
|
||||
return sorted(features, key=lambda x: x['rice_score'], reverse=True)
|
||||
|
||||
def analyze_portfolio(self, features: List[Dict]) -> Dict:
|
||||
"""
|
||||
Analyze the feature portfolio for balance and insights
|
||||
"""
|
||||
if not features:
|
||||
return {}
|
||||
|
||||
total_effort = sum(
|
||||
self.effort_map.get(f.get('effort', 'm').lower(), 5)
|
||||
for f in features
|
||||
)
|
||||
|
||||
total_reach = sum(f.get('reach', 0) for f in features)
|
||||
|
||||
effort_distribution = {}
|
||||
impact_distribution = {}
|
||||
|
||||
for feature in features:
|
||||
effort = feature.get('effort', 'm').lower()
|
||||
impact = feature.get('impact', 'medium').lower()
|
||||
|
||||
effort_distribution[effort] = effort_distribution.get(effort, 0) + 1
|
||||
impact_distribution[impact] = impact_distribution.get(impact, 0) + 1
|
||||
|
||||
# Calculate quick wins (high impact, low effort)
|
||||
quick_wins = [
|
||||
f for f in features
|
||||
if f.get('impact', '').lower() in ['massive', 'high']
|
||||
and f.get('effort', '').lower() in ['xs', 's']
|
||||
]
|
||||
|
||||
# Calculate big bets (high impact, high effort)
|
||||
big_bets = [
|
||||
f for f in features
|
||||
if f.get('impact', '').lower() in ['massive', 'high']
|
||||
and f.get('effort', '').lower() in ['l', 'xl']
|
||||
]
|
||||
|
||||
return {
|
||||
'total_features': len(features),
|
||||
'total_effort_months': total_effort,
|
||||
'total_reach': total_reach,
|
||||
'average_rice': round(sum(f['rice_score'] for f in features) / len(features), 2),
|
||||
'effort_distribution': effort_distribution,
|
||||
'impact_distribution': impact_distribution,
|
||||
'quick_wins': len(quick_wins),
|
||||
'big_bets': len(big_bets),
|
||||
'quick_wins_list': quick_wins[:3], # Top 3 quick wins
|
||||
'big_bets_list': big_bets[:3] # Top 3 big bets
|
||||
}
|
||||
|
||||
def generate_roadmap(self, features: List[Dict], team_capacity: int = 10) -> List[Dict]:
|
||||
"""
|
||||
Generate a quarterly roadmap based on team capacity
|
||||
|
||||
Args:
|
||||
features: Prioritized feature list
|
||||
team_capacity: Person-months available per quarter
|
||||
"""
|
||||
quarters = []
|
||||
current_quarter = {
|
||||
'quarter': 1,
|
||||
'features': [],
|
||||
'capacity_used': 0,
|
||||
'capacity_available': team_capacity
|
||||
}
|
||||
|
||||
for feature in features:
|
||||
effort = self.effort_map.get(feature.get('effort', 'm').lower(), 5)
|
||||
|
||||
if current_quarter['capacity_used'] + effort <= team_capacity:
|
||||
current_quarter['features'].append(feature)
|
||||
current_quarter['capacity_used'] += effort
|
||||
else:
|
||||
# Move to next quarter
|
||||
current_quarter['capacity_available'] = team_capacity - current_quarter['capacity_used']
|
||||
quarters.append(current_quarter)
|
||||
|
||||
current_quarter = {
|
||||
'quarter': len(quarters) + 1,
|
||||
'features': [feature],
|
||||
'capacity_used': effort,
|
||||
'capacity_available': team_capacity - effort
|
||||
}
|
||||
|
||||
if current_quarter['features']:
|
||||
current_quarter['capacity_available'] = team_capacity - current_quarter['capacity_used']
|
||||
quarters.append(current_quarter)
|
||||
|
||||
return quarters
|
||||
|
||||
def format_output(features: List[Dict], analysis: Dict, roadmap: List[Dict]) -> str:
|
||||
"""Format the results for display"""
|
||||
output = ["=" * 60]
|
||||
output.append("RICE PRIORITIZATION RESULTS")
|
||||
output.append("=" * 60)
|
||||
|
||||
# Top prioritized features
|
||||
output.append("\n📊 TOP PRIORITIZED FEATURES\n")
|
||||
for i, feature in enumerate(features[:10], 1):
|
||||
output.append(f"{i}. {feature.get('name', 'Unnamed')}")
|
||||
output.append(f" RICE Score: {feature['rice_score']}")
|
||||
output.append(f" Reach: {feature.get('reach', 0)} | Impact: {feature.get('impact', 'medium')} | "
|
||||
f"Confidence: {feature.get('confidence', 'medium')} | Effort: {feature.get('effort', 'm')}")
|
||||
output.append("")
|
||||
|
||||
# Portfolio analysis
|
||||
output.append("\n📈 PORTFOLIO ANALYSIS\n")
|
||||
output.append(f"Total Features: {analysis.get('total_features', 0)}")
|
||||
output.append(f"Total Effort: {analysis.get('total_effort_months', 0)} person-months")
|
||||
output.append(f"Total Reach: {analysis.get('total_reach', 0):,} users")
|
||||
output.append(f"Average RICE Score: {analysis.get('average_rice', 0)}")
|
||||
|
||||
output.append(f"\n🎯 Quick Wins: {analysis.get('quick_wins', 0)} features")
|
||||
for qw in analysis.get('quick_wins_list', []):
|
||||
output.append(f" • {qw.get('name', 'Unnamed')} (RICE: {qw['rice_score']})")
|
||||
|
||||
output.append(f"\n🚀 Big Bets: {analysis.get('big_bets', 0)} features")
|
||||
for bb in analysis.get('big_bets_list', []):
|
||||
output.append(f" • {bb.get('name', 'Unnamed')} (RICE: {bb['rice_score']})")
|
||||
|
||||
# Roadmap
|
||||
output.append("\n\n📅 SUGGESTED ROADMAP\n")
|
||||
for quarter in roadmap:
|
||||
output.append(f"\nQ{quarter['quarter']} - Capacity: {quarter['capacity_used']}/{quarter['capacity_used'] + quarter['capacity_available']} person-months")
|
||||
for feature in quarter['features']:
|
||||
output.append(f" • {feature.get('name', 'Unnamed')} (RICE: {feature['rice_score']})")
|
||||
|
||||
return "\n".join(output)
|
||||
|
||||
def load_features_from_csv(filepath: str) -> List[Dict]:
|
||||
"""Load features from CSV file"""
|
||||
features = []
|
||||
with open(filepath, 'r') as f:
|
||||
reader = csv.DictReader(f)
|
||||
for row in reader:
|
||||
feature = {
|
||||
'name': row.get('name', ''),
|
||||
'reach': int(row.get('reach', 0)),
|
||||
'impact': row.get('impact', 'medium'),
|
||||
'confidence': row.get('confidence', 'medium'),
|
||||
'effort': row.get('effort', 'm'),
|
||||
'description': row.get('description', '')
|
||||
}
|
||||
features.append(feature)
|
||||
return features
|
||||
|
||||
def create_sample_csv(filepath: str):
|
||||
"""Create a sample CSV file for testing"""
|
||||
sample_features = [
|
||||
['name', 'reach', 'impact', 'confidence', 'effort', 'description'],
|
||||
['User Dashboard Redesign', '5000', 'high', 'high', 'l', 'Complete redesign of user dashboard'],
|
||||
['Mobile Push Notifications', '10000', 'massive', 'medium', 'm', 'Add push notification support'],
|
||||
['Dark Mode', '8000', 'medium', 'high', 's', 'Implement dark mode theme'],
|
||||
['API Rate Limiting', '2000', 'low', 'high', 'xs', 'Add rate limiting to API'],
|
||||
['Social Login', '12000', 'high', 'medium', 'm', 'Add Google/Facebook login'],
|
||||
['Export to PDF', '3000', 'medium', 'low', 's', 'Export reports as PDF'],
|
||||
['Team Collaboration', '4000', 'massive', 'low', 'xl', 'Real-time collaboration features'],
|
||||
['Search Improvements', '15000', 'high', 'high', 'm', 'Enhance search functionality'],
|
||||
['Onboarding Flow', '20000', 'massive', 'high', 's', 'Improve new user onboarding'],
|
||||
['Analytics Dashboard', '6000', 'high', 'medium', 'l', 'Advanced analytics for users'],
|
||||
]
|
||||
|
||||
with open(filepath, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerows(sample_features)
|
||||
|
||||
print(f"Sample CSV created at: {filepath}")
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description='RICE Framework for Feature Prioritization')
|
||||
parser.add_argument('input', nargs='?', help='CSV file with features or "sample" to create sample')
|
||||
parser.add_argument('--capacity', type=int, default=10, help='Team capacity per quarter (person-months)')
|
||||
parser.add_argument('--output', choices=['text', 'json', 'csv'], default='text', help='Output format')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Create sample if requested
|
||||
if args.input == 'sample':
|
||||
create_sample_csv('sample_features.csv')
|
||||
return
|
||||
|
||||
# Use sample data if no input provided
|
||||
if not args.input:
|
||||
features = [
|
||||
{'name': 'User Dashboard', 'reach': 5000, 'impact': 'high', 'confidence': 'high', 'effort': 'l'},
|
||||
{'name': 'Push Notifications', 'reach': 10000, 'impact': 'massive', 'confidence': 'medium', 'effort': 'm'},
|
||||
{'name': 'Dark Mode', 'reach': 8000, 'impact': 'medium', 'confidence': 'high', 'effort': 's'},
|
||||
{'name': 'API Rate Limiting', 'reach': 2000, 'impact': 'low', 'confidence': 'high', 'effort': 'xs'},
|
||||
{'name': 'Social Login', 'reach': 12000, 'impact': 'high', 'confidence': 'medium', 'effort': 'm'},
|
||||
]
|
||||
else:
|
||||
features = load_features_from_csv(args.input)
|
||||
|
||||
# Calculate RICE scores
|
||||
calculator = RICECalculator()
|
||||
prioritized = calculator.prioritize_features(features)
|
||||
analysis = calculator.analyze_portfolio(prioritized)
|
||||
roadmap = calculator.generate_roadmap(prioritized, args.capacity)
|
||||
|
||||
# Output results
|
||||
if args.output == 'json':
|
||||
result = {
|
||||
'features': prioritized,
|
||||
'analysis': analysis,
|
||||
'roadmap': roadmap
|
||||
}
|
||||
print(json.dumps(result, indent=2))
|
||||
elif args.output == 'csv':
|
||||
# Output prioritized features as CSV
|
||||
if prioritized:
|
||||
keys = prioritized[0].keys()
|
||||
print(','.join(keys))
|
||||
for feature in prioritized:
|
||||
print(','.join(str(feature.get(k, '')) for k in keys))
|
||||
else:
|
||||
print(format_output(prioritized, analysis, roadmap))
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user